Effects of plant diversity on bioenergy parameters in grassland biomass

Extensive grassland biomass for bioenergy production has long been subject of scientific research. The possibility of combining nature conservation goals with a profitable management while reducing competition with food production has created a strong interest in this topic. However, the botanical composition will play a key role for solid fuel quality of grassland biomass and will have effects on the combustion process by potentially causing corrosion, emission and slagging. On the other hand, botanical composition will affect anaerobic digestibility and thereby the biogas potential. In this thesis aboveground biomass from the Jena-Experiment plots was harvested in 2008 and 2009 and analysed for the most relevant chemical constituents effecting fuel quality and anaerobic digestibility. Regarding combustion, the following parameters were of main focus: higher heating value (HHV), gross energy yield (GE), ash content, ash softening temperature (AST), K, Ca, Mg, N, Cl and S content. For biogas production the following parameters were investigated: substrate specific methane yield (CH4 sub), area specific methane yield (CH4 area), crude fibre (CF), crude protein (CP), crude lipid (CL) and nitrogen-free extract (NfE). Furthermore, an improvement of the fuel quality was investigated through applying the Integrated generation of solid Fuel and Biogas from Biomass (IFBB) procedure. Through the specific setup of the Jena-Experiment it was possible to outline the changes of these parameters along two diversity gradients: (i) species richness (SR; 1 to 60 species) and (ii) functional group (grasses, legumes, small herbs and tall herbs) presence. This was a novel approach on investigating the bioenergy characteristic of extensive grassland biomass and gave detailed insight in the sward-composition¬ - bioenergy relations such as: (i) the most relevant SR effect was the increase of energy yield for both combustion (annual GE increased by 26% from SR8→16 and by 65% from SR8→60) and anaerobic digestion (annual CH4 area increased by 22% from SR8→16 and by 49% from SR8→60) through a strong interaction of SR with biomass yield; (ii) legumes play a key role for the utilization of grassland biomass for energy production as they increase the energy content of the substrate (HHV and CH4 sub) and the energy yield (GE and CH4 area); (iii) combustion is the conversion technique that will yield the highest energy output but requires an improvement of the solid fuel quality in order to reduce the risk of corrosion, emission and slagging related problems. This was achieved through applying the IFBB-procedure, with reductions in ash (by 23%), N (28%), K (85%), Cl (56%) and S (59%) and equal levels of concentrations along the SR gradient.

Organische Mikro- und Nanodrähte auf Basis von Perylendiimiden

Diese Arbeit thematisiert die optimierte Darstellung von organischen Mikro- und Nanodrähten, Untersuchungen bezüglich deren molekularen Aufbaus und die anwendungsorientierte Charakterisierung der Eigenschaften. Mikro- und Nanodrähte haben in den letzten Jahren im Zuge der Miniaturisierung von Technologien an weitreichendem Interesse gewonnen. Solche eindimensionalen Strukturen, deren Durchmesser im Bereich weniger zehn Nanometer bis zu einigen wenigen Mikrometern liegt, sind Gegenstand intensiver Forschung. Neben anorganischen Ausgangssubstanzen zur Erzeugung von Mikro- und Nanodrähten haben organische Funktionsmaterialien aufgrund ihrer einfachen und kostengünstigen Verarbeitbarkeit sowie ihrer interessanten elektrischen und optischen Eigenschaften an Bedeutung gewonnen. Eine wichtige Materialklasse ist in diesem Zusammenhang die Verbindungsklasse der n-halbleitenden Perylentetracarbonsäurediimide (kurz Perylendiimide). Dem erfolgreichen Einsatz von eindimensionalen Strukturen als miniaturisierte Bausteine geht die optimierte und kontrollierte Herstellung voraus. Im Rahmen der Doktorarbeit wurde die neue Methode der Drahterzeugung „Trocknen unter Lösungsmittelatmosphäre“ entwickelt, welche auf Selbstassemblierung der Substanzmoleküle aus Lösung basiert und unter dem Einfluss von Lösungsmitteldampf direkt auf einem vorgegebenen Substrat stattfindet. Im Gegensatz zu literaturbekannten Methoden ist kein Transfer der Drähte aus einem Reaktionsgefäß nötig und damit verbundene Beschädigungen der Strukturen werden vermieden. Während herkömmliche Methoden in einer unkontrolliert großen Menge von ineinander verwundenen Drähten resultieren, erlaubt die substratbasierte Technik die Bildung voneinander separierter Einzelfasern und somit beispielsweise den Einsatz in Einzelstrukturbauteilen. Die erhaltenen Fasern sind morphologisch sehr gleichmäßig und weisen bei Längen von bis zu 5 mm bemerkenswert hohe Aspektverhältnisse von über 10000 auf. Darüber hinaus kann durch das direkte Drahtwachstum auf dem Substrat über den Einsatz von vorstrukturierten Oberflächen und Wachstumsmasken gerichtetes, lokal beschränktes Drahtwachstum erzielt werden und damit aktive Kontrolle auf Richtung und Wachstumsbereich der makroskopisch nicht handhabbaren Objekte ausgeübt werden. Um das Drahtwachstum auch hinsichtlich der Materialauswahl, d. h. der eingesetzten Ausgangsmaterialien zur Drahterzeugung und somit der resultierenden Eigenschaften der gebildeten Strukturen aktiv kontrollieren zu können, wird der Einfluss unterschiedlicher Parameter auf die Morphologie der Selbstassemblierungsprodukte am Beispiel unterschiedlicher Derivate betrachtet. So stellt sich zum einen die Art der eingesetzten Lösungsmittel in flüssiger und gasförmiger Phase beim Trocknen unter Lösungsmittelatmosphäre als wichtiger Faktor heraus. Beide Lösungsmittel dienen als Interaktionspartner für die Moleküle des funktionellen Drahtmaterials im Selbstassemblierungsprozess. Spezifische Wechselwirkungen zwischen Perylendiimid-Molekülen untereinander und mit Lösungsmittel-Molekülen bestimmen dabei die äußere Form der erhaltenen Strukturen. Ein weiterer wichtiger Faktor ist die Molekülstruktur des verwendeten funktionellen Perylendiimids. Es wird der Einfluss einer Bay-Substitution bzw. einer unsymmetrischen Imid-Substitution auf die Morphologie der erhaltenen Strukturen herausgestellt. Für das detaillierte Verständnis des Zusammenhanges zwischen Molekülstruktur und nötigen Wachstumsbedingungen für die Bildung von eindimensionalen Strukturen zum einen, aber auch die resultierenden Eigenschaften der erhaltenen Aggregationsprodukte zum anderen, sind Informationen über den molekularen Aufbau von großer Bedeutung. Im Rahmen der Doktorarbeit konnte ein molekular hoch geordneter, kristalliner Aufbau der Drähte nachgewiesen werden. Durch Kombination unterschiedlicher Messmethoden ist es gelungen, die molekulare Anordnung in Strukturen aus einem Spirobifluoren-substituierten Derivat in Form einer verkippten Molekülstapelung entlang der Drahtlängsrichtung zu bestimmen. Um mögliche Anwendungsbereiche der erzeugten Drähte aufzuzeigen, wurden diese hinsichtlich ihrer elektrischen und optischen Eigenschaften analysiert. Neben dem potentiellen Einsatz im Bereich von Filteranwendungen und Sensoren, sind vor allem die halbleitenden und optisch wellenleitenden Eigenschaften hervorzuheben. Es konnten organische Transistoren auf der Basis von Einzeldrähten mit im Vergleich zu Dünnschichtbauteilen erhöhten Ladungsträgerbeweglichkeiten präpariert werden. Darüber hinaus wurden die erzeugten eindimensionalen Strukturen als aktive optische Wellenleiter charakterisiert. Die im Rahmen der Dissertation erarbeiteten Kenntnisse bezüglich der Bildung von eindimensionalen Strukturen durch Selbstassemblierung, des Drahtaufbaus und erster anwendungsorientierter Charakterisierung stellen eine Basis zur Weiterentwicklung solcher miniaturisierter Bausteine für unterschiedlichste Anwendungen dar. Die neu entwickelte Methode des Trocknens unter Lösungsmittelatmosphäre ist nicht auf den Einsatz von Perylendiimiden beschränkt, sondern kann auf andere Substanzklassen ausgeweitet werden. Dies eröffnet breite Möglichkeiten der Materialauswahl und somit der Einsatzmöglichkeiten der erhaltenen Strukturen.

Strategische Erfolgsfaktoren für ein kosten-risiko-optimales Public Debt Management - Organisatorische und inhaltliche Parameter im internationalen Vergleich

Aus den im Rahmen dieser Forschungsarbeit empirisch gewonnenen Erkenntnissen werden Gestaltungsempfehlungen für das Public Debt Management abgeleitet. Diese zeigen, dass ein wirtschaftliches Public Debt Management nicht ein ausschließlich kostenminimierendes (sparsames), sondern ein kosten-risiko-optimales Public Debt Management mit effektiven internen und externen Überwachungsinstrumenten und wirksamer externer Finanzkontrolle sein muss.

Optimization-Based Robotic Manipulation for Safe Interaction with Human Operators

This thesis investigates a method for human-robot interaction (HRI) in order to uphold productivity of industrial robots like minimization of the shortest operation time, while ensuring human safety like collision avoidance. For solving such problems an online motion planning approach for robotic manipulators with HRI has been proposed. The approach is based on model predictive control (MPC) with embedded mixed integer programming. The planning strategies of the robotic manipulators mainly considered in the thesis are directly performed in the workspace for easy obstacle representation. The non-convex optimization problem is approximated by a mixed-integer program (MIP). It is further effectively reformulated such that the number of binary variables and the number of feasible integer solutions are drastically decreased. Safety-relevant regions, which are potentially occupied by the human operators, can be generated online by a proposed method based on hidden Markov models. In contrast to previous approaches, which derive predictions based on probability density functions in the form of single points, such as most likely or expected human positions, the proposed method computes safety-relevant subsets of the workspace as a region which is possibly occupied by the human at future instances of time. The method is further enhanced by combining reachability analysis to increase the prediction accuracy. These safety-relevant regions can subsequently serve as safety constraints when the motion is planned by optimization. This way one arrives at motion plans that are safe, i.e. plans that avoid collision with a probability not less than a predefined threshold. The developed methods have been successfully applied to a developed demonstrator, where an industrial robot works in the same space as a human operator. The task of the industrial robot is to drive its end-effector according to a nominal sequence of grippingmotion-releasing operations while no collision with a human arm occurs.

Parameter Estimation in Chaotic Systems

This report examines how to estimate the parameters of a chaotic system given noisy observations of the state behavior of the system. Investigating parameter estimation for chaotic systems is interesting because of possible applications for high-precision measurement and for use in other signal processing, communication, and control applications involving chaotic systems. In this report, we examine theoretical issues regarding parameter estimation in chaotic systems and develop an efficient algorithm to perform parameter estimation. We discover two properties that are helpful for performing parameter estimation on non-structurally stable systems. First, it turns out that most data in a time series of state observations contribute very little information about the underlying parameters of a system, while a few sections of data may be extraordinarily sensitive to parameter changes. Second, for one-parameter families of systems, we demonstrate that there is often a preferred direction in parameter space governing how easily trajectories of one system can "shadow'" trajectories of nearby systems. This asymmetry of shadowing behavior in parameter space is proved for certain families of maps of the interval. Numerical evidence indicates that similar results may be true for a wide variety of other systems. Using the two properties cited above, we devise an algorithm for performing parameter estimation. Standard parameter estimation techniques such as the extended Kalman filter perform poorly on chaotic systems because of divergence problems. The proposed algorithm achieves accuracies several orders of magnitude better than the Kalman filter and has good convergence properties for large data sets.

Modeling, Estimation, and Control of Robot-Soil Interactions

This thesis presents the development of hardware, theory, and experimental methods to enable a robotic manipulator arm to interact with soils and estimate soil properties from interaction forces. Unlike the majority of robotic systems interacting with soil, our objective is parameter estimation, not excavation. To this end, we design our manipulator with a flat plate for easy modeling of interactions. By using a flat plate, we take advantage of the wealth of research on the similar problem of earth pressure on retaining walls. There are a number of existing earth pressure models. These models typically provide estimates of force which are in uncertain relation to the true force. A recent technique, known as numerical limit analysis, provides upper and lower bounds on the true force. Predictions from the numerical limit analysis technique are shown to be in good agreement with other accepted models. Experimental methods for plate insertion, soil-tool interface friction estimation, and control of applied forces on the soil are presented. In addition, a novel graphical technique for inverting the soil models is developed, which is an improvement over standard nonlinear optimization. This graphical technique utilizes the uncertainties associated with each set of force measurements to obtain all possible parameters which could have produced the measured forces. The system is tested on three cohesionless soils, two in a loose state and one in a loose and dense state. The results are compared with friction angles obtained from direct shear tests. The results highlight a number of key points. Common assumptions are made in soil modeling. Most notably, the Mohr-Coulomb failure law and perfectly plastic behavior. In the direct shear tests, a marked dependence of friction angle on the normal stress at low stresses is found. This has ramifications for any study of friction done at low stresses. In addition, gradual failures are often observed for vertical tools and tools inclined away from the direction of motion. After accounting for the change in friction angle at low stresses, the results show good agreement with the direct shear values.

Teaching an Old Robot New Tricks: Learning Novel Tasks via Interaction with People and Things

As AI has begun to reach out beyond its symbolic, objectivist roots into the embodied, experientialist realm, many projects are exploring different aspects of creating machines which interact with and respond to the world as humans do. Techniques for visual processing, object recognition, emotional response, gesture production and recognition, etc., are necessary components of a complete humanoid robot. However, most projects invariably concentrate on developing a few of these individual components, neglecting the issue of how all of these pieces would eventually fit together. The focus of the work in this dissertation is on creating a framework into which such specific competencies can be embedded, in a way that they can interact with each other and build layers of new functionality. To be of any practical value, such a framework must satisfy the real-world constraints of functioning in real-time with noisy sensors and actuators. The humanoid robot Cog provides an unapologetically adequate platform from which to take on such a challenge. This work makes three contributions to embodied AI. First, it offers a general-purpose architecture for developing behavior-based systems distributed over networks of PC's. Second, it provides a motor-control system that simulates several biological features which impact the development of motor behavior. Third, it develops a framework for a system which enables a robot to learn new behaviors via interacting with itself and the outside world. A few basic functional modules are built into this framework, enough to demonstrate the robot learning some very simple behaviors taught by a human trainer. A primary motivation for this project is the notion that it is practically impossible to build an "intelligent" machine unless it is designed partly to build itself. This work is a proof-of-concept of such an approach to integrating multiple perceptual and motor systems into a complete learning agent.

Interaction and Intelligent Behavior

We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.

Certified Rapid Solution of Parametrized Linear Elliptic Equations: Application to Parameter Estimation

We present a technique for the rapid and reliable evaluation of linear-functional output of elliptic partial differential equations with affine parameter dependence. The essential components are (i) rapidly uniformly convergent reduced-basis approximations — Galerkin projection onto a space WN spanned by solutions of the governing partial differential equation at N (optimally) selected points in parameter space; (ii) a posteriori error estimation — relaxations of the residual equation that provide inexpensive yet sharp and rigorous bounds for the error in the outputs; and (iii) offline/online computational procedures — stratagems that exploit affine parameter dependence to de-couple the generation and projection stages of the approximation process. The operation count for the online stage — in which, given a new parameter value, we calculate the output and associated error bound — depends only on N (typically small) and the parametric complexity of the problem. The method is thus ideally suited to the many-query and real-time contexts. In this paper, based on the technique we develop a robust inverse computational method for very fast solution of inverse problems characterized by parametrized partial differential equations. The essential ideas are in three-fold: first, we apply the technique to the forward problem for the rapid certified evaluation of PDE input-output relations and associated rigorous error bounds; second, we incorporate the reduced-basis approximation and error bounds into the inverse problem formulation; and third, rather than regularize the goodness-of-fit objective, we may instead identify all (or almost all, in the probabilistic sense) system configurations consistent with the available experimental data — well-posedness is reflected in a bounded "possibility region" that furthermore shrinks as the experimental error is decreased.

Moderating effects of management control systems and innovation on performance. Simple methods for correcting the effects of measurement error for interaction effects in small samples

In the accounting literature, interaction or moderating effects are usually assessed by means of OLS regression and summated rating scales are constructed to reduce measurement error bias. Structural equation models and two-stage least squares regression could be used to completely eliminate this bias, but large samples are needed. Partial Least Squares are appropriate for small samples but do not correct measurement error bias. In this article, disattenuated regression is discussed as a small sample alternative and is illustrated on data of Bisbe and Otley (in press) that examine the interaction effect of innovation and style of use of budgets on performance. Sizeable differences emerge between OLS and disattenuated regression

Meaning of the λ parameter of skew–normal and log–skew normal distributions in fluid geochemistry

The literature related to skew–normal distributions has grown rapidly in recent years but at the moment few applications concern the description of natural phenomena with this type of probability models, as well as the interpretation of their parameters. The skew–normal distributions family represents an extension of the normal family to which a parameter (λ) has been added to regulate the skewness. The development of this theoretical field has followed the general tendency in Statistics towards more flexible methods to represent features of the data, as adequately as possible, and to reduce unrealistic assumptions as the normality that underlies most methods of univariate and multivariate analysis. In this paper an investigation on the shape of the frequency distribution of the logratio ln(Cl−/Na+) whose components are related to waters composition for 26 wells, has been performed. Samples have been collected around the active center of Vulcano island (Aeolian archipelago, southern Italy) from 1977 up to now at time intervals of about six months. Data of the logratio have been tentatively modeled by evaluating the performance of the skew–normal model for each well. Values of the λ parameter have been compared by considering temperature and spatial position of the sampling points. Preliminary results indicate that changes in λ values can be related to the nature of environmental processes affecting the data

Simultaneous estimation of indirect and interaction effects using structural equation models

Interaction effects are usually modeled by means of moderated regression analysis. Structural equation models with non-linear constraints make it possible to estimate interaction effects while correcting for measurement error. From the various specifications, Jöreskog and Yang's (1996, 1998), likely the most parsimonious, has been chosen and further simplified. Up to now, only direct effects have been specified, thus wasting much of the capability of the structural equation approach. This paper presents and discusses an extension of Jöreskog and Yang's specification that can handle direct, indirect and interaction effects simultaneously. The model is illustrated by a study of the effects of an interactive style of use of budgets on both company innovation and performance

Correcting Measurement Error Bias in Interaction Models with Small Samples

Several methods have been suggested to estimate non-linear models with interaction terms in the presence of measurement error. Structural equation models eliminate measurement error bias, but require large samples. Ordinary least squares regression on summated scales, regression on factor scores and partial least squares are appropriate for small samples but do not correct measurement error bias. Two stage least squares regression does correct measurement error bias but the results strongly depend on the instrumental variable choice. This article discusses the old disattenuated regression method as an alternative for correcting measurement error in small samples. The method is extended to the case of interaction terms and is illustrated on a model that examines the interaction effect of innovation and style of use of budgets on business performance. Alternative reliability estimates that can be used to disattenuate the estimates are discussed. A comparison is made with the alternative methods. Methods that do not correct for measurement error bias perform very similarly and considerably worse than disattenuated regression

Fault diagnosis by refining the parameter uncertainty space of nonlinear dynamic systems

This paper deals with fault detection and isolation problems for nonlinear dynamic systems. Both problems are stated as constraint satisfaction problems (CSP) and solved using consistency techniques. The main contribution is the isolation method based on consistency techniques and uncertainty space refining of interval parameters. The major advantage of this method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements, and model errors. Interval calculations bring independence from the assumption of monotony considered by several approaches for fault isolation which are based on observers. An application to a well known alcoholic fermentation process model is presented