and expiration-can be as high as 60-70% (Wallace, Willis, Nwaze, & Dieng, 2017; Zaffran et al., 2013). Although vaccine coolers are designed to keep vaccines cold, a poorly designed apparatus can result in accidental freezing of vaccines so that sub-potent vaccines can sometimes be administered (Chen, & Kristensen, 2009). A 2007 study found that in vaccine reports tracked longitudinally, 75-100% of vaccines were exposed to freezing temperatures; the authors recommend improved cold-chain transport equipment as a solution (Matthias, Robertson, Garrison, Newland, & Nelson, 2007). Vaccine freezing or overheating issues are not relegated solely to older studies or developing nations. The 2013-2014 H1N1pdm09 virus outbreak in the United States can likely be attributed to vaccine shipments being exposed to high temperatures (Caspard, Coelingh, Mallory, & Ambrose, 2016). The cost of most vaccines today ranges from $3.50-$7.50 per administration (Gates, 2012), so wastage results in a considerable economic loss. Importantly, when vaccines lose potency, there is a loss of confidence in vaccine therapy (Larson, Cooper, Eskola, Katz, & Ratzan, 2011). Thus, reducing vaccine wastage while increasing potency will provide more effective immunization in the rural, developing world at a reduced cost per dose. One way to address aspects of the wastage issue is the development of small coolers capable of transporting vaccines, maintained in the proper temperature range, from the regional health center to the distant client; this trip is termed the end stage of the cold chain. Coolers employing phase change materials including ice are capable of maintaining the desired temperature range for a period, but vaccines in such coolers are sometimes subject to overheating or freezing because of the lack of temperature regulation.
Generation of questions from an extract is a very tedious task for humans and an even tougher one for machines. In Automatic Question Generation (AQG), it is extremely important to examine the ways in which this can be achieved with sufficient levels of accuracy and efficiency. The way in which this can be taken ahead is by using Natural Language Processing (NLP) to process the input and to work with it for AQG. Using NLP with question generation algorithms the system can generate the questions for a better understanding of the text document. The input is pre-processed before actually moving in for the question generation process. The questions formed are first checked for proper context satisfaction with the context of the input to avoid invalid or unanswerable question generation. It is then preprocessed using various NLP-based mechanisms like tokenization, named entity recognition(NER) tagging, parts of speech(POS) tagging, etc. The question generation system consists of a machine learning classification-based Fill in the blank(FIB) generator that also generates multiple choices and a rule-based approach to generate Wh-type questions. It also consists of a question evaluator where the user can evaluate the generated question. The results of these evaluations can help in improving our system further. Also, evaluation of Wh questions has been done using the BLEU score to determine whether the automatically generated questions resemble closely the human-generated ones. This system can be used in various places to help ease the question generation and also at self-evaluator systems where the students can assess themselves so as to determine their conceptual understanding. Apart from educational use, it would also be helpful in building chatbot-based applications. This work can help improve the overall understanding of the level to which the concept given is understood by the candidate and the ways in which it can be understood more properly. We have taken a simple yet effective approach to generate the questions. Our evaluation results show that our model works well on simpler sentences.
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