Estratégia de navegação com planejamento dinâmico e algoritmo genético aplicada a robôs móveis terrestres

This work proposes a new autonomous navigation strategy assisted by genetic algorithm with dynamic planning for terrestrial mobile robots, called DPNA-GA (Dynamic Planning Navigation Algorithm optimized with Genetic Algorithm). The strategy was applied in environments - both static and dynamic - in which the location and shape of the obstacles is not known in advance. In each shift event, a control algorithm minimizes the distance between the robot and the object and maximizes the distance from the obstacles, rescheduling the route. Using a spatial location sensor and a set of distance sensors, the proposed navigation strategy is able to dynamically plan optimal collision-free paths. Simulations performed in different environments demonstrated that the technique provides a high degree of flexibility and robustness. For this, there were applied several variations of genetic parameters such as: crossing rate, population size, among others. Finally, the simulation results successfully demonstrate the effectiveness and robustness of DPNA-GA technique, validating it for real applications in terrestrial mobile robots.

Communication-efficient decentralized sequential detection

The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.

Cinematic and Aesthetic Cartographies of Subjective Mutation

This article examines the use of cinema as a mapping of subjective mutation in the work of Deleuze, Gauttari and Berardi. Drawing on Deleuze's distinction between the reduction of the art-work to the symptom and the idea of art as symptomatology, the article focuses on Berardi's use of cinematic examples, posing the question in each case of to what extent they function as symptomatologies or mere symptoms of cultural and subjective mutations in examples ranging from Bergman's Persona to Van Sant's Elephant to finish on speculations about Fincher's The Social Network as a critical engagement with subjective mutation in the 21st Century.

ERIC recommendations on TP53 mutation analysis in chronic lymphocytic leukemia.

Recent evidence suggests that - in addition to 17p deletion - TP53 mutation is an independent prognostic factor in chronic lymphocytic leukemia (CLL). Data from retrospective analyses and prospective clinical trials show that ∼5% of untreated CLL patients with treatment indication have a TP53 mutation in the absence of 17p deletion. These patients have a poor response and reduced progression-free survival and overall survival with standard treatment approaches. These data suggest that TP53 mutation testing warrants integration into current diagnostic work up of patients with CLL. There are a number of assays to detect TP53 mutations, which have respective advantages and shortcomings. Direct Sanger sequencing of exons 4-9 can be recommended as a suitable test to identify TP53 mutations for centers with limited experience with alternative screening methods. Recommendations are provided on standard operating procedures, quality control, reporting and interpretation. Patients with treatment indications should be investigated for TP53 mutations in addition to the work-up recommended by the International workshop on CLL guidelines. Patients with TP53 mutation may be considered for allogeneic stem cell transplantation in first remission. Alemtuzumab-based regimens can yield a substantial proportion of complete responses, although of short duration. Ideally, patients should be treated within clinical trials exploring new therapeutic agents.

Tyrosine kinase inhibitor insensitivity of non-cycling CD34+ human acute myeloid leukaemia cells with FMS-like tyrosine kinase 3 mutations.

The efficacy of tyrosine kinase (TK) inhibitors on non-cycling acute myeloid leukaemia (AML) cells, previously shown to have potent tumourigenic potential, is unknown. This pilot study describes the first attempt to characterize non-cycling cells from a small series of human FMS-like tyrosine kinase 3 (FLT3) mutation positive samples. CD34+ AML cells from patients with FLT3 mutation positive AML were cultured on murine stroma. In expansion cultures, non-cycling cells were found to retain CD34+ expression in contrast to dividing cells. Leukaemic gene rearrangements could be detected in non-cycling cells, indicating their leukaemic origin. Significantly, the FLT3-internal tandem duplication (ITD) mutation was found in the non-cycling fraction of four out of five cases. Exposure to the FLT3-directed inhibitor TKI258 clearly inhibited the growth of AML CD34+ cells in short-term cultures and colony-forming unit assays. Crucially, non-cycling cells were not eradicated, with the exception of one case, which exhibited exquisite sensitivity to the compound. Moreover, in longer-term cultures, TKI258-treated non-cycling cells showed no growth impairment compared to treatment-naive non-cycling cells. These findings suggest that non-cycling cells in AML may constitute a disease reservoir that is resistant to TK inhibition. Further studies with a larger sample size and other inhibitors are warranted.

Virtual Clinical Trials in PET Imaging for Improved Diagnosis of and Evaluation of Therapies for Cancer

Thesis (Ph.D.)--University of Washington, 2016-08

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization–maximization (MM) algorithm with a Nelder–Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

A distributed, compact routing protocol for the Internet

The Internet has grown in size at rapid rates since BGP records began, and continues to do so. This has raised concerns about the scalability of the current BGP routing system, as the routing state at each router in a shortest-path routing protocol will grow at a supra-linearly rate as the network grows. The concerns are that the memory capacity of routers will not be able to keep up with demands, and that the growth of the Internet will become ever more cramped as more and more of the world seeks the benefits of being connected. Compact routing schemes, where the routing state grows only sub-linearly relative to the growth of the network, could solve this problem and ensure that router memory would not be a bottleneck to Internet growth. These schemes trade away shortest-path routing for scalable memory state, by allowing some paths to have a certain amount of bounded “stretch”. The most promising such scheme is Cowen Routing, which can provide scalable, compact routing state for Internet routing, while still providing shortest-path routing to nearly all other nodes, with only slightly stretched paths to a very small subset of the network. Currently, there is no fully distributed form of Cowen Routing that would be practical for the Internet. This dissertation describes a fully distributed and compact protocol for Cowen routing, using the k-core graph decomposition. Previous compact routing work showed the k-core graph decomposition is useful for Cowen Routing on the Internet, but no distributed form existed. This dissertation gives a distributed k-core algorithm optimised to be efficient on dynamic graphs, along with with proofs of its correctness. The performance and efficiency of this distributed k-core algorithm is evaluated on large, Internet AS graphs, with excellent results. This dissertation then goes on to describe a fully distributed and compact Cowen Routing protocol. This protocol being comprised of a landmark selection process for Cowen Routing using the k-core algorithm, with mechanisms to ensure compact state at all times, including at bootstrap; a local cluster routing process, with mechanisms for policy application and control of cluster sizes, ensuring again that state can remain compact at all times; and a landmark routing process is described with a prioritisation mechanism for announcements that ensures compact state at all times.

Monitoramento e identificação numérico e experimental de danos em vigas e pontes de aço e concreto utilizando transformadas de wavelet

Tese (doutorado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.

Algoritmos de otimização e o problema inverso aplicados à identificação de danos estruturais com o uso do MEF

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.

POSITIVE FILTERED P_N METHOD FOR LINEAR TRANSPORT EQUATIONS AND THE ASSOCIATED OPTIMIZATION ALGORITHM

We propose a positive, accurate moment closure for linear kinetic transport equations based on a filtered spherical harmonic (FP_N) expansion in the angular variable. The FP_N moment equations are accurate approximations to linear kinetic equations, but they are known to suffer from the occurrence of unphysical, negative particle concentrations. The new positive filtered P_N (FP_N+) closure is developed to address this issue. The FP_N+ closure approximates the kinetic distribution by a spherical harmonic expansion that is non-negative on a finite, predetermined set of quadrature points. With an appropriate numerical PDE solver, the FP_N+ closure generates particle concentrations that are guaranteed to be non-negative. Under an additional, mild regularity assumption, we prove that as the moment order tends to infinity, the FP_N+ approximation converges, in the L2 sense, at the same rate as the FP_N approximation; numerical tests suggest that this assumption may not be necessary. By numerical experiments on the challenging line source benchmark problem, we confirm that the FP_N+ method indeed produces accurate and non-negative solutions. To apply the FP_N+ closure on problems at large temporal-spatial scales, we develop a positive asymptotic preserving (AP) numerical PDE solver. We prove that the propose AP scheme maintains stability and accuracy with standard mesh sizes at large temporal-spatial scales, while, for generic numerical schemes, excessive refinements on temporal-spatial meshes are required. We also show that the proposed scheme preserves positivity of the particle concentration, under some time step restriction. Numerical results confirm that the proposed AP scheme is capable for solving linear transport equations at large temporal-spatial scales, for which a generic scheme could fail. Constrained optimization problems are involved in the formulation of the FP_N+ closure to enforce non-negativity of the FP_N+ approximation on the set of quadrature points. These optimization problems can be written as strictly convex quadratic programs (CQPs) with a large number of inequality constraints. To efficiently solve the CQPs, we propose a constraint-reduced variant of a Mehrotra-predictor-corrector algorithm, with a novel constraint selection rule. We prove that, under appropriate assumptions, the proposed optimization algorithm converges globally to the solution at a locally q-quadratic rate. We test the algorithm on randomly generated problems, and the numerical results indicate that the combination of the proposed algorithm and the constraint selection rule outperforms other compared constraint-reduced algorithms, especially for problems with many more inequality constraints than variables.

Simulador de escoamento multifásico em poços de petróleo (SEMPP)

The multiphase flow occurrence in the oil and gas industry is common throughout fluid path, production, transportation and refining. The multiphase flow is defined as flow simultaneously composed of two or more phases with different properties and immiscible. An important computational tool for the design, planning and optimization production systems is multiphase flow simulation in pipelines and porous media, usually made by multiphase flow commercial simulators. The main purpose of the multiphase flow simulators is predicting pressure and temperature at any point at the production system. This work proposes the development of a multiphase flow simulator able to predict the dynamic pressure and temperature gradient in vertical, directional and horizontal wells. The prediction of pressure and temperature profiles was made by numerical integration using marching algorithm with empirical correlations and mechanistic model to predict pressure gradient. The development of this tool involved set of routines implemented through software programming Embarcadero C++ Builder® 2010 version, which allowed the creation of executable file compatible with Microsoft Windows® operating systems. The simulator validation was conduct by computational experiments and comparison the results with the PIPESIM®. In general, the developed simulator achieved excellent results compared with those obtained by PIPESIM and can be used as a tool to assist production systems development

Association of follicle stimulating hormone receptor promoter with ovarian response in IVF-ET patients

Background: Poor ovarian response phenomenon has been observed in some of the in vitro fertilization-embryo transfer patients. Some investigations found that follicle stimulating hormone receptor (FSHR) gene plays a role in the process, but no direct evidence shows the correlation between genotypes of FSHR and ovarian response. Objective: Exploring the molecular mechanism behind the mutation of FSHR promoter association with ovarian granulosa cells and poor ovarian response. Materials and Methods: This cross sectional study was performed using 158 women undergoing the controlled short program ovarian stimulation for IVF treatment. The 263 bp DNA fragments before the follicle stimulating hormone (FSH) receptor 5' initiation site were sequenced in the patients under IVF cycle, 70 of which had poor ovarian response and 88 showed normal ovarian responses. Results: With a mutation rate of 40%, 63 in 158 cases showed a 29th site G→A point mutation; among the mutated cases, the mutation rate of the poor ovarian responders was significantly higher than the normal group (60% versus 23.9%; χ2=21.450, p<0.001). Besides, the variability was also obvious in antral follicle count, and ovum pick-ups. The estradiol peak values and the number of mature eggs between the two groups had significant difference. However, there was no obvious variability (t=0.457, p=0.324) in the basic FSH values between the two groups (normal group, 7.2±2.3 U/L; mutation group, 7.1±2.0 U/L). Conclusion: The activity of FSHR promoter is significantly affected by the 29th site G→A mutation that will weaken promoter activity and result in poor response to FSH.

Dynamical modeling of biogas production supported by an experimental anaerobic digestion process

This paper presents the development of a combined experimental and numerical approach to study the anaerobic digestion of both the wastes produced in a biorefinery using yeast for biodiesel production and the wastes generated in the preceding microbial biomass production. The experimental results show that it is possible to valorise through anaerobic digestion all the tested residues. In the implementation of the numerical model for anaerobic digestion, a procedure for the identification of its parameters needs to be developed. A hybrid search Genetic Algorithm was used, followed by a direct search method. In order to test the procedure for estimation of parameters, first noise-free data was considered and a critical analysis of the results obtain so far was undertaken. As a demonstration of its application, the procedure was applied to experimental data.

Strategic Orientations, Dynamic Capabilities, and Firm Performance: an Analysis for Knowledge Intensive Business Services

The strategic orientations of a firm are considered crucial for enhancing firm performance and their impact can be even greater when associated with dynamic capabilities, particularly in complex and dynamic environments. This study empirically analyzes the relationship between market, entrepreneurial and learning orientations, dynamic capabilities, and performance using an integrative approach hitherto little explored. Using a sample of 209 knowledge intensive business service firms, this paper applies structural equation modeling to explore both direct effects of strategic orientations and the mediating role of dynamic capabilities on performance. The study demonstrates that learning orientation and one of the dimensions of entrepreneurial orientation have a direct positive effect on performance. On the other hand, dynamic capabilities mediate the relationships between some of the strategic orientations and firm performance. Overall, when dynamic capabilities are combined with the appropriate strategic orientations, they enhance firm performance. This paper contributes to a better understanding of the knowledge economy, given the important role knowledge intensive business services play in such a dynamic and pivotal sector.