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The objective of this study was to investigate the moisture removal characteristics of thin layer rough rice heated by infrared (IR) and cooled with various cooling methods. Thin layer rough rice samples with different initial moisture contents (MCs) were heated using a catalytic IR emitter for four exposure times and radiation intensities. High heating rate and moisture removal were achieved during the IR heating period. After heating, more moisture removal was achieved during the cooling period. The achieved grain temperatures ranged from 35.1 to 68.4C under the tested heating conditions. The vacuum and forced air cooling methods removed more moisture than did the natural cooling. When rice with 25.7% MC was heated by IR, MC was reduced by 3.2, 3.5, and 3.8 percentage points for rice heated to 63.5C at the IR intensity of 5348 W/m2 for120 s followed by natural cooling for 40 min, forced air cooling for 5 min and vacuum cooling for 10 min, respectively. Practical Applications To design efficient infrared (IR) dryers for rough rice, it is important to optimize the operating parameters of IR dryer to achieve high heating rate, fast drying and good quality of end‐products. To achieve the aforementioned objectives, we have been conducting several studies including our previous publications (Pan, Khir et al. and this study). The outcomes of our studies have clearly indicated that a high heating rate, fast drying, good quality and simultaneous drying and disinfestation can be achieved by IR heating of rough rice to bout 60C followed by tempering and natural cooling with tested bed thickness up to 10 mm. Consequently, IR heating followed by cooling could be an effective approach for designing IR rough rice dryers. It is expected that this alternative approach could be used as an energy saving drying method with improved drying efficiency, space saving, clean working environment and superior product quality compared with the conventional heated air drying method.

<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; mso-layout-grid-align: none;" align="left"><span style="font-size: 9pt; mso-bidi-font-weight: bold;"><span style="font-family: Times New Roman;">The aim of this paper is to evaluate a Text to Knowledge Mapping (TKM) Prototype. The prototype is domain-specific, the purpose of which is to map instructional text onto a knowledge domain. The context of the knowledge domain is DC electrical circuit. During development, the prototype has been tested with a limited data set from the domain. The prototype reached a stage where it needs to be evaluated with a representative linguistic data set called corpus. A corpus is a collection of text drawn from typical sources which can be used as a test data set to evaluate NLP systems. As there is no available corpus for the domain, we developed and annotated a representative corpus. The evaluation of the prototype considers two of its major components- lexical components and knowledge model. Evaluation on lexical components enriches the lexical resources of the prototype like vocabulary and grammar structures. This leads the prototype to parse a reasonable amount of sentences in the corpus. While dealing with the lexicon was straight forward, the identification and extraction of appropriate semantic relations was much more involved. It was necessary, therefore, to manually develop a conceptual structure for the domain to formulate a domain-specific framework of semantic relations. The framework of semantic relationsthat has resulted from this study consisted of 55 relations, out of which 42 have inverse relations. We also conducted rhetorical analysis on the corpus to prove its representativeness in conveying semantic. Finally, we conducted a topical and discourse analysis on the corpus to analyze the coverage of discourse by the prototype. <em></em></span></span></p>

The aim of this paper is to evaluate the lexical components of a Text to Knowledge Mapping (TKM) prototype. The prototype is domain-specific, the purpose of which is to map instructional text onto a knowledge domain. The context of the knowledge domain of the prototype is physics, specifically DC electrical circuits. During development, the prototype has been tested with a limited data set from the domain. The prototype now reached a stage where it needs to be evaluated with a representative linguistic data set called corpus. A corpus is a collection of text drawn from typical sources which can be used as a test data set to evaluate NLP systems. As there is no available corpus for the domain, we developed a representative corpus and annotated it with linguistic information. The evaluation of the prototype considers one of its two main components-lexical knowledge base. With the corpus, the evaluation enriches the lexical knowledge resources like vocabulary and grammar structure. This leads the prototype to parse a reasonable amount of sentences in the corpus.