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The safe upper limit for inclusion of vitamin A in complete diets for growing dogs is uncertain, with the result that current recommendations range from 5.24 to 104.80 mu mol retinol (5000 to 100 000 IU vitamin A)/4184 kJ (1000 kcal) metabolisable energy (ME). The aim of the present study was to determine the effect of feeding four concentrations of vitamin A to puppies from weaning until 1 year of age. A total of forty-nine puppies, of two breeds, Labrador Retriever and Miniature Schnauzer, were randomly assigned to one of four treatment groups. Following weaning at 8 weeks of age, puppies were fed a complete food supplemented with retinyl acetate diluted in vegetable oil and fed at 1ml oil/100 g diet to achieve an intake of 5.24, 13.10, 78.60 and 104.80 mu mol retinol (5000, 12 500, 75 000 and 100 000 IU vitamin A)/4184 kJ (1000 kcal) ME. Fasted blood and urine samples were collected at 8, 10, 12, 14, 16, 20, 26, 36 and 52 weeks of age and analysed for markers of vitamin A metabolism and markers of safety including haematological and biochemical variables, bone-specific alkaline phosphatase, cross-linked carboxyterminal telopeptides of type I collagen and dual-energy X-ray absorptiometry. Clinical examinations were conducted every 4 weeks. Data were analysed by means of a mixed model analysis with Bonferroni corrections for multiple endpoints. There was no effect of vitamin A concentration on any of the parameters, with the exception of total serum retinyl esters, and no effect of dose on the number, type and duration of adverse events. We therefore propose that 104.80 mu mol retinol (100 000 IU vitamin A)/4184 kJ (1000 kcal) is a suitable safe upper limit for use in the formulation of diets designed for puppy growth.
The reaction of the German labor market to the Great Recession 2008/09 was relatively mild – especially compared to other countries. The reason lies not only in the specific type of the recession – which was favorable for the German economy structure – but also in a series of labor market reforms initiated between 2002 and 2005 altering, inter alia, labor supply incentives. However, irrespective of the mild response to the Great Recession, there are a number of substantial future challenges the German labor market will soon have to face. Female labor supply still lies well below that of other countries and a massive demographic change over the next 50 years will have substantial effects on labor supply as well as the pension system. In addition, due to a skill-biased technological change over the next decades, firms will face problems of finding employees with adequate skills. The aim of this paper is threefold. First, we outline why the German labor market reacted in such a mild fashion, describe current economic trends of the labor market in light of general trends in the European Union, and reveal some of the main associated challenges. Thereafter, the paper analyzes recent reforms of the main institutional settings of the labor market which influence labor supply. Finally, based on the status quo of these institutional settings, the paper gives a brief overview of strategies to combat adequately the challenges in terms of labor supply and to ensure economic growth in the future.
The closer the better
(2012)
A growing literature has suggested that processing of visual information presented near the hands is facilitated. In this study, we investigated whether the near-hands superiority effect also occurs with the hands moving. In two experiments, participants performed a cyclical bimanual movement task requiring concurrent visual identification of briefly presented letters. For both the static and dynamic hand conditions, the results showed improved letter recognition performance with the hands closer to the stimuli. The finding that the encoding advantage for near-hand stimuli also occurred with the hands moving suggests that the effect is regulated in real time, in accordance with the concept of a bimodal neural system that dynamically updates hand position in external space.
Duplicate detection is the task of identifying all groups of records within a data set that represent the same real-world entity, respectively. This task is difficult, because (i) representations might differ slightly, so some similarity measure must be defined to compare pairs of records and (ii) data sets might have a high volume making a pair-wise comparison of all records infeasible. To tackle the second problem, many algorithms have been suggested that partition the data set and compare all record pairs only within each partition. One well-known such approach is the Sorted Neighborhood Method (SNM), which sorts the data according to some key and then advances a window over the data comparing only records that appear within the same window. We propose several variations of SNM that have in common a varying window size and advancement. The general intuition of such adaptive windows is that there might be regions of high similarity suggesting a larger window size and regions of lower similarity suggesting a smaller window size. We propose and thoroughly evaluate several adaption strategies, some of which are provably better than the original SNM in terms of efficiency (same results with fewer comparisons).
Developing Critical Thinking
(2012)
Governments at central and sub-national levels are increasingly pursuing participatory mechanisms in a bid to improve governance and service delivery. This has been largely in the context of decentralization reforms in which central governments transfer (share) political, administrative, fiscal and economic powers and functions to sub-national units. Despite the great international support and advocacy for participatory governance where citizen’s voice plays a key role in decision making of decentralized service delivery, there is a notable dearth of empirical evidence as to the effect of such participation. This is the question this study sought to answer based on a case study of direct citizen participation in Local Authorities (LAs) in Kenya. This is as formally provided for by the Local Authority Service Delivery Action Plan (LASDAP) framework that was established to ensure citizens play a central role in planning and budgeting, implementation and monitoring of locally identified services towards improving livelihoods and reducing poverty. Influence of participation was assessed in terms of how it affected five key determinants of effective service delivery namely: efficient allocation of resources; equity in service delivery; accountability and reduction of corruption; quality of services; and, cost recovery. It finds that the participation of citizens is minimal and the resulting influence on the decentralized service delivery negligible. It concludes that despite the dismal performance of citizen participation, LASDAP has played a key role towards institutionalizing citizen participation that future structures will build on. It recommends that an effective framework of citizen participation should be one that is not directly linked to politicians; one that is founded on a legal framework and where citizens have a legal recourse opportunity; and, one that obliges LA officials both to implement what citizen’s proposals which meet the set criteria as well as to account for their actions in the management of public resources.
Developing critical thinking
(2012)
We develop the method of Fischer-Riesz equations for general boundary value problems elliptic in the sense of Douglis-Nirenberg. To this end we reduce them to a boundary problem for a (possibly overdetermined) first order system whose classical symbol has a left inverse. For such a problem there is a uniquely determined boundary value problem which is adjoint to the given one with respect to the Green formula. On using a well elaborated theory of approximation by solutions of the adjoint problem, we find the Cauchy data of solutions of our problem.
In many applications one is faced with the problem of inferring some functional relation between input and output variables from given data. Consider, for instance, the task of email spam filtering where one seeks to find a model which automatically assigns new, previously unseen emails to class spam or non-spam. Building such a predictive model based on observed training inputs (e.g., emails) with corresponding outputs (e.g., spam labels) is a major goal of machine learning. Many learning methods assume that these training data are governed by the same distribution as the test data which the predictive model will be exposed to at application time. That assumption is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for instance, in the above example of email spam filtering. Here, email service providers employ spam filters and spam senders engineer campaign templates such as to achieve a high rate of successful deliveries despite any filters. Most of the existing work casts such situations as learning robust models which are unsusceptible against small changes of the data generation process. The models are constructed under the worst-case assumption that these changes are performed such to produce the highest possible adverse effect on the performance of the predictive model. However, this approach is not capable to realistically model the true dependency between the model-building process and the process of generating future data. We therefore establish the concept of prediction games: We model the interaction between a learner, who builds the predictive model, and a data generator, who controls the process of data generation, as an one-shot game. The game-theoretic framework enables us to explicitly model the players' interests, their possible actions, their level of knowledge about each other, and the order at which they decide for an action. We model the players' interests as minimizing their own cost function which both depend on both players' actions. The learner's action is to choose the model parameters and the data generator's action is to perturbate the training data which reflects the modification of the data generation process with respect to the past data. We extensively study three instances of prediction games which differ regarding the order in which the players decide for their action. We first assume that both player choose their actions simultaneously, that is, without the knowledge of their opponent's decision. We identify conditions under which this Nash prediction game has a meaningful solution, that is, a unique Nash equilibrium, and derive algorithms that find the equilibrial prediction model. As a second case, we consider a data generator who is potentially fully informed about the move of the learner. This setting establishes a Stackelberg competition. We derive a relaxed optimization criterion to determine the solution of this game and show that this Stackelberg prediction game generalizes existing prediction models. Finally, we study the setting where the learner observes the data generator's action, that is, the (unlabeled) test data, before building the predictive model. As the test data and the training data may be governed by differing probability distributions, this scenario reduces to learning under covariate shift. We derive a new integrated as well as a two-stage method to account for this data set shift. In case studies on email spam filtering we empirically explore properties of all derived models as well as several existing baseline methods. We show that spam filters resulting from the Nash prediction game as well as the Stackelberg prediction game in the majority of cases outperform other existing baseline methods.
Education in knowledge society is challenged with a lot of problems in particular the interaction between the teacher and learner in social networking software as a key factor affects the learners’ learning and satisfaction (Prammanee, 2005) where “to teach is to communicate, to communicate is to interact, to interact is to learn” (Hefzallah, 2004, p. 48). Analyzing the relation between teacher-learner interaction from a side and learning outcome and learners’ satisfaction from the other side, some basic problems regarding a new learning culture using social networking software are discussed. Most of the educational institutions pay a lot of attentions to the equipments and emerging Information and Communication Technologies (ICTs) in learning situations. They try to incorporate ICT into their institutions as teaching and learning environments. They do this because they expect that by doing so they will improve the outcome of the learning process. Despite this, the learning outcome as reported in most studies is very limited, because the expectations of self-directed learning are much higher than the reality. Findings from an empirical study (investigating the role of teacher-learner interaction through new digital media wiki in higher education and learning outcome and learner’s satisfaction) are presented recommendations about the necessity of pedagogical interactions in support of teaching and learning activities in wiki courses in order to improve the learning outcome. Conclusions show the necessity for significant changes in the approach of vocational teacher training programs of online teachers in order to meet the requirements of new digital media in coherence with a new learning culture. These changes have to address collaborative instead of individual learning and ICT wiki as a tool for knowledge construction instead of a tool for gathering information.
Cargo transport by molecular motors is ubiquitous in all eukaryotic cells and is typically driven cooperatively by several molecular motors, which may belong to one or several motor species like kinesin, dynein or myosin. These motor proteins transport cargos such as RNAs, protein complexes or organelles along filaments, from which they unbind after a finite run length. Understanding how these motors interact and how their movements are coordinated and regulated is a central and challenging problem in studies of intracellular transport. In this thesis, we describe a general theoretical framework for the analysis of such transport processes, which enables us to explain the behavior of intracellular cargos based on the transport properties of individual motors and their interactions. Motivated by recent in vitro experiments, we address two different modes of transport: unidirectional transport by two identical motors and cooperative transport by actively walking and passively diffusing motors. The case of cargo transport by two identical motors involves an elastic coupling between the motors that can reduce the motors’ velocity and/or the binding time to the filament. We show that this elastic coupling leads, in general, to four distinct transport regimes. In addition to a weak coupling regime, kinesin and dynein motors are found to exhibit a strong coupling and an enhanced unbinding regime, whereas myosin motors are predicted to attain a reduced velocity regime. All of these regimes, which we derive both by analytical calculations and by general time scale arguments, can be explored experimentally by varying the elastic coupling strength. In addition, using the time scale arguments, we explain why previous studies came to different conclusions about the effect and relevance of motor-motor interference. In this way, our theory provides a general and unifying framework for understanding the dynamical behavior of two elastically coupled molecular motors. The second mode of transport studied in this thesis is cargo transport by actively pulling and passively diffusing motors. Although these passive motors do not participate in active transport, they strongly enhance the overall cargo run length. When an active motor unbinds, the cargo is still tethered to the filament by the passive motors, giving the unbound motor the chance to rebind and continue its active walk. We develop a stochastic description for such cooperative behavior and explicitly derive the enhanced run length for a cargo transported by one actively pulling and one passively diffusing motor. We generalize our description to the case of several pulling and diffusing motors and find an exponential increase of the run length with the number of involved motors.
One of the most exciting predictions of Einstein's theory of gravitation that have not yet been proven experimentally by a direct detection are gravitational waves. These are tiny distortions of the spacetime itself, and a world-wide effort to directly measure them for the first time with a network of large-scale laser interferometers is currently ongoing and expected to provide positive results within this decade. One potential source of measurable gravitational waves is the inspiral and merger of two compact objects, such as binary black holes. Successfully finding their signature in the noise-dominated data of the detectors crucially relies on accurate predictions of what we are looking for. In this thesis, we present a detailed study of how the most complete waveform templates can be constructed by combining the results from (A) analytical expansions within the post-Newtonian framework and (B) numerical simulations of the full relativistic dynamics. We analyze various strategies to construct complete hybrid waveforms that consist of a post-Newtonian inspiral part matched to numerical-relativity data. We elaborate on exsisting approaches for nonspinning systems by extending the accessible parameter space and introducing an alternative scheme based in the Fourier domain. Our methods can now be readily applied to multiple spherical-harmonic modes and precessing systems. In addition to that, we analyze in detail the accuracy of hybrid waveforms with the goal to quantify how numerous sources of error in the approximation techniques affect the application of such templates in real gravitational-wave searches. This is of major importance for the future construction of improved models, but also for the correct interpretation of gravitational-wave observations that are made utilizing any complete waveform family. In particular, we comprehensively discuss how long the numerical-relativity contribution to the signal has to be in order to make the resulting hybrids accurate enough, and for currently feasible simulation lengths we assess the physics one can potentially do with template-based searches.
One of the key challenges in service-oriented systems engineering is the prediction and assurance of non-functional properties, such as the reliability and the availability of composite interorganizational services. Such systems are often characterized by a variety of inherent uncertainties, which must be addressed in the modeling and the analysis approach. The different relevant types of uncertainties can be categorized into (1) epistemic uncertainties due to incomplete knowledge and (2) randomization as explicitly used in protocols or as a result of physical processes. In this report, we study a probabilistic timed model which allows us to quantitatively reason about nonfunctional properties for a restricted class of service-oriented real-time systems using formal methods. To properly motivate the choice for the used approach, we devise a requirements catalogue for the modeling and the analysis of probabilistic real-time systems with uncertainties and provide evidence that the uncertainties of type (1) and (2) in the targeted systems have a major impact on the used models and require distinguished analysis approaches. The formal model we use in this report are Interval Probabilistic Timed Automata (IPTA). Based on the outlined requirements, we give evidence that this model provides both enough expressiveness for a realistic and modular specifiation of the targeted class of systems, and suitable formal methods for analyzing properties, such as safety and reliability properties in a quantitative manner. As technical means for the quantitative analysis, we build on probabilistic model checking, specifically on probabilistic time-bounded reachability analysis and computation of expected reachability rewards and costs. To carry out the quantitative analysis using probabilistic model checking, we developed an extension of the Prism tool for modeling and analyzing IPTA. Our extension of Prism introduces a means for modeling probabilistic uncertainty in the form of probability intervals, as required for IPTA. For analyzing IPTA, our Prism extension moreover adds support for probabilistic reachability checking and computation of expected rewards and costs. We discuss the performance of our extended version of Prism and compare the interval-based IPTA approach to models with fixed probabilities.
This thesis contains several theoretical studies on optomechanical systems, i.e. physical devices where mechanical degrees of freedom are coupled with optical cavity modes. This optomechanical interaction, mediated by radiation pressure, can be exploited for cooling and controlling mechanical resonators in a quantum regime. The goal of this thesis is to propose several new ideas for preparing meso- scopic mechanical systems (of the order of 10^15 atoms) into highly non-classical states. In particular we have shown new methods for preparing optomechani-cal pure states, squeezed states and entangled states. At the same time, proce-dures for experimentally detecting these quantum effects have been proposed. In particular, a quantitative measure of non classicality has been defined in terms of the negativity of phase space quasi-distributions. An operational al- gorithm for experimentally estimating the non-classicality of quantum states has been proposed and successfully applied in a quantum optics experiment. The research has been performed with relatively advanced mathematical tools related to differential equations with periodic coefficients, classical and quantum Bochner’s theorems and semidefinite programming. Nevertheless the physics of the problems and the experimental feasibility of the results have been the main priorities.
Complex networks have been successfully employed to represent different levels of biological systems, ranging from gene regulation to protein-protein interactions and metabolism. Network-based research has mainly focused on identifying unifying structural properties, including small average path length, large clustering coefficient, heavy-tail degree distribution, and hierarchical organization, viewed as requirements for efficient and robust system architectures. Existing studies estimate the significance of network properties using a generic randomization scheme - a Markov-chain switching algorithm - which generates unrealistic reactions in metabolic networks, as it does not account for the physical principles underlying metabolism. Therefore, it is unclear whether the properties identified with this generic approach are related to the functions of metabolic networks. Within this doctoral thesis, I have developed an algorithm for mass-balanced randomization of metabolic networks, which runs in polynomial time and samples networks almost uniformly at random. The properties of biological systems result from two fundamental origins: ubiquitous physical principles and a complex history of evolutionary pressure. The latter determines the cellular functions and abilities required for an organism’s survival. Consequently, the functionally important properties of biological systems result from evolutionary pressure. By employing randomization under physical constraints, the salient structural properties, i.e., the smallworld property, degree distributions, and biosynthetic capabilities of six metabolic networks from all kingdoms of life are shown to be independent of physical constraints, and thus likely to be related to evolution and functional organization of metabolism. This stands in stark contrast to the results obtained from the commonly applied switching algorithm. In addition, a novel network property is devised to quantify the importance of reactions by simulating the impact of their knockout. The relevance of the identified reactions is verified by the findings of existing experimental studies demonstrating the severity of the respective knockouts. The results suggest that the novel property may be used to determine the reactions important for viability of organisms. Next, the algorithm is employed to analyze the dependence between mass balance and thermodynamic properties of Escherichia coli metabolism. The thermodynamic landscape in the vicinity of the metabolic network reveals two regimes of randomized networks: those with thermodynamically favorable reactions, similar to the original network, and those with less favorable reactions. The results suggest that there is an intrinsic dependency between thermodynamic favorability and evolutionary optimization. The method is further extended to optimizing metabolic pathways by introducing novel chemically feasibly reactions. The results suggest that, in three organisms of biotechnological importance, introduction of the identified reactions may allow for optimizing their growth. The approach is general and allows identifying chemical reactions which modulate the performance with respect to any given objective function, such as the production of valuable compounds or the targeted suppression of pathway activity. These theoretical developments can find applications in metabolic engineering or disease treatment. The developed randomization method proposes a novel approach to measuring the significance of biological network properties, and establishes a connection between large-scale approaches and biological function. The results may provide important insights into the functional principles of metabolic networks, and open up new possibilities for their engineering.
Since available phosphate (Pi) resources in soil are limited, symbiotic interactions between plant roots and arbuscular mycorrhizal (AM) fungi are a widespread strategy to improve plant phosphate nutrition. The repression of AM symbiosis by a high plant Pi-status indicates a link between Pi homeostasis signalling and AM symbiosis development. This assumption is supported by the systemic induction of several microRNA399 (miR399) primary transcripts in shoots and a simultaneous accumulation of mature miR399 in roots of mycorrhizal plants. However, the physiological role of this miR399 expression pattern is still elusive and offers the question whether other miRNAs are also involved in AM symbiosis. Therefore, a deep sequencing approach was applied to investigate miRNA-mediated posttranscriptional gene regulation in M. truncatula mycorrhizal roots. Degradome analysis revealed that 185 transcripts were cleaved by miRNAs, of which the majority encoded transcription factors and disease resistance genes, suggesting a tight control of transcriptional reprogramming and a downregulation of defence responses by several miRNAs in mycorrhizal roots. Interestingly, 45 of the miRNA-cleaved transcripts showed a significant differentially regulated between mycorrhizal and non-mycorrhizal roots. In addition, key components of the Pi homeostasis signalling pathway were analyzed concerning their expression during AM symbiosis development. MtPhr1 overexpression and time course expression data suggested a strong interrelation between the components of the PHR1-miR399-PHO2 signalling pathway and AM symbiosis, predominantly during later stages of symbiosis. In situ hybridizations confirmed accumulation of mature miR399 in the phloem and in arbuscule-containing cortex cells of mycorrhizal roots. Moreover, a novel target of the miR399 family, named as MtPt8, was identified by the above mentioned degradome analysis. MtPt8 encodes a Pi-transporter exclusively transcribed in mycorrhizal roots and its promoter activity was restricted to arbuscule-containing cells. At a low Pi-status, MtPt8 transcript abundance inversely correlated with a mature miR399 expression pattern. Increased MtPt8 transcript levels were accompanied by elevated symbiotic Pi-uptake efficiency, indicating its impact on balancing plant and fungal Pi-acquisition. In conclusion, this study provides evidence for a direct link of the regulatory mechanisms of plant Pi-homeostasis and AM symbiosis at a cell-specific level. The results of this study, especially the interaction of miR399 and MtPt8 provide a fundamental step for future studies of plant-microbe-interactions with regard to agricultural and ecological aspects.
During the overall development of complex engineering systems different modeling notations are employed. For example, in the domain of automotive systems system engineering models are employed quite early to capture the requirements and basic structuring of the entire system, while software engineering models are used later on to describe the concrete software architecture. Each model helps in addressing the specific design issue with appropriate notations and at a suitable level of abstraction. However, when we step forward from system design to the software design, the engineers have to ensure that all decisions captured in the system design model are correctly transferred to the software engineering model. Even worse, when changes occur later on in either model, today the consistency has to be reestablished in a cumbersome manual step. In this report, we present in an extended version of [Holger Giese, Stefan Neumann, and Stephan Hildebrandt. Model Synchronization at Work: Keeping SysML and AUTOSAR Models Consistent. In Gregor Engels, Claus Lewerentz, Wilhelm Schäfer, Andy Schürr, and B. Westfechtel, editors, Graph Transformations and Model Driven Enginering - Essays Dedicated to Manfred Nagl on the Occasion of his 65th Birthday, volume 5765 of Lecture Notes in Computer Science, pages 555–579. Springer Berlin / Heidelberg, 2010.] how model synchronization and consistency rules can be applied to automate this task and ensure that the different models are kept consistent. We also introduce a general approach for model synchronization. Besides synchronization, the approach consists of tool adapters as well as consistency rules covering the overlap between the synchronized parts of a model and the rest. We present the model synchronization algorithm based on triple graph grammars in detail and further exemplify the general approach by means of a model synchronization solution between system engineering models in SysML and software engineering models in AUTOSAR which has been developed for an industrial partner. In the appendix as extension to [19] the meta-models and all TGG rules for the SysML to AUTOSAR model synchronization are documented.
Theory of mRNA degradation
(2012)
One of the central themes of biology is to understand how individual cells achieve a high fidelity in gene expression. Each cell needs to ensure accurate protein levels for its proper functioning and its capability to proliferate. Therefore, complex regulatory mechanisms have evolved in order to render the expression of each gene dependent on the expression level of (all) other genes. Regulation can occur at different stages within the framework of the central dogma of molecular biology. One very effective and relatively direct mechanism concerns the regulation of the stability of mRNAs. All organisms have evolved diverse and powerful mechanisms to achieve this. In order to better comprehend the regulation in living cells, biochemists have studied specific degradation mechanisms in detail. In addition to that, modern high-throughput techniques allow to obtain quantitative data on a global scale by parallel analysis of the decay patterns of many different mRNAs from different genes. In previous studies, the interpretation of these mRNA decay experiments relied on a simple theoretical description based on an exponential decay. However, this does not account for the complexity of the responsible mechanisms and, as a consequence, the exponential decay is often not in agreement with the experimental decay patterns. We have developed an improved and more general theory of mRNA degradation which provides a general framework of mRNA expression and allows describing specific degradation mechanisms. We have made an attempt to provide detailed models for the regulation in different organisms. In the yeast S. cerevisiae, different degradation pathways are known to compete and furthermore most of them rely on the biochemical modification of mRNA molecules. In bacteria such as E. coli, degradation proceeds primarily endonucleolytically, i.e. it is governed by the initial cleavage within the coding region. In addition, it is often coupled to the level of maturity and the size of the polysome of an mRNA. Both for S. cerevisiae and E. coli, our descriptions lead to a considerable improvement of the interpretation of experimental data. The general outcome is that the degradation of mRNA must be described by an age-dependent degradation rate, which can be interpreted as a consequence of molecular aging of mRNAs. Within our theory, we find adequate ways to address this much debated topic from a theoretical perspective. The improvements of the understanding of mRNA degradation can be readily applied to further comprehend the mRNA expression under different internal or environmental conditions such as after the induction of transcription or stress application. Also, the role of mRNA decay can be assessed in the context of translation and protein synthesis. The ultimate goal in understanding gene regulation mediated by mRNA stability will be to identify the relevance and biological function of different mechanisms. Once more quantitative data will become available, our description allows to elaborate the role of each mechanism by devising a suitable model.
This paper develops a spatial model to analyze the stability of a market sharing agreement between two firms. We find that the stability of the cartel depends on the relative market size of each firm. Collusion is not attractive for firms with a small home market, but the incentive for collusion increases when the firm’s home market is getting larger relative to the home market of the competitor. The highest stability of a cartel and additionally the highest social welfare is found when regions are symmetric. Further we can show that a monetary transfer can stabilize the market sharing agreement.