Environmental variables and fisheries diversity of the Naaf River Estuary, Bangladesh

The Naaf River estuary is one of the large estuaries in the Bangladesh coastal region not to have been affected by extensive human disturbance. This research provides information about the fisheries diversity status by Estuarine Set Bag Net (ESBN) sampling relation to physicochemical variables in both spatio-temporal scales. About 25 km of the lower estuary was divided into six zones for sample collection by considering the accessibility and availability of the ESBN operation, fish landing centers and location of the fishing villages. In total 48 samples have been analyzed which were taken throughout March to October 2006. To quantify the species diversity, all fisheries data were analyzed by using EstimateS and EcoSim software which accounts the different diversity indices viz., species richness, Shannon–Wiener diversity Index, Dominance and Evenness index. The research results demonstrate that the Naaf River estuary is a habitat of 161 (species richness, Sobs=161, Choa 1=162±2.34, ACE=161.73) different species which belong to 98 fin fishes, 23 shrimps and prawns,13 crabs, 11 molluscs, 3 echinoderms, 4 other crustaceans;while 9 remain unidentified. Results on the aquatic environment,mainly salinity and turbidity were found to have a major influence on their occurrence and distribution. All the findings indicated that the Naaf River estuary is a highly productive system and provides a favourable environment for large variety of estuarine species assemblages.

Neural and computational representations of decision variables

These studies explore how, where, and when representations of variables critical to decision-making are represented in the brain. In order to produce a decision, humans must first determine the relevant stimuli, actions, and possible outcomes before applying an algorithm that will select an action from those available. When choosing amongst alternative stimuli, the framework of value-based decision-making proposes that values are assigned to the stimuli and that these values are then compared in an abstract “value space” in order to produce a decision. Despite much progress, in particular regarding the pinpointing of ventromedial prefrontal cortex (vmPFC) as a region that encodes the value, many basic questions remain. In Chapter 2, I show that distributed BOLD signaling in vmPFC represents the value of stimuli under consideration in a manner that is independent of the type of stimulus it is. Thus the open question of whether value is represented in abstraction, a key tenet of value-based decision-making, is confirmed. However, I also show that stimulus-dependent value representations are also present in the brain during decision-making and suggest a potential neural pathway for stimulus-to-value transformations that integrates these two results.

More broadly speaking, there is both neural and behavioral evidence that two distinct control systems are at work during action selection. These two systems compose the “goal-directed system”, which selects actions based on an internal model of the environment, and the “habitual” system, which generates responses based on antecedent stimuli only. Computational characterizations of these two systems imply that they have different informational requirements in terms of input stimuli, actions, and possible outcomes. Associative learning theory predicts that the habitual system should utilize stimulus and action information only, while goal-directed behavior requires that outcomes as well as stimuli and actions be processed. In Chapter 3, I test whether areas of the brain hypothesized to be involved in habitual versus goal-directed control represent the corresponding theorized variables.

The question of whether one or both of these neural systems drives Pavlovian conditioning is less well-studied. Chapter 4 describes an experiment in which subjects were scanned while engaged in a Pavlovian task with a simple non-trivial structure. After comparing a variety of model-based and model-free learning algorithms (thought to underpin goal-directed and habitual decision-making, respectively), it was found that subjects’ reaction times were better explained by a model-based system. In addition, neural signaling of precision, a variable based on a representation of a world model, was found in the amygdala. These data indicate that the influence of model-based representations of the environment can extend even to the most basic learning processes.

Knowledge of the state of hidden variables in an environment is required for optimal inference regarding the abstract decision structure of a given environment and therefore can be crucial to decision-making in a wide range of situations. Inferring the state of an abstract variable requires the generation and manipulation of an internal representation of beliefs over the values of the hidden variable. In Chapter 5, I describe behavioral and neural results regarding the learning strategies employed by human subjects in a hierarchical state-estimation task. In particular, a comprehensive model fit and comparison process pointed to the use of "belief thresholding". This implies that subjects tended to eliminate low-probability hypotheses regarding the state of the environment from their internal model and ceased to update the corresponding variables. Thus, in concert with incremental Bayesian learning, humans explicitly manipulate their internal model of the generative process during hierarchical inference consistent with a serial hypothesis testing strategy.

Continuous stochastic processes

A general review of stochastic processes is given in the introduction; definitions, properties and a rough classification are presented together with the position and scope of the author's work as it fits into the general scheme.

The first section presents a brief summary of the pertinent analytical properties of continuous stochastic processes and their probability-theoretic foundations which are used in the sequel.

The remaining two sections (II and III), comprising the body of the work, are the author's contribution to the theory. It turns out that a very inclusive class of continuous stochastic processes are characterized by a fundamental partial differential equation and its adjoint (the Fokker-Planck equations). The coefficients appearing in those equations assimilate, in a most concise way, all the salient properties of the process, freed from boundary value considerations. The writer’s work consists in characterizing the processes through these coefficients without recourse to solving the partial differential equations.

First, a class of coefficients leading to a unique, continuous process is presented, and several facts are proven to show why this class is restricted. Then, in terms of the coefficients, the unconditional statistics are deduced, these being the mean, variance and covariance. The most general class of coefficients leading to the Gaussian distribution is deduced, and a complete characterization of these processes is presented. By specializing the coefficients, all the known stochastic processes may be readily studied, and some examples of these are presented; viz. the Einstein process, Bachelier process, Ornstein-Uhlenbeck process, etc. The calculations are effectively reduced down to ordinary first order differential equations, and in addition to giving a comprehensive characterization, the derivations are materially simplified over the solution to the original partial differential equations.

In the last section the properties of the integral process are presented. After an expository section on the definition, meaning, and importance of the integral process, a particular example is carried through starting from basic definition. This illustrates the fundamental properties, and an inherent paradox. Next the basic coefficients of the integral process are studied in terms of the original coefficients, and the integral process is uniquely characterized. It is shown that the integral process, with a slight modification, is a continuous Markoff process.

The elementary statistics of the integral process are deduced: means, variances, and covariances, in terms of the original coefficients. It is shown that an integral process is never temporally homogeneous in a non-degenerate process.

Finally, in terms of the original class of admissible coefficients, the statistics of the integral process are explicitly presented, and the integral process of all known continuous processes are specified.

Transmission matrices and lumped parameter models for continuous systems

The use of transmission matrices and lumped parameter models for describing continuous systems is the subject of this study. Non-uniform continuous systems which play important roles in practical vibration problems, e.g., torsional oscillations in bars, transverse bending vibrations of beams, etc., are of primary importance.

A new approach for deriving closed form transmission matrices is applied to several classes of non-uniform continuous segments of one dimensional and beam systems. A power series expansion method is presented for determining approximate transmission matrices of any order for segments of non-uniform systems whose solutions cannot be found in closed form. This direct series method is shown to give results comparable to those of the improved lumped parameter models for one dimensional systems.

Four types of lumped parameter models are evaluated on the basis of the uniform continuous one dimensional system by comparing the behavior of the frequency root errors. The lumped parameter models which are based upon a close fit to the low frequency approximation of the exact transmission matrix, at the segment level, are shown to be superior. On this basis an improved lumped parameter model is recommended for approximating non-uniform segments. This new model is compared to a uniform segment approximation and error curves are presented for systems whose areas very quadratically and linearly. The effect of varying segment lengths is investigated for one dimensional systems and results indicate very little improvement in comparison to the use of equal length segments. For purposes of completeness, a brief summary of various lumped parameter models and other techniques which have previously been used to approximate the uniform Bernoulli-Euler beam is a given.

Changes in physical and chemical variables

An article reviewing the work undertaken looking at the seasonal variation of chemical conditions in water at various depths in lakes. The laboratory tests undertaken for the research is outlined, as well as details of the sampling locations and the staff involved with the work. One figure shows the seasonal variation in the amounts of dissolved substances in the surface water of Windermere during 1936. Another figure shows seasonal varation inthe dry weight of phyto- and zooplankton in Windermere. Seasonal changes are discussed further and a table is included showing chemical conditions in winter and summer for Windermere.

Transverse superresolution with the radial continuous transmittance filter

The radial continuous transmittance filter is presented to realize transverse superresolution. It consists of two parallel polarizers and a radial birefringent element sandwiched between of them. By adjusting the angle between optical axis of the radial birefringent element and the polarization direction of the polarizers, transverse superresolution can be realized. But transverse superresolution is obtained at the cost of the axial resolution and the increase of the side-lobes in strength. So we then mend such filter, with it not only enhance the transverse resolution but also suppress the influence of the side-lobes and the reduction of the axial resolution. At the same time, the Strehl ratio increases. The advantage of such a filter used in superresolution technique is that it is easy to fabricate because its fabrication does not deal with the variation of the phase. (c) 2005 Elsevier GmbH. All rights reserved.

Modelagem, simulação e otimização operacional de unidades de síntese de metanol

A indústria de processos químicos tem sofrido consideráveis transformações devido ao acirramento da competitividade. Importantes progressos tecnológicos têm sido atingidos através de técnicas de modelagem, simulação e otimização visando o aumento da lucratividade e melhoria contínua nos processos industriais. Neste contexto, as plantas de metanol, um dos mais importantes produtos petroquímicos, podem ser destacadas. Atualmente, a principal matéria-prima para obtenção de metanol é o gás natural. A produção do metanol é caracterizada por três etapas: geração de gás de síntese, conversão do gás de síntese em metanol (unidade de síntese ou loop de síntese) e purificação do produto na especificação requerida. Os custos fixos e variáveis da unidade de síntese são fortemente dependentes das variáveis operacionais, como temperatura, pressão, razão de reciclo e composição da carga. Desta forma, foi desenvolvido um conjunto de modelos e algoritmos computacionais para representar matematicamente unidades de síntese de metanol. O modelo apresenta operações unitárias associadas aos seguintes equipamentos: divisores de correntes, misturadores de correntes, compressores, trocadores de calor, vasos de flash e reatores. Inicialmente, foi proposto um simulador estacionário, que serviu como base para um pseudo-estacionário, o qual contempla a desativação do catalisador por sinterização térmica. Os simuladores foram criados segundo uma arquitetura seqüencial modular e empregou-se o método de substituição sucessiva para a convergência dos reciclos. O estudo envolveu dois fluxogramas típicos, um constituído por reatores adiabáticos em série, enquanto o outro constituído por um reator tipo quench. Uma análise do efeito das principais variáveis operacionais foi realizada para o reator e para o loop de síntese. Estudou-se também o efeito da desativação do catalisador ao longo do tempo. Uma ferramenta de otimização operacional foi empregada para alcançar a máxima produção manipulando as injeções de carga fria na entrada dos leitos catalíticos. Formulou-se também um problema de maximização do lucro em função da otimização de paradas da unidade para troca do catalisador. Os resultados obtidos apontam que a ferramenta desenvolvida é promissora para a compreensão e otimização da unidade objeto deste estudo