Stochastic foundations of dynamic trade and labor market models

This dissertation consists of three self-contained papers that are related to two main topics. In particular, the first and third studies focus on labor market modeling, whereas the second essay presents a dynamic international trade setup.rnrnIn Chapter "Expenses on Labor Market Reforms during Transitional Dynamics", we investigate the arising costs of a potential labor market reform from a government point of view. To analyze various effects of unemployment benefits system changes, this chapter develops a dynamic model with heterogeneous employed and unemployed workers.rn rnIn Chapter "Endogenous Markup Distributions", we study how markup distributions adjust when a closed economy opens up. In order to perform this analysis, we first present a closed-economy general-equilibrium industry dynamics model, where firms enter and exit markets, and then extend our analysis to the open-economy case.rn rnIn Chapter "Unemployment in the OECD - Pure Chance or Institutions?", we examine effects of aggregate shocks on the distribution of the unemployment rates in OECD member countries.rn rnIn all three chapters we model systems that behave randomly and operate on stochastic processes. We therefore exploit stochastic calculus that establishes clear methodological links between the chapters.

ICOMF: Towards a Multi-cloud Ecosystem for Dynamic Resource Composition and Scaling

Modern cloud-based applications and infrastructures may include resources and services (components) from multiple cloud providers, are heterogeneous by nature and require adjustment, composition and integration. The specific application requirements can be met with difficulty by the current static predefined cloud integration architectures and models. In this paper, we propose the Intercloud Operations and Management Framework (ICOMF) as part of the more general Intercloud Architecture Framework (ICAF) that provides a basis for building and operating a dynamically manageable multi-provider cloud ecosystem. The proposed ICOMF enables dynamic resource composition and decomposition, with a main focus on translating business models and objectives to cloud services ensembles. Our model is user-centric and focuses on the specific application execution requirements, by leveraging incubating virtualization techniques. From a cloud provider perspective, the ecosystem provides more insight into how to best customize the offerings of virtualized resources.

Dynamic Model-based Management of Service-Oriented Infrastructure.

Models are an effective tool for systems and software design. They allow software architects to abstract from the non-relevant details. Those qualities are also useful for the technical management of networks, systems and software, such as those that compose service oriented architectures. Models can provide a set of well-defined abstractions over the distributed heterogeneous service infrastructure that enable its automated management. We propose to use the managed system as a source of dynamically generated runtime models, and decompose management processes into a composition of model transformations. We have created an autonomic service deployment and configuration architecture that obtains, analyzes, and transforms system models to apply the required actions, while being oblivious to the low-level details. An instrumentation layer automatically builds these models and interprets the planned management actions to the system. We illustrate these concepts with a distributed service update operation.

Multi-agent Architecture for Heterogeneous Reasoning under Uncertainty Combining MSBN and Ontologies in Distributed Network Diagnosis

This article proposes a MAS architecture for network diagnosis under uncertainty. Network diagnosis is divided into two inference processes: hypothesis generation and hypothesis confirmation. The first process is distributed among several agents based on a MSBN, while the second one is carried out by agents using semantic reasoning. A diagnosis ontology has been defined in order to combine both inference processes. To drive the deliberation process, dynamic data about the influence of observations are taken during diagnosis process. In order to achieve quick and reliable diagnoses, this influence is used to choose the best action to perform. This approach has been evaluated in a P2P video streaming scenario. Computational and time improvements are highlight as conclusions.

Response threshold models, stochastic learning automata and ant colony optimization-based decentralized self-coordination algorithms for heterogeneous multi-tasks distribution in multi-robot systems

In recent decades, there has been an increasing interest in systems comprised of several autonomous mobile robots, and as a result, there has been a substantial amount of development in the eld of Articial Intelligence, especially in Robotics. There are several studies in the literature by some researchers from the scientic community that focus on the creation of intelligent machines and devices capable to imitate the functions and movements of living beings. Multi-Robot Systems (MRS) can often deal with tasks that are dicult, if not impossible, to be accomplished by a single robot. In the context of MRS, one of the main challenges is the need to control, coordinate and synchronize the operation of multiple robots to perform a specic task. This requires the development of new strategies and methods which allow us to obtain the desired system behavior in a formal and concise way. This PhD thesis aims to study the coordination of multi-robot systems, in particular, addresses the problem of the distribution of heterogeneous multi-tasks. The main interest in these systems is to understand how from simple rules inspired by the division of labor in social insects, a group of robots can perform tasks in an organized and coordinated way. We are mainly interested on truly distributed or decentralized solutions in which the robots themselves, autonomously and in an individual manner, select a particular task so that all tasks are optimally distributed. In general, to perform the multi-tasks distribution among a team of robots, they have to synchronize their actions and exchange information. Under this approach we can speak of multi-tasks selection instead of multi-tasks assignment, which means, that the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation ix of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. In addition, it is very interesting the evaluation of the results in function in each approach, comparing the results obtained by the introducing noise in the number of pending loads, with the purpose of simulate the robot's error in estimating the real number of pending tasks. The main contribution of this thesis can be found in the approach based on self-organization and division of labor in social insects. An experimental scenario for the coordination problem among multiple robots, the robustness of the approaches and the generation of dynamic tasks have been presented and discussed. The particular issues studied are: Threshold models: It presents the experiments conducted to test the response threshold model with the objective to analyze the system performance index, for the problem of the distribution of heterogeneous multitasks in multi-robot systems; also has been introduced additive noise in the number of pending loads and has been generated dynamic tasks over time. Learning automata methods: It describes the experiments to test the learning automata-based probabilistic algorithms. The approach was tested to evaluate the system performance index with additive noise and with dynamic tasks generation for the same problem of the distribution of heterogeneous multi-tasks in multi-robot systems. Ant colony optimization: The goal of the experiments presented is to test the ant colony optimization-based deterministic algorithms, to achieve the distribution of heterogeneous multi-tasks in multi-robot systems. In the experiments performed, the system performance index is evaluated by introducing additive noise and dynamic tasks generation over time.

Low-cost retrofitting solutions for RC frames using masonry infill panels

A large number of reinforced concrete (RC) frame structures built in earthquake-prone areas such as Haiti are vulnerable to strong ground motions. Structures in developing countries need low-cost seismic retrofit solutions to reduce their vulnerability. This paper investigates the feasibility of using masonry infill walls to reduce deformations and damage caused by strong ground motions in brittle and weak RC frames designed only for gravity loads. A numerical experiment was conducted in which several idealized prototypes representing RC frame structures of school buildings damaged during the Port-au-Prince earthquake (Haiti, 2010) were strengthened by adding elements representing masonry infill walls arranged in different configurations. Each configuration was characterized by the ratio Rm of the area of walls in the direction of the ground motion (in plan) installed in each story to the total floor area. The numerical representations of these idealized RC frame structures with different values of Rm were (hypothetically) subjected to three major earthquakes with peak ground accelerations of approximately 0.5g. The results of the non-linear dynamic response analyses were summarized in tentative relationships between Rm and four parameters commonly used to characterize the seismic response of structures: interstory drift, Park and Ang indexes of damage, and total amount of energy dissipated by the main frame. It was found that Rm=4% is a reasonable minimum design value for seismic retrofitting purposes in cases in which available resources are not sufficient to afford conventional retrofit measures.

Decentralized multi-tasks distribution in heterogeneous robot teams by means of ant colony optimization and learning automata

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-election of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested on decentralized solution where the robots themselves autonomously and in an individual manner, are responsible of selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-tasks distribution problem and we propose a solution using two different approaches by applying Ant Colony Optimization-based deterministic algorithms as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithm, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Application of self-organizing techniques for the distribution of heterogeneous multi-tasks in multi-robot systems

This paper focuses on the general problem of coordinating of multi-robot systems, more specifically, it addresses the self-election of heterogeneous and specialized tasks by autonomous robots. In this regard, it has proposed experimenting with two different techniques based chiefly on selforganization and emergence biologically inspired, by applying response threshold models as well as ant colony optimization. Under this approach it can speak of multi-tasks selection instead of multi-tasks allocation, that means, as the agents or robots select the tasks instead of being assigned a task by a central controller. The key element in these algorithms is the estimation of the stimuli and the adaptive update of the thresholds. This means that each robot performs this estimate locally depending on the load or the number of pending tasks to be performed. It has evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Response threshold models and stochastic learning automata for self-coordination of heterogeneous multi-task distribution in multi-robot systems.

This paper focuses on the general problem of coordinating multiple robots. More specifically, it addresses the self-selection of heterogeneous specialized tasks by autonomous robots. In this paper we focus on a specifically distributed or decentralized approach as we are particularly interested in a decentralized solution where the robots themselves autonomously and in an individual manner, are responsible for selecting a particular task so that all the existing tasks are optimally distributed and executed. In this regard, we have established an experimental scenario to solve the corresponding multi-task distribution problem and we propose a solution using two different approaches by applying Response Threshold Models as well as Learning Automata-based probabilistic algorithms. We have evaluated the robustness of the algorithms, perturbing the number of pending loads to simulate the robot’s error in estimating the real number of pending tasks and also the dynamic generation of loads through time. The paper ends with a critical discussion of experimental results.

Dynamic stiffnesses of foundations

A general theory that described the B.I.E.M. in steady-state elastodynamics is developed. A comprehensive formulation for homogeneous and heterogeneous media is presented and also some results in practical cases as well as a general review of several other possibilities.

Dynamic expression of multiple scavenger receptor cysteine-rich genes in coelomocytes of the purple sea urchin

Coelomocytes, the heterogeneous population of sea urchin putative immune cells, were found to express a complex set of transcripts featuring scavenger receptor cysteine-rich (SRCR) repeats. SRCR domains define a metazoan superfamily of proteins, many of which are implicated in development and regulation of the immune system of vertebrates. Coelomocytes transcribe multiple SRCR genes from among a multigene family encoding an estimated number of 1,200 SRCR domains in specific patterns particular to each individual. Transcription levels for given SRCR genes may range from pronounced to undetectable, yet all tested animals harbor the genomic loci encoding these genes. Analysis of several SRCR genes revealed multiple loci corresponding to each type. In the case of one SRCR type, a cluster of at least three genes was detected within a 133-kb bacterial artificial chromosome insert, and conserved as well as unique regions were identified in sequences of three genomic clones derived from a single animal. Array hybridizations with repeated samples of coelomocyte messages revealed substantial alterations in levels of expression of many SRCR genes, with fluctuations of up to 10-fold in 1 week and up to 30-fold over a period of 3 months. This report is the first demonstration of genomic and transcriptional complexity in molecules expressed by invertebrate coelomocytes. The mechanisms controlling SRCR gene expression and the functional significance of this dynamic system await elucidation.

Dynamic friction and the origin of the complexity of earthquake sources.

We study a simple antiplane fault of finite length embedded in a homogeneous isotropic elastic solid to understand the origin of seismic source heterogeneity in the presence of nonlinear rate- and state-dependent friction. All the mechanical properties of the medium and friction are assumed homogeneous. Friction includes a characteristic length that is longer than the grid size so that our models have a well-defined continuum limit. Starting from a heterogeneous initial stress distribution, we apply a slowly increasing uniform stress load far from the fault and we simulate the seismicity for a few 1000 events. The style of seismicity produced by this model is determined by a control parameter associated with the degree of rate dependence of friction. For classical friction models with rate-independent friction, no complexity appears and seismicity is perfectly periodic. For weakly rate-dependent friction, large ruptures are still periodic, but small seismicity becomes increasingly nonstationary. When friction is highly rate-dependent, seismicity becomes nonperiodic and ruptures of all sizes occur inside the fault. Highly rate-dependent friction destabilizes the healing process producing premature healing of slip and partial stress drop. Partial stress drop produces large variations in the state of stress that in turn produce earthquakes of different sizes. Similar results have been found by other authors using the Burridge and Knopoff model. We conjecture that all models in which static stress drop is only a fraction of the dynamic stress drop produce stress heterogeneity.

Interactions within heterogeneous systems of uncontrolled rectifiers for aircraft electrical power systems

There is an increase in the use of multi-pulse, rectifier-fed motor-drive equipment on board more-electric aircraft. Motor drives with feedback control appear as constant power loads to the rectifiers, which can cause instability of the DC filter capacitor voltage at the output of the rectifier. This problem can be exacerbated by interactions between rectifiers that share a common source impedance. In order that such a system can be analysed, there is a need for average, dynamic models of systems of rectifiers. In this study, an efficient, compact method for deriving the approximate, linear, large-signal, average models of two heterogeneous systems of rectifiers, which are fed from a common source impedance, is presented. The models give insight into significant interaction effects that occur between the converters, and that arise through the shared source impedance. First, a 6-pulse and doubly wound, transformer-fed, 12-pulse rectifier system is considered, followed by a 6-pulse and autotransformer-fed, 12-pulse rectifier system. The system models are validated against detailed simulations and laboratory prototypes, and key characteristics of the two system types are compared.

Nonlinear penalties in dynamic optical networks employing autonomous transponders

We report for the first time on the limitations in the operational power range of network traffic in the presence of heterogeneous 28-Gbaud polarization-multiplexed quadrature amplitude modulation (PM-mQAM) channels in a nine-channel dynamic optical mesh network. In particular, we demonstrate that transponders which autonomously select a modulation order and launch power to optimize their own performance will have a severe impact on copropagating network traffic. Our results also suggest that altruistic transponder operation may offer even lower penalties than fixed launch power operation.

Dynamic restructuring of Pd nanoparticles and single crystals during catalytic selective alcohol oxidation

The aerobic selective oxidation (selox) of alcohols represents an environmentally benign and atom efficient chemical valorisation route to commercially important allylic aldehydes, such as crotonaldehyde and cinnamaldehyde, which find application in pesticides, fragrances and food additives. Palladium nanoparticles are highly active and selective heterogeneous catalysts for such oxidative dehydrogenations, permitting the use of air (or dioxygen) as a green oxidant in place of stoichiometric chromate permanganate saltsor H2O2. Here we discuss how time-resolved, in-situ X-ray spectroscopies (XAS and XPS) reveal dynamic restructuring of dispersed Pd nanoparticles and Pd single-crystals in response to changing reaction environments, and thereby identify surface PdO as the active species responsible for palladium catalysed crotyl alcohol selox (Figure 1); on-stream reduction to palladium metal under oxygen-poor regimes thus appears the primary cause of catalyst deactivation. This insight has guided the subsequent application of surfactant-templating and inorganic nanocrystal methodologies to optimize the density of desired active PdO sites for the selective oxidation of natural products such as sesquiterpenoids.