Structure of magnetic poly(oxyethylene)-siloxane nanohybrids doped with Fe-II and Fe-III

Hybrid organic - inorganic nanocomposites doped with Fe-II and Fe-III ions and exhibiting interesting magnetic properties have been obtained by the sol - gel process. The hybrid matrix of these ormosils ( organically modified silicates), classed as di-ureasils and termed U( 2000), is composed of poly( oxyethylene) chains of variable length grafted to siloxane groups by means of urea crosslinkages. Iron perchlorate and iron nitrate were incorporated in the diureasil matrices, leading to compositions within the range 80 greater than or equal to n greater than or equal to 10, n being the molar ratio of ether-type O atoms per cation. The structure of the doped diureasils was investigated by small-angle X-ray scattering (SAXS). For Fe-II-doped samples, SAXS results suggest the existence of a two-level hierarchical structure. The primary level is composed of spatially correlated siloxane clusters embedded in the polymeric matrix and the secondary, coarser level consists of domains where the siloxane clusters are segregated. The structure of Fe-III-doped hybrids is different, revealing the existence of iron oxide based nanoclusters, identified as ferrihydrite by wide-angle X-ray diffraction, dispersed in the hybrid matrix. The magnetic susceptibility of these materials was determined by zero-field-cooling and field-cooling procedures as functions of both temperature and field. The different magnetic features between Fe-II- and Fe-III-doped samples are consistent with the structural differences revealed by SAXS. While Fe-II-doped composites exhibit a paramagnetic Curie-type behaviour, hybrids containing Fe-III ions show thermal and field irreversibilities.

Self-Structuring of Lamellar Bridged Silsesquioxanes with Long Side Spacers

Diurea cross-linked bridged silsesquioxanes (BSs) C(10)C(11)C(10) derived from organosilane precursors, including decylene chains as side spacers and alkylene chains with variable length as central spacers (EtO)(3)Si- (CH(2))(10)-Y(CH(2))(n)-Y-(CH(2))(10)-Si(OEt)(3) (n = 7, 9-12; Y = urea group and Et = ethyl), have been synthesized through the combination of self-directed assembly and an acid-catalyzed sol gel route involving the addition of dimethylsulfoxide (DMSO) and a large excess of water. This new family of hybrids has enabled us to conclude that the length of the side spacers plays a unique role in the structuring of alkylene-based BSs, although their morphology remains unaffected. All the samples adopt a lamellar structure. While the alkylene chains are totally disordered in the case of the C(10)C(7)C(10) sample, a variable proportion of all-trans and gauche conformers exists in the materials with longer central spacers. The highest degree of structuring occurs for n = 9. The inclusion of decylene instead of propylene chains as side spacers leads to the formation of a stronger hydrogen-bonded urea-urea array as evidenced by two dimensional correlation Fourier transform infrared spectroscopic analysis. The emission spectra and emission quantum yields of the C(10)C(n)C(10) Cm materials are similar to those reported for diurea cross-linked alkylene-based BSs incorporating propylene chains as side spacers and prepared under different experimental conditions. The emission of the C(10)C(n)C(10) hybrids is ascribed to the overlap of two distinct components that occur within the urea cross-linkages and within the siliceous nanodomains. Time-resolved photoluminescence spectroscopy has provided evidence that the average distance between the siliceous domains and the urea cross-links is similar in the C(10)C(n)C(10) BSs and in oxyethylene-based hybrid analogues incorporating propylene chains as side spacers (diureasils), an indication that the longer side chains in the former materials adopt gauche conformations. It has also allowed us to demonstrate for the first time that the emission features of the urea-related component of the emission of alkylene-based BSs depend critically on the length of the side spacers.

Evolução das disparidades da extração vegetal e da silvicultura na Amazônia Legal: uma aplicação da cadeia de Markov

O artigo analisa a convergência municipal da produtividade vegetal (extração vegetal e silvicultura) na região da Amazônia Legal entre os anos de 1996 e 2006. Para analisar a convergência, optou-se pela metodologia da matriz de transição de Markov (Processo Estacionário de Primeira Ordem de Markov). Os resultados mostram a existência de 13 classes de convergência da produtividade vegetal. No longo prazo, a hipótese de convergência absoluta não se mantém, visto que 68,23% dos municípios encontram-se numa classe inferior à média municipal, 33,54% em uma classe intermediária acima da média e 13,41% em uma classe superior acima da média.

Aplicações das Cadeias de Markov para o Ensino Médio

Pós-graduação em Matemática em Rede Nacional - IBILCE

Técnicas de otimização em alinhamentos múltiplos de sequência via Cadeias de Markov

Pós-graduação em Ciência da Computação - IBILCE

Machine-learning methods for structure prediction of β-barrel membrane proteins

Different types of proteins exist with diverse functions that are essential for living organisms. An important class of proteins is represented by transmembrane proteins which are specifically designed to be inserted into biological membranes and devised to perform very important functions in the cell such as cell communication and active transport across the membrane. Transmembrane β-barrels (TMBBs) are a sub-class of membrane proteins largely under-represented in structure databases because of the extreme difficulty in experimental structure determination. For this reason, computational tools that are able to predict the structure of TMBBs are needed. In this thesis, two computational problems related to TMBBs were addressed: the detection of TMBBs in large datasets of proteins and the prediction of the topology of TMBB proteins. Firstly, a method for TMBB detection was presented based on a novel neural network framework for variable-length sequence classification. The proposed approach was validated on a non-redundant dataset of proteins. Furthermore, we carried-out genome-wide detection using the entire Escherichia coli proteome. In both experiments, the method significantly outperformed other existing state-of-the-art approaches, reaching very high PPV (92%) and MCC (0.82). Secondly, a method was also introduced for TMBB topology prediction. The proposed approach is based on grammatical modelling and probabilistic discriminative models for sequence data labeling. The method was evaluated using a newly generated dataset of 38 TMBB proteins obtained from high-resolution data in the PDB. Results have shown that the model is able to correctly predict topologies of 25 out of 38 protein chains in the dataset. When tested on previously released datasets, the performances of the proposed approach were measured as comparable or superior to the current state-of-the-art of TMBB topology prediction.

New markov chain based methods for single and cross-domain sentiment classification

Nowadays communication is switching from a centralized scenario, where communication media like newspapers, radio, TV programs produce information and people are just consumers, to a completely different decentralized scenario, where everyone is potentially an information producer through the use of social networks, blogs, forums that allow a real-time worldwide information exchange. These new instruments, as a result of their widespread diffusion, have started playing an important socio-economic role. They are the most used communication media and, as a consequence, they constitute the main source of information enterprises, political parties and other organizations can rely on. Analyzing data stored in servers all over the world is feasible by means of Text Mining techniques like Sentiment Analysis, which aims to extract opinions from huge amount of unstructured texts. This could lead to determine, for instance, the user satisfaction degree about products, services, politicians and so on. In this context, this dissertation presents new Document Sentiment Classification methods based on the mathematical theory of Markov Chains. All these approaches bank on a Markov Chain based model, which is language independent and whose killing features are simplicity and generality, which make it interesting with respect to previous sophisticated techniques. Every discussed technique has been tested in both Single-Domain and Cross-Domain Sentiment Classification areas, comparing performance with those of other two previous works. The performed analysis shows that some of the examined algorithms produce results comparable with the best methods in literature, with reference to both single-domain and cross-domain tasks, in $2$-classes (i.e. positive and negative) Document Sentiment Classification. However, there is still room for improvement, because this work also shows the way to walk in order to enhance performance, that is, a good novel feature selection process would be enough to outperform the state of the art. Furthermore, since some of the proposed approaches show promising results in $2$-classes Single-Domain Sentiment Classification, another future work will regard validating these results also in tasks with more than $2$ classes.

A Markov chain location-allocation meta-model for hurricane relief

An emergency is a deviation from a planned course of events that endangers people, properties, or the environment. It can be described as an unexpected event that causes economic damage, destruction, and human suffering. When a disaster happens, Emergency Managers are expected to have a response plan to most likely disaster scenarios. Unlike earthquakes and terrorist attacks, a hurricane response plan can be activated ahead of time, since a hurricane is predicted at least five days before it makes landfall. This research looked into the logistics aspects of the problem, in an attempt to develop a hurricane relief distribution network model. We addressed the problem of how to efficiently and effectively deliver basic relief goods to victims of a hurricane disaster. Specifically, where to preposition State Staging Areas (SSA), which Points of Distributions (PODs) to activate, and the allocation of commodities to each POD. Previous research has addressed several of these issues, but not with the incorporation of the random behavior of the hurricane's intensity and path. This research presents a stochastic meta-model that deals with the location of SSAs and the allocation of commodities. The novelty of the model is that it treats the strength and path of the hurricane as stochastic processes, and models them as Discrete Markov Chains. The demand is also treated as stochastic parameter because it depends on the stochastic behavior of the hurricane. However, for the meta-model, the demand is an input that is determined using Hazards United States (HAZUS), a software developed by the Federal Emergency Management Agency (FEMA) that estimates losses due to hurricanes and floods. A solution heuristic has been developed based on simulated annealing. Since the meta-model is a multi-objective problem, the heuristic is a multi-objective simulated annealing (MOSA), in which the initial solution and the cooling rate were determined via a Design of Experiments. The experiment showed that the initial temperature (T0) is irrelevant, but temperature reduction (δ) must be very gradual. Assessment of the meta-model indicates that the Markov Chains performed as well or better than forecasts made by the National Hurricane Center (NHC). Tests of the MOSA showed that it provides solutions in an efficient manner. Thus, an illustrative example shows that the meta-model is practical.

O uso de modelos ocultos de Markov no estudo do fluxo de rios intermitentes

In this work, we present our understanding about the article of Aksoy [1], which uses Markov chains to model the flow of intermittent rivers. Then, we executed an application of his model in order to generate data for intermittent streamflows, based on a data set of Brazilian streams. After that, we build a hidden Markov model as a proposed new approach to the problem of simulation of such flows. We used the Gamma distribution to simulate the increases and decreases in river flows, along with a two-state Markov chain. The motivation for us to use a hidden Markov model comes from the possibility of obtaining the same information that the Aksoy’s model provides, but using a single tool capable of treating the problem as a whole, and not through multiple independent processes

Likelihood-Based Inference for Partially Observed Multi-Type Markov Branching Processes

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

Análise do crescimento econômico no Rio Grande do Sul através da aplicação de cadeias de Markov

O estudo do crescimento econômico é de suma importância para que possamos averiguar a trajetória de uma economia ao longo do tempo, a proposta desse trabalho é analisar o crescimento econômico no estado do Rio Grande do Sul, através do instrumental das cadeias de Markov, a ideia principal do estudo está na hipótese de convergência de renda. Primeiramente será testado a hipótese de convergência de renda do estado por meio das microrregiões, para isso serão utilizados dados de produto per capita dos anos de 1990, 2000 e 2010. Também será testado a hipótese de convergência para os municípios do Conselho Regional de Desenvolvimento Sul, situado no Rio Grande do Sul, utilizando dados de renda per capita dos anos de 1991, 2000 e 2010. Os resultados obtidos para as microrregiões do Rio Grande do Sul mostram que as economias não estão convergindo em sua totalidade para uma classe de renda especifica, porém é percebido que no longo prazo haverá uma maior concentração das microrregiões nos extratos de renda próximos a média, o tempo esperado para que as economias cheguem ao seu estado estacionário é de seis períodos. Por meio dos resultados obtidos para a região do Corede Sul, temos que as economias convergirão em sua maioria para a classe de renda médio pobre, seguido pela classe dos médios ricos. Ambas as classes estão situadas próximas a média regional, sendo que as classes de renda pobre e rico situadas aos extremos serão extintas no longo prazo. O tempo esperado para que as economias cheguem ao estado estacionário é de onze períodos.

Análise do crescimento econômico no Rio Grande do Sul através da aplicação de cadeias de markov

O estudo do crescimento econômico é de suma importância para que possamos averiguar a trajetória de uma economia ao longo do tempo, a proposta desse trabalho é analisar o crescimento econômico no estado do Rio Grande do Sul, através do instrumental das cadeias de Markov, a ideia principal do estudo está na hipótese de convergência de renda. Primeiramente será testado a hipótese de convergência de renda do estado por meio das microrregiões, para isso serão utilizados dados de produto per capita dos anos de 1990, 2000 e 2010. Também será testado a hipótese de convergência para os municípios do Conselho Regional de Desenvolvimento Sul, situado no Rio Grande do Sul, utilizando dados de renda per capita dos anos de 1991, 2000 e 2010. Os resultados obtidos para as microrregiões do Rio Grande do Sul mostram que as economias não estão convergindo em sua totalidade para uma classe de renda especifica, porém é percebido que no longo prazo haverá uma maior concentração das microrregiões nos extratos de renda próximos a média, o tempo esperado para que as economias cheguem ao seu estado estacionário é de seis períodos. Por meio dos resultados obtidos para a região do Corede Sul, temos que as economias convergirão em sua maioria para a classe de renda médio pobre, seguido pela classe dos médio ricos. Ambas as classes estão situadas próximas a média regional, sendo que as classes de renda pobre e rico situadas aos extremos serão extintas no longo prazo. O tempo esperado para que as economias cheguem ao estado estacionário é de onze períodos.

A path-based framework for analyzing large markov models

Reliability and dependability modeling can be employed during many stages of analysis of a computing system to gain insights into its critical behaviors. To provide useful results, realistic models of systems are often necessarily large and complex. Numerical analysis of these models presents a formidable challenge because the sizes of their state-space descriptions grow exponentially in proportion to the sizes of the models. On the other hand, simulation of the models requires analysis of many trajectories in order to compute statistically correct solutions. This dissertation presents a novel framework for performing both numerical analysis and simulation. The new numerical approach computes bounds on the solutions of transient measures in large continuous-time Markov chains (CTMCs). It extends existing path-based and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship makes it possible for the approach to explore multiple paths at the same time,· thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow it to analyze very large models using a very small amount of storage. Often, path-based techniques must compute many paths to obtain tight bounds. In addition to presenting the basic path-based approach, we also present algorithms for computing more paths and tighter bounds quickly. One resulting approach is based on the concept of path composition whereby precomputed subpaths are composed to compute the whole paths efficiently. Another approach is based on selecting important paths (among a set of many paths) for evaluation. Many path-based techniques suffer from having to evaluate many (unimportant) paths. Evaluating the important ones helps to compute tight bounds efficiently and quickly.

POLLING SYSTEMS WITH PARAMETER REGENERATION, THE GENERAL CASE

We consider a polling model with multiple stations, each with Poisson arrivals and a queue of infinite capacity. The service regime is exhaustive and there is Jacksonian feedback of served customers. What is new here is that when the server comes to a station it chooses the service rate and the feedback parameters at random; these remain valid during the whole stay of the server at that station. We give criteria for recurrence, transience and existence of the sth moment of the return time to the empty state for this model. This paper generalizes the model, when only two stations accept arriving jobs, which was considered in [Ann. Appl. Probab. 17 (2007) 1447-1473]. Our results are stated in terms of Lyapunov exponents for random matrices. From the recurrence criteria it can be seen that the polling model with parameter regeneration can exhibit the unusual phenomenon of null recurrence over a thick region of parameter space.

Evaluation of algorithms used to order markers on genetic maps

When building genetic maps, it is necessary to choose from several marker ordering algorithms and criteria, and the choice is not always simple. In this study, we evaluate the efficiency of algorithms try (TRY), seriation (SER), rapid chain delineation (RCD), recombination counting and ordering (RECORD) and unidirectional growth (UG), as well as the criteria PARF (product of adjacent recombination fractions), SARF (sum of adjacent recombination fractions), SALOD (sum of adjacent LOD scores) and LHMC (likelihood through hidden Markov chains), used with the RIPPLE algorithm for error verification, in the construction of genetic linkage maps. A linkage map of a hypothetical diploid and monoecious plant species was simulated containing one linkage group and 21 markers with fixed distance of 3 cM between them. In all, 700 F(2) populations were randomly simulated with and 400 individuals with different combinations of dominant and co-dominant markers, as well as 10 and 20% of missing data. The simulations showed that, in the presence of co-dominant markers only, any combination of algorithm and criteria may be used, even for a reduced population size. In the case of a smaller proportion of dominant markers, any of the algorithms and criteria (except SALOD) investigated may be used. In the presence of high proportions of dominant markers and smaller samples (around 100), the probability of repulsion linkage increases between them and, in this case, use of the algorithms TRY and SER associated to RIPPLE with criterion LHMC would provide better results. Heredity (2009) 103, 494-502; doi:10.1038/hdy.2009.96; published online 29 July 2009