Urban Detection From Hyperspectral Images Using Dimension-Reduction Model and Fusion of Multiple Segmentations Based on Stuctural and Textural Features

Ce mémoire de maîtrise présente une nouvelle approche non supervisée pour détecter et segmenter les régions urbaines dans les images hyperspectrales. La méthode proposée n ́ecessite trois étapes. Tout d’abord, afin de réduire le coût calculatoire de notre algorithme, une image couleur du contenu spectral est estimée. A cette fin, une étape de réduction de dimensionalité non-linéaire, basée sur deux critères complémentaires mais contradictoires de bonne visualisation; à savoir la précision et le contraste, est réalisée pour l’affichage couleur de chaque image hyperspectrale. Ensuite, pour discriminer les régions urbaines des régions non urbaines, la seconde étape consiste à extraire quelques caractéristiques discriminantes (et complémentaires) sur cette image hyperspectrale couleur. A cette fin, nous avons extrait une série de paramètres discriminants pour décrire les caractéristiques d’une zone urbaine, principalement composée d’objets manufacturés de formes simples g ́eométriques et régulières. Nous avons utilisé des caractéristiques texturales basées sur les niveaux de gris, la magnitude du gradient ou des paramètres issus de la matrice de co-occurrence combinés avec des caractéristiques structurelles basées sur l’orientation locale du gradient de l’image et la détection locale de segments de droites. Afin de réduire encore la complexité de calcul de notre approche et éviter le problème de la ”malédiction de la dimensionnalité” quand on décide de regrouper des données de dimensions élevées, nous avons décidé de classifier individuellement, dans la dernière étape, chaque caractéristique texturale ou structurelle avec une simple procédure de K-moyennes et ensuite de combiner ces segmentations grossières, obtenues à faible coût, avec un modèle efficace de fusion de cartes de segmentations. Les expérimentations données dans ce rapport montrent que cette stratégie est efficace visuellement et se compare favorablement aux autres méthodes de détection et segmentation de zones urbaines à partir d’images hyperspectrales.

Features of neighbourhood environments associated with obesity and related health behaviours in children: using multiple statistical approaches to identify obesogenic environments

Contexte: L'obésité chez les jeunes représente aujourd’hui un problème de santé publique à l’échelle mondiale. Afin d’identifier des cibles potentielles pour des stratégies populationnelles de prévention, les liens entre les caractéristiques du voisinage, l’obésité chez les jeunes et les habitudes de vie font de plus en plus l’objet d’études. Cependant, la recherche à ce jour comporte plusieurs incohérences. But: L’objectif général de cette thèse est d’étudier la contribution de différentes caractéristiques du voisinage relativement à l’obésité chez les jeunes et les habitudes de vie qui y sont associées. Les objectifs spécifiques consistent à: 1) Examiner les associations entre la présence de différents commerces d’alimentation dans les voisinages résidentiels et scolaires des enfants et leurs habitudes alimentaires; 2) Examiner comment l’exposition à certaines caractéristiques du voisinage résidentiel détermine l’obésité au niveau familial (chez le jeune, la mère et le père), ainsi que l’obésité individuelle pour chaque membre de la famille; 3) Identifier des combinaisons de facteurs de risque individuels, familiaux et du voisinage résidentiel qui prédisent le mieux l’obésité chez les jeunes, et déterminer si ces profils de facteurs de risque prédisent aussi un changement dans l’obésité après un suivi de deux ans. Méthodes: Les données proviennent de l’étude QUALITY, une cohorte québécoise de 630 jeunes, âgés de 8-10 ans au temps 1, avec une histoire d’obésité parentale. Les voisinages de 512 participants habitant la Région métropolitaine de Montréal ont été caractérisés à l’aide de : 1) données spatiales provenant du recensement et de bases de données administratives, calculées pour des zones tampons à partir du réseau routier et centrées sur le lieu de la résidence et de l’école; et 2) des observations menées par des évaluateurs dans le voisinage résidentiel. Les mesures du voisinage étudiées se rapportent aux caractéristiques de l’environnement bâti, social et alimentaire. L’obésité a été estimée aux temps 1 et 2 à l’aide de l’indice de masse corporelle (IMC) calculé à partir du poids et de la taille mesurés. Les habitudes alimentaires ont été mesurées au temps 1 à l'aide de trois rappels alimentaires. Les analyses effectuées comprennent, entres autres, des équations d'estimation généralisées, des régressions multiniveaux et des analyses prédictives basées sur des arbres de décision. Résultats: Les résultats démontrent la présence d’associations avec l’obésité chez les jeunes et les habitudes alimentaires pour certaines caractéristiques du voisinage. En particulier, la présence de dépanneurs et de restaurants-minutes dans le voisinage résidentiel et scolaire est associée avec de moins bonnes habitudes alimentaires. La présence accrue de trafic routier, ainsi qu’un faible niveau de prestige et d’urbanisation dans le voisinage résidentiel sont associés à l’obésité familiale. Enfin, les résultats montrent qu’habiter un voisinage obésogène, caractérisé par une défavorisation socioéconomique, la présence de moins de parcs et de plus de dépanneurs, prédit l'obésité chez les jeunes lorsque combiné à la présence de facteurs de risque individuels et familiaux. Conclusion: Cette thèse contribue aux écrits sur les voisinages et l’obésité chez les jeunes en considérant à la fois l'influence potentielle du voisinage résidentiel et scolaire ainsi que l’influence de l’environnement familial, en utilisant des méthodes objectives pour caractériser le voisinage et en utilisant des méthodes statistiques novatrices. Les résultats appuient en outre la notion que les efforts de prévention de l'obésité doivent cibler les multiples facteurs de risque de l'obésité chez les jeunes dans les environnements bâtis, sociaux et familiaux de ces jeunes.

Hessian-based occlusion-aware radiance caching

Simuler efficacement l'éclairage global est l'un des problèmes ouverts les plus importants en infographie. Calculer avec précision les effets de l'éclairage indirect, causés par des rebonds secondaires de la lumière sur des surfaces d'une scène 3D, est généralement un processus coûteux et souvent résolu en utilisant des algorithmes tels que le path tracing ou photon mapping. Ces techniquesrésolvent numériquement l'équation du rendu en utilisant un lancer de rayons Monte Carlo. Ward et al. ont proposé une technique nommée irradiance caching afin d'accélérer les techniques précédentes lors du calcul de la composante indirecte de l'éclairage global sur les surfaces diffuses. Krivanek a étendu l'approche de Ward et Heckbert pour traiter le cas plus complexe des surfaces spéculaires, en introduisant une approche nommée radiance caching. Jarosz et al. et Schwarzhaupt et al. ont proposé un modèle utilisant le hessien et l'information de visibilité pour raffiner le positionnement des points de la cache dans la scène, raffiner de manière significative la qualité et la performance des approches précédentes. Dans ce mémoire, nous avons étendu les approches introduites dans les travaux précédents au problème du radiance caching pour améliorer le positionnement des éléments de la cache. Nous avons aussi découvert un problème important négligé dans les travaux précédents en raison du choix des scènes de test. Nous avons fait une étude préliminaire sur ce problème et nous avons trouvé deux solutions potentielles qui méritent une recherche plus approfondie.

Investigations on The Finite Size Effects of Some Inverse Spinels and Studies on Their Composites Based on Nitrile Rubber

Magnetism and magnetic materials have been an ever-attractive subject area for engineers and scientists alike because of its versatility in finding applications in useful devices. They find applications in a host of devices ranging from rudimentary devices like loud speakers to sophisticated gadgets like waveguides and Magnetic Random Access Memories (MRAM).The one and only material in the realm of magnetism that has been at the centre stage of applications is ferrites and in that spinel ferrites received the lions share as far as practical applications are concerned.It has been the endeavour of scientists and engineers to remove obsolescence and improve upon the existing so as to save energy and integrate in to various other systems. This has been the hallmark of material scientists and this has led to new materials and new technologies.In the field of ferrites too there has been considerable interest to devise new materials based on iron oxides and other compounds. This means synthesising ultra fine particles and tuning its properties to device new materials. There are various preparation techniques ranging from top- down to bottom-up approaches. This includes synthesising at molecular level, self assembling,gas based condensation. Iow temperature eo-precipitation, solgel process and high energy ball milling. Among these methods sol-gel process allows good control of the properties of ceramic materials. The advantage of this method includes processing at low temperature. mixing at the molecular level and fabrication of novel materials for various devices.Composites are materials. which combine the good qualities of one or more components. They can be prepared in situ or by mechanical means by the incorporation of fine particles in appropriate matrixes. The size of the magnetic powders as well as the nature of matrix affect the processability and other physical properties of the final product. These plastic/rubber magnets can in turn be useful for various applications in different devices. In applications involving ferrites at high frequencies, it is essential that the material possesses an appropriate dielectric permittivity and suitable magnetic permeability. This can be achieved by synthesizing rubber ferrite composites (RFC's). RFCs are very useful materials for microwave absorptions. Hence the synthesis of ferrites in the nanoregirne.investigations on their size effects on the structural, magnetic, and electrical properties and the incorporation of these ferrites into polymer matrixes assume significance.In the present study, nano particles of NiFe204, Li(!5Fe2S04 and Col-e-O, are prepared by sol gel method. By appropriate heat treatments, particles of different grain sizes are obtained. The structural, magnetic and electrical measurements are evaluated as a function of grain size and temperature. NiFel04 prepared in the ultrafine regime are then incorporated in nitrile rubber matrix. The incorporation was carried out according to a specific recipe and for various loadings of magnetic fillers. The cure characteristics, magnetic properties, electrical properties and mechanical properties of these elastomer blends are carried out. The electrical permittivity of all the rubber samples in the X - band are also conducted.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Synthesis, characterization and properties of Conductive elastomeric composites based on Polypyrrole and short Nylon-6 fiber

The search for new materials especially those possessing special properties continues at a great pace because of ever growing demands of the modern life. The focus on the use of intrinsically conductive polymers in organic electronic devices has led to the development of a totally new class of smart materials. Polypyrrole (PPy) is one of the most stable known conducting polymers and also one of the easiest to synthesize. In addition, its high conductivity, good redox reversibility and excellent microwave absorbing characteristics have led to the existence of wide and diversified applications for PPy. However, as any conjugated conducting polymer, PPy lacks processability, flexibility and strength which are essential for industrial requirements. Among various approaches to making tractable materials based on PPy, incorporating PPy within an electrically insulating polymer appears to be a promising method, and this has triggered the development of blends or composites. Conductive elastomeric composites of polypyrrole are important in that they are composite materials suitable for devices where flexibility is an important parameter. Moreover these composites can be moulded into complex shapes. In this work an attempt has been made to prepare conducting elastomeric composites by the incorporation of PPy and PPy coated short Nylon-6 fiber with insulating elastomer matrices- natural rubber and acrylonitrile butadiene rubber. It is well established that mechanical properties of rubber composites can be greatly improved by adding short fibers. Generally short fiber reinforced rubber composites are popular in industrial fields because of their processing advantages, low cost, and their greatly improved technical properties such as strength, stiffness, modulus and damping. In the present work, PPy coated fiber is expected to improve the mechanical properties of the elastomer-PPy composites, at the same time increasing the conductivity. In addition to determination of DC conductivity and evaluation of mechanical properties, the work aims to study the thermal stability, dielectric properties and electromagnetic interference shielding effectiveness of the composites. The thesis consists of ten chapters.

Molecular Approaches For Characterization And Determination Of Pathogenicity Of Vibrios With Special Reference To Vibrio Harveyi From Penaeus Monodon Larval Production Systems

The present study focuses on vibrios especially Vibrio harveyi isolated from shrimp (P. monodon) larval production systems from both east and west coasts during times of mortality. A comprehensive approach has been made to work out their systematics through numerical taxonomy and group them based on RAPD profiling and to segregate the virulent from non- virulent isolates based on the presence of virulent genes as well as their phenotypic expression. The information gathered has helped to develop a simple scheme of identification based on phenotypic characters and segregate the virulent from non virulent strains of V. harveyi.

Offline Handwritten Malayalam Character Recognition Based on Chain Code Histogram

Optical Character Recognition plays an important role in Digital Image Processing and Pattern Recognition. Even though ambient study had been performed on foreign languages like Chinese and Japanese, effort on Indian script is still immature. OCR in Malayalam language is more complex as it is enriched with largest number of characters among all Indian languages. The challenge of recognition of characters is even high in handwritten domain, due to the varying writing style of each individual. In this paper we propose a system for recognition of offline handwritten Malayalam vowels. The proposed method uses Chain code and Image Centroid for the purpose of extracting features and a two layer feed forward network with scaled conjugate gradient for classification

Multifinger Feature Level Fusion Based Fingerprint Identification

Fingerprint based authentication systems are one of the cost-effective biometric authentication techniques employed for personal identification. As the data base population increases, fast identification/recognition algorithms are required with high accuracy. Accuracy can be increased using multimodal evidences collected by multiple biometric traits. In this work, consecutive fingerprint images are taken, global singularities are located using directional field strength and their local orientation vector is formulated with respect to the base line of the finger. Feature level fusion is carried out and a 32 element feature template is obtained. A matching score is formulated for the identification and 100% accuracy was obtained for a database of 300 persons. The polygonal feature vector helps to reduce the size of the feature database from the present 70-100 minutiae features to just 32 features and also a lower matching threshold can be fixed compared to single finger based identification

Characterization and applications of two protease enzymes obtained by culture dependent and independent approaches from mangrove sediments

Soil community genomics or metagenomics is employed in this study to analyze the evolutionary related - ness of mangrove microbial community. The metagenomic DNA was isolated from mangrove sediment and 16SrDNA was amplified using universal primers. The amplicons were ligated into pTZ57R/T cloning vector and transformed onto E. coli JM109 host cells. The recombinant plasmids were isolated from positive clones and the insert was confirmed by its reamplification. The amplicons were subjected to Amplified Ribosomal DNA Restriction Analysis (ARDRA) using three different tetra cutter restriction enzymes namely Sau3A1, Hha1 and HpaII. The 16SrDNA insert were sequenced and their identity was determined. The sequences were submitted to NCBI database and accession numbers obtained. The phylo - genetic tree was constructed based on Neighbor-Joining technique. Clones belonged to two major phyla of the bacterial domain, namely Firmicutes and Proteobacteria, with members of Firmicutes predominating. The microbial diversity of the mangrove sediment was explored in this manner.

Continuation and discontinuation of local institution in community based natural resource management

Currently the push toward frontier areas, which until twenty years ago were still largely untouched by commercial agriculture, is taking place on a massive scale. This push is being driven not the least by global economic developments, such as the price increase of agriculture commodities like coffee and cocoa. In most cases the indigenous communities become trapped between the state monopoly in natural resource management and the competition for resources by external actors. In this processes the indigenous communities start to lose their access to resources. Another victim in this process is the environment where the natural resources are imbedded. International and national organizations working to conserve environment have became conscious of the important role that indigenous people could fulfill as partners in this endeavour. This partnership in struggle has produced a new discourse on the relationship between indigenous people and their environment. As a further consequence, programs were set up to develop what became known as Community Based Natural Resource Management (CBNRM) with its numerous variations. Based on a case study in a village on the eastern border of the Lore Lindu National Park in Central Sulawesi, this study questioned the basic assumption behind the concept of Community Based Natural Resource Management (CBNRM). Namely the assumption that communities living at the margin of forest are socially and culturally homogenous, still more or less egalitarian, and basically living in harmony with their natural environment. This study was inspired by the persistent critique – although still a minority – on the basic assumption the CBNRM from academicians and practitioners working through the Entitlement perspective. Another inspiration was the mounting critique toward the participatory approach. In its effort the study explore further the usefulness of certain approaches. One of the approach much relied on in this study was the local history of the community studied, through exerting oral and local written documents on local history, legends and local stories. These sources proofed quite capable in bringing the local history into the light. Another was the actor oriented approach, which later came to be supported by the concept of Social Pool Resources. The latter concept proofed to be useful as analytical instrument to integrate social institutions and the common pool resources, as a field of action for the different actors as human agencies.

Management approaches in organic potato and tomato production

The presented thesis considered three different system approach topics to ensure yield and plant health in organically grown potatoes and tomatoes. The first topic describes interactions between late blight (Phytophthora infestans) incidence and soil nitrogen supply on yield in organic potato farming focussing in detail on the yield loss relationship of late blight based on results of several field trials. The interactive effects of soil N-supply, climatic conditions and late blight on the yield were studied in the presence and absence of copper fungicides from 2002-2004 for the potato cultivar Nicola. Under conditions of central Germany the use of copper significantly reduced late blight in almost all cases (15-30 %). However, the reductions in disease through copper application did not result in statistically significant yield increases (+0 – +10 %). Subsequently, only 30 % of the variation in yield could be attributed to disease reductions. A multiple regression model (R²Max), however, including disease reduction, growth duration and temperature sum from planting until 60 % disease severity was reached and soil mineral N contents 10 days after emergence could explain 75 % of the observed variations in yield. The second topic describes the effect of some selected organic fertilisers and biostimulant products on nitrogen-mineralization and efficiency, yield and diseases in organic potato and tomato trials. The organic fertilisers Biofeed Basis (BFB, plant derived, AgroBioProducts, Wageningen, Netherlands) and BioIlsa 12,5 Export (physically hydrolysed leather shavings, hair and skin of animals; ILSA, Arizignano, Italy) and two biostimulant products BioFeed Quality (BFQ, multi-compound seaweed extract, AgroBioProducts) and AUSMA (aqueous pine and spruce needle extract, A/S BIOLAT, Latvia), were tested. Both fertilisers supplied considerable amounts of nitrogen during the main uptake phases of the crops and reached yields as high or higher as compared to the control with horn meal fertilisation. The N-efficiency of the tested fertilisers in potatoes ranged from 90 to 159 kg yield*kg-1 N – input. Most effective with tomatoes were the combined treatments of fertiliser BFB and the biostimulants AUSMA and BFQ. Both biostimulants significantly increased the share of healthy fruit and/or the number of fruits. BFQ significantly increased potato yields (+6 %) in one out of two years and reduced R. solani-infestation in the potatoes. This suggests that the biostimulants had effects on plant metabolism and resistance properties. However, no effects of biostimulants on potato late blight could be observed in the fields. The third topic focused on the effect of suppressive composts and seed tuber health on the saprophytic pathogen Rhizoctonia solani in organic potato systems. In the present study 5t ha-1 DM of a yard and bio-waste (60/40) compost produced in a 5 month composting process and a 15 month old 100 % yard waste compost were used to assess the effects on potato infection with R. solani when applying composts within the limits allowed. Across the differences in initial seed tuber infestation and 12 cultivars 5t DM ha-1 of high quality composts, applied in the seed tuber area, reduced the infestation of harvested potatoes with black scurf, tuber malformations and dry core tubers by 20 to 84 %, 20 to 49 % and 38 to 54 %, respectively, while marketable yields were increased by 5 to 25 % due to lower rates of wastes after sorting (marketable yield is gross yield minus malformed tubers, tubers with dry core, tubers with black scurf > 15% infested skin). The rate of initial black scurf infection of the seed tubers also affected tuber number, health and quality significantly. Compared to healthy seed tubers initial black scurf sclerotia infestation of 2-5 and >10 % of tuber surface led in untreated plots to a decrease in marketable yields by 14-19 and 44-66 %, a increase of black scurf severity by 8-40 and 34-86 % and also increased the amount of malformed and dry core tubers by 32-57 and 109-214 %.

Decision‐Making in Public Good Dilemmas: Theory and Agent‐Based Simulation

In many real world contexts individuals find themselves in situations where they have to decide between options of behaviour that serve a collective purpose or behaviours which satisfy one’s private interests, ignoring the collective. In some cases the underlying social dilemma (Dawes, 1980) is solved and we observe collective action (Olson, 1965). In others social mobilisation is unsuccessful. The central topic of social dilemma research is the identification and understanding of mechanisms which yield to the observed cooperation and therefore resolve the social dilemma. It is the purpose of this thesis to contribute this research field for the case of public good dilemmas. To do so, existing work that is relevant to this problem domain is reviewed and a set of mandatory requirements is derived which guide theory and method development of the thesis. In particular, the thesis focusses on dynamic processes of social mobilisation which can foster or inhibit collective action. The basic understanding is that success or failure of the required process of social mobilisation is determined by heterogeneous individual preferences of the members of a providing group, the social structure in which the acting individuals are contained, and the embedding of the individuals in economic, political, biophysical, or other external contexts. To account for these aspects and for the involved dynamics the methodical approach of the thesis is computer simulation, in particular agent-based modelling and simulation of social systems. Particularly conductive are agent models which ground the simulation of human behaviour in suitable psychological theories of action. The thesis develops the action theory HAPPenInGS (Heterogeneous Agents Providing Public Goods) and demonstrates its embedding into different agent-based simulations. The thesis substantiates the particular added value of the methodical approach: Starting out from a theory of individual behaviour, in simulations the emergence of collective patterns of behaviour becomes observable. In addition, the underlying collective dynamics may be scrutinised and assessed by scenario analysis. The results of such experiments reveal insights on processes of social mobilisation which go beyond classical empirical approaches and yield policy recommendations on promising intervention measures in particular.

Sensor-based Control of Chewing and Rumination Behavior of Dairy Cows

The measurement of feed intake, feeding time and rumination time, summarized by the term feeding behavior, are helpful indicators for early recognition of animals which show deviations in their behavior. The overall objective of this work was the development of an early warning system for inadequate feeding rations and digestive and metabolic disorders, which prevention constitutes the basis for health, performance, and reproduction. In a literature review, the current state of the art and the suitability of different measurement tools to determine feeding behavior of ruminants was discussed. Five measurement methods based on different methodological approaches (visual observance, pressure transducer, electrical switches, electrical deformation sensors and acoustic biotelemetry), and three selected measurement techniques (the IGER Behavior Recorder, the Hi-Tag rumination monitoring system and RumiWatchSystem) were described, assessed and compared to each other within this review. In the second study, the new system for measuring feeding behavior of dairy cows was evaluated. The measurement of feeding behavior ensues through electromyography (EMG). For validation, the feeding behavior of 14 cows was determined by both the EMG system and by visual observation. The high correlation coefficients indicate that the current system is a reliable and suitable tool for monitoring the feeding behavior of dairy cows. The aim of a further study was to compare the DairyCheck (DC) system and two additional measurement systems for measuring rumination behavior in relation to efficiency, reliability and reproducibility, with respect to each other. The two additional systems were labeled as the Lely Qwes HR (HR) sensor, and the RumiWatchSystem (RW). Results of accordance of RW and DC to each other were high. The last study examined whether rumination time (RT) is affected by the onset of calving and if it might be a useful indicator for the prediction of imminent birth. Data analysis referred to the final 72h before the onset of calving, which were divided into twelve 6h-blocks. The results showed that RT was significantly reduced in the final 6h before imminent birth.