Efeito do substrato e das condições de tratamento do fermento sobre a fermentação etanólica contaminada com leveduras Saccharomyces cerevisiae selvagens e Lactobacillus fermentum

Pouco se sabe sobre o efeito do substrato e a interação entre as leveduras selvagens e bactérias do gênero Lactobacillus na fermentação alcoólica, pois os estudos tem se concentrado na avaliação dos efeitos da contaminação por um ou outro contaminante separadamente. Diante disso, este trabalho teve como objetivos estudar o efeito do substrato e das condições de tratamento do fermento sobre as fermentações contaminadas com ambos os micro-organismos, leveduras S. cerevisiae selvagens (três linhagens apresentando colônias rugosas e células dispostas em pseudohifas) e Lactobacillus fermentum, tendo a linhagem industrial de S. cerevisiae PE-2 como levedura do processo. Foram realizadas fermentações em batelada em mosto de caldo e de melaço, sem reciclo e com reciclo celular, utilizando tanto a cultura pura da linhagem PE-2 quanto as culturas mistas com as linhagens rugosas e ou L. fermentum. Foram avaliadas modificações no tratamento ácido do fermento, visando o controle do crescimento dos contaminantes sem afetar a levedura do processo. Em seguida, foram conduzidas fermentações contaminadas e não contaminadas submetidas ao tratamento ácido combinado com adição de etanol, tanto em caldo quanto em melaço, utilizando-se PE-2, uma das linhagens rugosas e L. fermentum. A atividade da invertase extracelular foi também avaliada em ambos os substratos para os micro-organismos estudados, em condições de crescimento. Concluiu-se que o tipo de substrato de fermentação, caldo de cana ou melaço, influenciou o desempenho da linhagem industrial PE-2 assim como afetou o desenvolvimento das contaminações com as leveduras rugosas S. cerevisiae na presença ou ausência da bactéria L. fermentum, em fermentações sem reciclo celular. O efeito da contaminação foi mais evidente quando se utilizou caldo de cana do que melaço como substrato, no caso da contaminação com leveduras rugosas, e o inverso no caso da contaminação com L. fermentum. O efeito da contaminação sobre a eficiência fermentativa foi maior na presença da levedura rugosa do que com a bactéria, e a contaminação dupla (tanto com a levedura rugosa quanto com a bactéria) não teve efeito maior sobre a eficiência fermentativa do que a contaminação simples, por um ou por outro micro-organismo isoladamente, especialmente na fermentação em batelada com reciclo celular, independentemente do substrato. Nas fermentações com reciclo de células, o efeito do substrato foi menos evidente. O controle do crescimento das linhagens rugosas pode ser realizado modificando o tratamento ácido normalmente realizado na indústria, seja pela adição de etanol à solução ácida ou pelo abaixamento do pH, dependendo da linhagem rugosa. O tratamento combinado baixo pH (2,0) + 13% etanol afetou a fisiologia da linhagem industrial, trazendo prejuízos à fermentação com reciclo celular, com pequeno controle sobre o crescimento da levedura rugosa e causando morte celular à L. fermentum. A diferença na atividade invertásica entre as linhagens rugosas e industrial de S. cerevisiae pode ser a responsável pela fermentação lenta apresentada pelas linhagens rugosas quando presentes na fermentação, sendo não significativa a influência do substrato sobre a atividade dessa enzima.

Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

In the Rough : In-Field evaluation of an autonomous vehicle for golf course maintenance

Maintenance is a time consuming and expensive task for any golf course or driving range manager. For a golf course the primary tasks are grass mowing and maintenance (fertilizer and herbicide spreading), while for a driving range mowing, maintenance and ball collection are required. All these tasks require an operator to drive a vehicle along paths which are generally predefined. This paper presents some preliminary in-field tsting results for an automated tractor vehicle performing golf ball collection on an actual driving range, and mowing on difficult unstructured terrain.

A Two-stage information filtering based on rough decision rule and pattern mining

Information Overload and Mismatch are two fundamental problems affecting the effectiveness of information filtering systems. Even though both term-based and patternbased approaches have been proposed to address the problems of overload and mismatch, neither of these approaches alone can provide a satisfactory solution to address these problems. This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern-based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experimental results based on the RCV1 corpus show that the proposed twostage filtering model significantly outperforms the both termbased and pattern-based information filtering models.

Rough sets based reasoning and pattern mining for a two-stage information filtering system

This paper presents a novel two-stage information filtering model which combines the merits of term-based and pattern- based approaches to effectively filter sheer volume of information. In particular, the first filtering stage is supported by a novel rough analysis model which efficiently removes a large number of irrelevant documents, thereby addressing the overload problem. The second filtering stage is empowered by a semantically rich pattern taxonomy mining model which effectively fetches incoming documents according to the specific information needs of a user, thereby addressing the mismatch problem. The experiments have been conducted to compare the proposed two-stage filtering (T-SM) model with other possible "term-based + pattern-based" or "term-based + term-based" IF models. The results based on the RCV1 corpus show that the T-SM model significantly outperforms other types of "two-stage" IF models.

Rough fuzzy control of SVC for power system stability enhancement

This paper presents a new approach to the design of a rough fuzzy controller for the control loop of the SVC (static VAR system) in a two area power system for stability enhancement with particular emphasis on providing effective damping for oscillatory instabilities. The performances of the rough fuzzy and the conventional fuzzy controller are compared with that of the conventional PI controller for a variety of transient disturbances, highlighting the effectiveness of the rough fuzzy controller in damping the inter-area oscillations. The effect of the rough fuzzy controller in improving the CCT (critical clearing time) of the two area system is elaborated in this paper as well.

Rough Game

Short Story About football Rugby League and City Country Clashes. Written to explore, examine and attempt to subvert generic conventions identified in PhD research. Good football fiction exists and it is plentiful.

Rough set based approach to text classification

Textual document set has become an important and rapidly growing information source in the web. Text classification is one of the crucial technologies for information organisation and management. Text classification has become more and more important and attracted wide attention of researchers from different research fields. In this paper, many feature selection methods, the implement algorithms and applications of text classification are introduced firstly. However, because there are much noise in the knowledge extracted by current data-mining techniques for text classification, it leads to much uncertainty in the process of text classification which is produced from both the knowledge extraction and knowledge usage, therefore, more innovative techniques and methods are needed to improve the performance of text classification. It has been a critical step with great challenge to further improve the process of knowledge extraction and effectively utilization of the extracted knowledge. Rough Set decision making approach is proposed to use Rough Set decision techniques to more precisely classify the textual documents which are difficult to separate by the classic text classification methods. The purpose of this paper is to give an overview of existing text classification technologies, to demonstrate the Rough Set concepts and the decision making approach based on Rough Set theory for building more reliable and effective text classification framework with higher precision, to set up an innovative evaluation metric named CEI which is very effective for the performance assessment of the similar research, and to propose a promising research direction for addressing the challenging problems in text classification, text mining and other relative fields.

Rough Seas

Favel Parrett’s second novel, When the Night Comes, opens with its teenage protagonist Isla lying awake in her bunk on a night ferry to Tasmania in the mid-1980s, ‘waiting for the rough seas’. Her younger brother sleeps beside her, and her distracted, emotionally distant mother – the kind of woman who is ‘always sitting places by herself in the night’ – is smoking on deck. Together, the three are weathering the roiling overnight passage in order to escape a violent past and make a new life in Hobart. The rough seas the novel goes on to navigate are, as one might expect, both literal and metaphorical...

Rough Fuzzy Control of SVC for Power System Stability Enhancement

Modern power systems have become more complex due to the growth in load demand, the installation of Flexible AC Transmission Systems (FACTS) devices and the integration of new HVDC links into existing AC grids. On the other hand, the introduction of the deregulated and unbundled power market operational mechanism, together with present changes in generation sources including connections of large renewable energy generation with intermittent feature in nature, have further increased the complexity and uncertainty for power system operation and control. System operators and engineers have to confront a series of technical challenges from the operation of currently interconnected power systems. Among the many challenges, how to evaluate the steady state and dynamic behaviors of existing interconnected power systems effectively and accurately using more powerful computational analysis models and approaches becomes one of the key issues in power engineering. The traditional computing techniques have been widely used in various fields for power system analysis with varying degrees of success. The rapid development of computational intelligence, such as neural networks, fuzzy systems and evolutionary computation, provides tools and opportunities to solve the complex technical problems in power system planning, operation and control.

Cytochrome c oxidase is preferentially synthesized in the rough endoplasmic reticulum--mitochondrion complex in rat liver

The specific activity and content of cytochrome oxidase in the rough endoplasmic reticulum--mitochondrion complex are higher than in the mitochondrial fraction. Radiolabelling studies with the use of hepatocytes and isolated microsomal and rough endoplasmic reticulum--mitochondrion fractions, followed by immunoprecipitation with anti-(cytochrome oxidase) antibody, reveal that the nuclear-coded cytoplasmic subunits of cytochrome oxidase are preferentially synthesized in the latter fraction. The results have a bearing on the mechanism of transport of these subunits into mitochondria.