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In order to improve unlimited TTS, a framework to organize the multiple perceived units into discourse is proposed in [1]. To make an unlimited TTS system, we must transform the original text to the text with corresponding boundary breaks. So we describe how we predicate prosody from Text in this paper. We use the corpora with boundary breaks which follow the prosody framework. Then we use the lexical and syntactic information to predict prosody from text. The result shows that the weighted precision in our model is better than some speakers. We have shown our model can predict a reasonable prosody form text.

In order to improve unlimited TTS, a framework to organize the multiple perceived units into discourse is proposed in [1]. To make an unlimited TTS system, we must transform the original text to the text with corresponding boundary breaks. So we describe how we predicate prosody from Text in this paper. We use the corpora with boundary breaks which follow the prosody framework. Then we use the lexical and syntactic information to predict prosody from text. The result shows that the weighted precision in our model is better than some speakers. We have shown our model can predict a reasonable prosody form text.

The layer of substrate in trays is limited to a bed height of around 5 cm by considerations of heat and O transfer within the bed. Therefore scale-up of the process cannot be achieved by increasing the bed height. The only manner to scale up a tray process to large scale is to increase the surface area of the trays, which is equivalent to saying that the large-scale process must use a large number of trays of the same size as those in which the laboratory studies were done. The use of large numbers of trays implies the necessity either for manual handling or highly sophisticated robotic systems, both of which can be inordinately expensive. However, in regions in which manual labor costs are low, such tray-type processes may find applications.

In order to improve unlimited TTS, a framework to organize the multiple perceived units into discourse is proposed in [1]. To make an unlimited TTS system, we must transform the original text to the text with corresponding boundary breaks. So we describe how we predicate prosody from Text in this paper. We use the corpora with boundary breaks which follow the prosody framework. Then we use the lexical and syntactic information to predict prosody from text. The result shows that the weighted precision in our model is better than some speakers. We have shown our model can predict a reasonable prosody form text.

In order to improve unlimited TTS, a framework to organize the multiple perceived units into discourse is proposed in [1]. To make an unlimited TTS system, we must transform the original text to the text with corresponding boundary breaks. So we describe how we predicate prosody from Text in this paper. We use the corpora with boundary breaks which follow the prosody framework. Then we use the lexical and syntactic information to predict prosody from text. The result shows that the weighted precision in our model is better than some speakers. We have shown our model can predict a reasonable prosody form text.

The layer of substrate in trays is limited to a bed height of around 5 cm by considerations of heat and O transfer within the bed. Therefore scale-up of the process cannot be achieved by increasing the bed height. The only manner to scale up a tray process to large scale is to increase the surface area of the trays, which is equivalent to saying that the large-scale process must use a large number of trays of the same size as those in which the laboratory studies were done. The use of large numbers of trays implies the necessity either for manual handling or highly sophisticated robotic systems, both of which can be inordinately expensive. However, in regions in which manual labor costs are low, such tray-type processes may find applications.

While attention should certainly be given to furthering the development of fully-mechanistic models, at the moment fast-solving models are sufficiently accurate to be useful tools in the design of bioreactors and the optimization of their operation. The rest of the book concentrates on fast-solving models. Chapters 14 to 17 describe approaches to establishing and modeling the growth kinetics in a manner appropriate for incorporation into fast-solving models. Chapters 18 to 20 show how the heat and mass transfer phenomena within bioreactors can be described at an appropriate level of detail for a fast-solving model. Chapters 22 to 25 then present several fast-solving models and show how they can be used to give insights into optimal design and operation. We are confident that readers, with relatively little effort, can adapt these models to their own systems, and obtain useful results from doing so.

This chapter has shown how the basic empirical kinetic equation is written, and how the parameters of the equation can be written as functions of the key environmental variables. The next chapter extends the discussion to how we can model the effects that growth has on the environment of the organism.

In order to improve unlimited TTS, a framework to organize the multiple perceived units into discourse is proposed in [1]. To make an unlimited TTS system, we must transform the original text to the text with corresponding boundary breaks. So we describe how we predicate prosody from Text in this paper. We use the corpora with boundary breaks which follow the prosody framework. Then we use the lexical and syntactic information to predict prosody from text. The result shows that the weighted precision in our model is better than some speakers. We have shown our model can predict a reasonable prosody form text.

Intermittently-mixed, forcefully-aerated bioreactors appear to have some potential, judging by the fact that several processes involving bioreactors that operate in this mode have been demonstrated at a reasonably large scale. They appear to offer some benefits in control of the conditions within the bed, while minimizing the deleterious effects that continuous mixing can have, at least for fungal processes.

Based on what is known to date, it would seem that the best strategy is not to try to use mixing of the bed directly as a temperature control strategy. For fungal fermentations such a strategy would lead to intolerably frequent mixing events. Rather, the mixing events should be used to:

Attempts to control the temperature in such bioreactors therefore should be focused on manipulation of the temperature, humidity, and flow rate of the inlet air. These have not been explored to any great extent, but Chap. 25 will present a mathematical model of an intermittently-mixed, forcefully-aerated bioreactor that can be used to explore the question of how best to operate such bioreactors in order to control the temperature.