Photoelectric Measurements and Modeling of Bacteriorhodopsin

Recent technology has provided us with new information about the internal structures and properties of biomolecules. This has lead to the design of applications based on underlying biological processes. Applications proposed for biomolecules are, for example, the future computers and different types of sensors. One potential biomolecule to be incorporated in the applications is bacteriorhodopsin. Bacteriorhodopsin is a light-sensitive biomolecule, which works in a similar way as the light sensitive cells of the human eye. Bacteriorhodopsin reacts to light by undergoing a complicated series of chemical and thermal transitions. During these transitions, a proton translocation occurs inside the molecule. It is possible to measure the photovoltage caused by the proton translocations when a vast number of molecules is immobilized in a thin film. Also the changes in the light absorption of the film can be measured. This work aimed to develop the electronics needed for the voltage measurements of the bacteriorhodopsin-based optoelectronic sensors. The development of the electronics aimed to get more accurate information about the structure and functionality of these sensors. The sensors used in this work contain a thick film of bacteriorhodopsin immobilized in polyvinylalcohol. This film is placed between two transparent electrodes. The result of this work is an instrumentation amplifier which can be placed in a small space very close to the sensor. By using this amplifier, the original photovoltage can be measured in more detail. The response measured using this amplifier revealed two different components, which could not be distinguished earlier. Another result of this work is the model for the photoelectric response in dry polymer films.

Application of simulated annealing in simulation and optimization of drying process of Zea mays malt

Kinetic simulation and drying process optimization of corn malt by Simulated Annealing (SA) for estimation of temperature and time parameters in order to preserve maximum amylase activity in the obtained product are presented here. Germinated corn seeds were dried at 54-76 °C in a convective dryer, with occasional measurement of moisture content and enzymatic activity. The experimental data obtained were submitted to modeling. Simulation and optimization of the drying process were made by using the SA method, a randomized improvement algorithm, analogous to the simulated annealing process. Results showed that seeds were best dried between 3h and 5h. Among the models used in this work, the kinetic model of water diffusion into corn seeds showed the best fitting. Drying temperature and time showed a square influence on the enzymatic activity. Optimization through SA showed the best condition at 54 ºC and between 5.6h and 6.4h of drying. Values of specific activity in the corn malt were found between 5.26±0.06 SKB/mg and 15.69±0,10% of remaining moisture.

Simulation and optimization tools in paper machine concept design

The last decade has shown that the global paper industry needs new processes and products in order to reassert its position in the industry. As the paper markets in Western Europe and North America have stabilized, the competition has tightened. Along with the development of more cost-effective processes and products, new process design methods are also required to break the old molds and create new ideas. This thesis discusses the development of a process design methodology based on simulation and optimization methods. A bi-level optimization problem and a solution procedure for it are formulated and illustrated. Computational models and simulation are used to illustrate the phenomena inside a real process and mathematical optimization is exploited to find out the best process structures and control principles for the process. Dynamic process models are used inside the bi-level optimization problem, which is assumed to be dynamic and multiobjective due to the nature of papermaking processes. The numerical experiments show that the bi-level optimization approach is useful for different kinds of problems related to process design and optimization. Here, the design methodology is applied to a constrained process area of a papermaking line. However, the same methodology is applicable to all types of industrial processes, e.g., the design of biorefiners, because the methodology is totally generalized and can be easily modified.

Simulation and material calibration of ultra high strength steel (UHSS) S960 welded joint under static tensile test utilizing finite element method (FEM)

The aim of this work was to calibrate the material properties including strength and strain values for different material zones of ultra-high strength steel (UHSS) welded joints under monotonic static loading. The UHSS is heat sensitive and softens by heat due to welding, the affected zone is heat affected zone (HAZ). In this regard, cylindrical specimens were cut out from welded joints of Strenx® 960 MC and Strenx® Tube 960 MH, were examined by tensile test. The hardness values of specimens’ cross section were measured. Using correlations between hardness and strength, initial material properties were obtained. The same size specimen with different zones of material same as real specimen were created and defined in finite element method (FEM) software with commercial brand Abaqus 6.14-1. The loading and boundary conditions were defined considering tensile test values. Using initial material properties made of hardness-strength correlations (true stress-strain values) as Abaqus main input, FEM is utilized to simulate the tensile test process. By comparing FEM Abaqus results with measured results of tensile test, initial material properties will be revised and reused as software input to be fully calibrated in such a way that FEM results and tensile test results deviate minimum. Two type of different S960 were used including 960 MC plates, and structural hollow section 960 MH X-joint. The joint is welded by BöhlerTM X96 filler material. In welded joints, typically the following zones appear: Weld (WEL), Heat affected zone (HAZ) coarse grained (HCG) and fine grained (HFG), annealed zone, and base material (BaM). Results showed that: The HAZ zone is softened due to heat input while welding. For all the specimens, the softened zone’s strength is decreased and makes it a weakest zone where fracture happens while loading. Stress concentration of a notched specimen can represent the properties of notched zone. The load-displacement diagram from FEM modeling matches with the experiments by the calibrated material properties by compromising two correlations of hardness and strength.

Interannual and interdecadal oscillations in hydrological variables: Sources and modeling of the persistence in the Elbe River Basin

Wenn man die Existenz von physikalischen Mechanismen ignoriert, die für die Struktur hydrologischer Zeitreihen verantwortlich sind, kann das zu falschen Schlussfolgerungen bzgl. des Vorhandenseins möglicher Gedächtnis (memory) -Effekte, d.h. von Persistenz, führen. Die hier vorgelegte Doktorarbeit spürt der niedrigfrequenten klimatischen Variabilität innerhalb den hydrologischen Zyklus nach und bietet auf dieser "Reise" neue Einsichten in die Transformation der charakteristischen Eigenschaften von Zeitreihen mit einem Langzeitgedächtnis. Diese Studie vereint statistische Methoden der Zeitreihenanalyse mit empirisch-basierten Modelltechniken, um operative Modelle zu entwickeln, die in der Lage sind (1) die Dynamik des Abflusses zu modellieren, (2) sein zukünftiges Verhalten zu prognostizieren und (3) die Abflusszeitreihen an unbeobachteten Stellen abzuschätzen. Als solches präsentiert die hier vorgelegte Dissertation eine ausführliche Untersuchung zu den Ursachen der niedrigfrequenten Variabilität von hydrologischen Zeitreihen im deutschen Teil des Elbe-Einzugsgebietes, den Folgen dieser Variabilität und den physikalisch basierten Reaktionen von Oberflächen- und Grundwassermodellen auf die niedrigfrequenten Niederschlags-Eingangsganglinien. Die Doktorarbeit gliedert sich wie folgt: In Kapitel 1 wird als Hintergrundinformation das Hurst Phänomen beschrieben und ein kurzer Rückblick auf diesbezügliche Studien gegeben. Das Kapitel 2 diskutiert den Einfluss der Präsenz von niedrigfrequenten periodischen Zeitreihen auf die Zuverlässigkeit verschiedener Hurst-Parameter-Schätztechniken. Kapitel 3 korreliert die niedrigfrequente Niederschlagsvariabilität mit dem Index der Nord-Atlantischen Ozillations (NAO). Kapitel 4-6 sind auf den deutschen Teil des Elbe-Einzugsgebietes fokussiert. So werden in Kapitel 4 die niedrigfrequenten Variabilitäten der unterschiedlichen hydro-meteorologischen Parameter untersucht und es werden Modelle beschrieben, die die Dynamik dieser Niedrigfrequenzen und deren zukünftiges Verhalten simulieren. Kapitel 5 diskutiert die mögliche Anwendung der Ergebnisse für die charakteristische Skalen und die Verfahren der Analyse der zeitlichen Variabilität auf praktische Fragestellungen im Wasserbau sowie auf die zeitliche Bestimmung des Gebiets-Abflusses an unbeobachteten Stellen. Kapitel 6 verfolgt die Spur der Niedrigfrequenzzyklen im Niederschlag durch die einzelnen Komponenten des hydrologischen Zyklus, nämlich dem Direktabfluss, dem Basisabfluss, der Grundwasserströmung und dem Gebiets-Abfluss durch empirische Modellierung. Die Schlussfolgerungen werden im Kapitel 7 präsentiert. In einem Anhang werden technische Einzelheiten zu den verwendeten statistischen Methoden und die entwickelten Software-Tools beschrieben.

Large-Size AlGaN/GaN HEMT Large-Signal Electrothermal Characterization and Modeling for Wireless Digital Communications

The rapid growth in high data rate communication systems has introduced new high spectral efficient modulation techniques and standards such as LTE-A (long term evolution-advanced) for 4G (4th generation) systems. These techniques have provided a broader bandwidth but introduced high peak-to-average power ratio (PAR) problem at the high power amplifier (HPA) level of the communication system base transceiver station (BTS). To avoid spectral spreading due to high PAR, stringent requirement on linearity is needed which brings the HPA to operate at large back-off power at the expense of power efficiency. Consequently, high power devices are fundamental in HPAs for high linearity and efficiency. Recent development in wide bandgap power devices, in particular AlGaN/GaN HEMT, has offered higher power level with superior linearity-efficiency trade-off in microwaves communication. For cost-effective HPA design to production cycle, rigorous computer aided design (CAD) AlGaN/GaN HEMT models are essential to reflect real response with increasing power level and channel temperature. Therefore, large-size AlGaN/GaN HEMT large-signal electrothermal modeling procedure is proposed. The HEMT structure analysis, characterization, data processing, model extraction and model implementation phases have been covered in this thesis including trapping and self-heating dispersion accounting for nonlinear drain current collapse. The small-signal model is extracted using the 22-element modeling procedure developed in our department. The intrinsic large-signal model is deeply investigated in conjunction with linearity prediction. The accuracy of the nonlinear drain current has been enhanced through several issues such as trapping and self-heating characterization. Also, the HEMT structure thermal profile has been investigated and corresponding thermal resistance has been extracted through thermal simulation and chuck-controlled temperature pulsed I(V) and static DC measurements. Higher-order equivalent thermal model is extracted and implemented in the HEMT large-signal model to accurately estimate instantaneous channel temperature. Moreover, trapping and self-heating transients has been characterized through transient measurements. The obtained time constants are represented by equivalent sub-circuits and integrated in the nonlinear drain current implementation to account for complex communication signals dynamic prediction. The obtained verification of this table-based large-size large-signal electrothermal model implementation has illustrated high accuracy in terms of output power, gain, efficiency and nonlinearity prediction with respect to standard large-signal test signals.

Measuring motion with kinematically redundant accelerometer arrays: theory, simulation and implementation

This work presents two schemes of measuring the linear and angular kinematics of a rigid body using a kinematically redundant array of triple-axis accelerometers with potential applications in biomechanics. A novel angular velocity estimation algorithm is proposed and evaluated that can compensate for angular velocity errors using measurements of the direction of gravity. Analysis and discussion of optimal sensor array characteristics are provided. A damped 2 axis pendulum was used to excite all 6 DoF of the a suspended accelerometer array through determined complex motion and is the basis of both simulation and experimental studies. The relationship between accuracy and sensor redundancy is investigated for arrays of up to 100 triple axis (300 accelerometer axes) accelerometers in simulation and 10 equivalent sensors (30 accelerometer axes) in the laboratory test rig. The paper also reports on the sensor calibration techniques and hardware implementation.

Satellite observations and modeling of transport in the upper troposphere through the lower mesosphere during the 2006 major stratospheric sudden warming

An unusually strong and prolonged stratospheric sudden warming (SSW) in January 2006 was the first major SSW for which globally distributed long-lived trace gas data are available covering the upper troposphere through the lower mesosphere. We use Aura Microwave Limb Sounder (MLS), Atmospheric Chemistry Experiment-Fourier Transform Spectrometer (ACE-FTS) data, the SLIMCAT Chemistry Transport Model (CTM), and assimilated meteorological analyses to provide a comprehensive picture of transport during this event. The upper tropospheric ridge that triggered the SSW was associated with an elevated tropopause and layering in trace gas profiles in conjunction with stratospheric and tropospheric intrusions. Anomalous poleward transport (with corresponding quasi-isentropic troposphere-to-stratosphere exchange at the lowest levels studied) in the region over the ridge extended well into the lower stratosphere. In the middle and upper stratosphere, the breakdown of the polar vortex transport barrier was seen in a signature of rapid, widespread mixing in trace gases, including CO, H2O, CH4 and N2O. The vortex broke down slightly later and more slowly in the lower than in the middle stratosphere. In the middle and lower stratosphere, small remnants with trace gas values characteristic of the pre-SSW vortex lingered through the weak and slow recovery of the vortex. The upper stratospheric vortex quickly reformed, and, as enhanced diabatic descent set in, CO descended into this strong vortex, echoing the fall vortex development. Trace gas evolution in the SLIMCAT CTM agrees well with that in the satellite trace gas data from the upper troposphere through the middle stratosphere. In the upper stratosphere and lower mesosphere, the SLIMCAT simulation does not capture the strong descent of mesospheric CO and H2O values into the reformed vortex; this poor CTM performance in the upper stratosphere and lower mesosphere results primarily from biases in the diabatic descent in assimilated analyses.

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models.

Simulation and optimisation techniques for sawmill yard operation : A literature review

Increasing costs and competitive business strategies are pushing sawmill enterprises to make an effort for optimization of their process management. Organizational decisions mainly concentrate on performance and reduction of operational costs in order to maintain profit margins. Although many efforts have been made, effective utilization of resources, optimal planning and maximum productivity in sawmill are still challenging to sawmill industries. Many researchers proposed the simulation models in combination with optimization techniques to address problems of integrated logistics optimization. The combination of simulation and optimization technique identifies the optimal strategy by simulating all complex behaviours of the system under consideration including objectives and constraints. During the past decade, an enormous number of studies were conducted to simulate operational inefficiencies in order to find optimal solutions. This paper gives a review on recent developments and challenges associated with simulation and optimization techniques. It was believed that the review would provide a perfect ground to the authors in pursuing further work in optimizing sawmill yard operations.

Simulation and economic analysis of bovine sex selection

A simulation model implemented in the programming software Delphi XE® was applied to evaluate sex selection in bovine. The hypothesis under investigation was that a dynamic model with stochastic and deterministic elements could detect the sexed semen technique to minimize pregnancy cost and to determine the adequate number of recipients required for in vivo (ET) and in vitro embryo production (IVP) in the proposed scenarios. Sex selection was compared through semen sexed using flow cytometry (C1) and density gradient centrifugation techniques (C2) in ET and IVP. Sensibility analyses were used to identify the adequate number of recipients for each scenario. This number was reinserted into the model to determine the biological and financial values that maximized ET and IVP using sexed semen (C1M and C2M). New scenarios showed that the density gradient technique minimized pregnancy cost based on the proposed scenarios. In addition, the adequate number of recipients (ET - C1M - 115 and C2M - 105)/(IVP - C1M - 145 and C2M - 140) per donor used was determined to minimize the pregnancy cost in all scenarios.

Non-Markovian stochastic processes and their applications: from anomalous diffusion to time series analysis

This work provides a forward step in the study and comprehension of the relationships between stochastic processes and a certain class of integral-partial differential equation, which can be used in order to model anomalous diffusion and transport in statistical physics. In the first part, we brought the reader through the fundamental notions of probability and stochastic processes, stochastic integration and stochastic differential equations as well. In particular, within the study of H-sssi processes, we focused on fractional Brownian motion (fBm) and its discrete-time increment process, the fractional Gaussian noise (fGn), which provide examples of non-Markovian Gaussian processes. The fGn, together with stationary FARIMA processes, is widely used in the modeling and estimation of long-memory, or long-range dependence (LRD). Time series manifesting long-range dependence, are often observed in nature especially in physics, meteorology, climatology, but also in hydrology, geophysics, economy and many others. We deepely studied LRD, giving many real data examples, providing statistical analysis and introducing parametric methods of estimation. Then, we introduced the theory of fractional integrals and derivatives, which indeed turns out to be very appropriate for studying and modeling systems with long-memory properties. After having introduced the basics concepts, we provided many examples and applications. For instance, we investigated the relaxation equation with distributed order time-fractional derivatives, which describes models characterized by a strong memory component and can be used to model relaxation in complex systems, which deviates from the classical exponential Debye pattern. Then, we focused in the study of generalizations of the standard diffusion equation, by passing through the preliminary study of the fractional forward drift equation. Such generalizations have been obtained by using fractional integrals and derivatives of distributed orders. In order to find a connection between the anomalous diffusion described by these equations and the long-range dependence, we introduced and studied the generalized grey Brownian motion (ggBm), which is actually a parametric class of H-sssi processes, which have indeed marginal probability density function evolving in time according to a partial integro-differential equation of fractional type. The ggBm is of course Non-Markovian. All around the work, we have remarked many times that, starting from a master equation of a probability density function f(x,t), it is always possible to define an equivalence class of stochastic processes with the same marginal density function f(x,t). All these processes provide suitable stochastic models for the starting equation. Studying the ggBm, we just focused on a subclass made up of processes with stationary increments. The ggBm has been defined canonically in the so called grey noise space. However, we have been able to provide a characterization notwithstanding the underline probability space. We also pointed out that that the generalized grey Brownian motion is a direct generalization of a Gaussian process and in particular it generalizes Brownain motion and fractional Brownain motion as well. Finally, we introduced and analyzed a more general class of diffusion type equations related to certain non-Markovian stochastic processes. We started from the forward drift equation, which have been made non-local in time by the introduction of a suitable chosen memory kernel K(t). The resulting non-Markovian equation has been interpreted in a natural way as the evolution equation of the marginal density function of a random time process l(t). We then consider the subordinated process Y(t)=X(l(t)) where X(t) is a Markovian diffusion. The corresponding time-evolution of the marginal density function of Y(t) is governed by a non-Markovian Fokker-Planck equation which involves the same memory kernel K(t). We developed several applications and derived the exact solutions. Moreover, we considered different stochastic models for the given equations, providing path simulations.

Characterization and modeling of the barrier properties in nanostructured systems

The object of the present study is the process of gas transport in nano-sized materials, i.e. systems having structural elements of the order of nanometers. The aim of this work is to advance the understanding of the gas transport mechanism in such materials, for which traditional models are not often suitable, by providing a correct interpretation of the relationship between diffusive phenomena and structural features. This result would allow the development new materials with permeation properties tailored on the specific application, especially in packaging systems. The methods used to achieve this goal were a detailed experimental characterization and different simulation methods. The experimental campaign regarded the determination of oxygen permeability and diffusivity in different sets of organic-inorganic hybrid coatings prepared via sol-gel technique. The polymeric samples coated with these hybrid layers experienced a remarkable enhancement of the barrier properties, which was explained by the strong interconnection at the nano-scale between the organic moiety and silica domains. An analogous characterization was performed on microfibrillated cellulose films, which presented remarkable barrier effect toward oxygen when it is dry, while in the presence of water the performance significantly drops. The very low value of water diffusivity at low activities is also an interesting characteristic which deals with its structural properties. Two different approaches of simulation were then considered: the diffusion of oxygen through polymer-layered silicates was modeled on a continuum scale with a CFD software, while the properties of n-alkanthiolate self assembled monolayers on gold were analyzed from a molecular point of view by means of a molecular dynamics algorithm. Modeling transport properties in layered nanocomposites, resulting from the ordered dispersion of impermeable flakes in a 2-D matrix, allowed the calculation of the enhancement of barrier effect in relation with platelets structural parameters leading to derive a new expression. On this basis, randomly distributed systems were simulated and the results were analyzed to evaluate the different contributions to the overall effect. The study of more realistic three-dimensional geometries revealed a prefect correspondence with the 2-D approximation. A completely different approach was applied to simulate the effect of temperature on the oxygen transport through self assembled monolayers; the structural information obtained from equilibrium MD simulations showed that raising the temperature, makes the monolayer less ordered and consequently less crystalline. This disorder produces a decrease in the barrier free energy and it lowers the overall resistance to oxygen diffusion, making the monolayer more permeable to small molecules.

Supramolecular approaches to organized luminescent nanostructures for sensing, labeling and imaging applications

The common thread of this thesis is the will of investigating properties and behavior of assemblies. Groups of objects display peculiar properties, which can be very far from the simple sum of respective components’ properties. This is truer, the smaller is inter-objects distance, i.e. the higher is their density, and the smaller is the container size. “Confinement” is in fact a key concept in many topics explored and here reported. It can be conceived as a spatial limitation, that yet gives origin to unexpected processes and phenomena based on inter-objects communication. Such phenomena eventually result in “non-linear properties”, responsible for the low predictability of large assemblies. Chapter 1 provides two insights on surface chemistry, namely (i) on a supramolecular assembly based on orthogonal forces, and (ii) on selective and sensitive fluorescent sensing in thin polymeric film. In chapters 2 to 4 confinement of molecules plays a major role. Most of the work focuses on FRET within core-shell nanoparticles, investigated both through a simulation model and through experiments. Exciting results of great applicative interest are drawn, such as a method of tuning emission wavelength at constant excitation, and a way of overcoming self-quenching processes by setting up a competitive deactivation channel. We envisage applications of these materials as labels for multiplexing analysis, and in all fields of fluorescence imaging, where brightness coupled with biocompatibility and water solubility is required. Adducts of nanoparticles and molecular photoswitches are investigated in the context of superresolution techniques for fluorescence microscopy. In chapter 5 a method is proposed to prepare a library of functionalized Pluronic F127, which gives access to a twofold “smart” nanomaterial, namely both (i)luminescent and (ii)surface-functionalized SCSSNPs. Focus shifts in chapter 6 to confinement effects in an upper size scale. Moving from nanometers to micrometers, we investigate the interplay between microparticles flowing in microchannels where a constriction affects at very long ranges structure and dynamics of the colloidal paste.