Enzyme production from food wastes using a biorefinery concept

According to Food and Agricultural Organization (FAO), one-third of food produced globally for human consumption (nearly 1.3 billion tonnes) is lost along the food supply chain. In many countries food waste is currently landfilled or incinerated together with other combustible municipal wastes for possible recovery of energy. However, these two options are facing more and more economic and environmental stresses. Due to its organic- and nutrient-rich nature, theoretically food waste can be converted to valuable products (e.g. bio-products such as methane, hydrogen, ethanol, enzymes, organic acids, chemicals and fuels) through various fermentation processes. Such conversion of food waste is potentially more profitable than its conversion to animal feed or transportation fuel. Food waste valorisation has therefore gained interest, with value added bio-products such as methane, hydrogen, ethanol, enzymes, organic acids, chemicals, and fuels. Therefore, the aim of this review is to provide information on the food waste situation with emphasis on Asia–Pacific countries and the state of the art food waste processing technologies to produce enzymes

A new minimum description length based pruning technique for rule induction algorithms

When learning is based on noisy data, the induced rule sets have a tendency to overfit the training data, and this degrades the performance of the resulting classifier. Consequently, the ability to tolerate noise is a necessity for robust, practical learning methods. Pruning is a common way of handling noisy data. This paper presents a new pruning technique built on the sound foundation of the minimum description length principle. The proposed pruning technique has the advantage that it does not require the set of examples employed for pruning to be distinct from the set used to build the rule set. The new technique is designed to improve the performance of the RULe Extraction System (RULES) family of inductive learning algorithms, but can be used for pruning rule sets created by other learning algorithms. It was tested in RULES-6, the latest algorithm in the family, and showed significant performance improvements

Correlation of psychomotor skills and didactic performance among dental students in Saudi Arabia

Ahmed R Afify,1 Khalid H Zawawi,1 Hisham I Othman,2 Ayman A Al-Dharrab31Department of Preventive Dental Sciences, 2Department of Basic Oral and Clinical Sciences, 3Department of Oral and Maxillofacial Rehabilitation, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi ArabiaObjectives: The objective of this study is to investigate the correlation between the psychomotor skills and the academic performance of dental students.Methods: Didactic and preclinical scores were collected for students who graduated from the Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia, in 2011. Three courses (Dental Anatomy, Removable Prosthodontic Denture, and Orthodontics) were selected. Correlations comparing didactic and practical scores were done for the total samples, then for the males and females separately.Results: There was no significant correlation between the practical and didactic scores for the three courses for the total sample. There was a significant correlation between all three subjects in the didactic scores. For females, the results showed that there was only a significant correlation between the practical and didactic scores for Dental Anatomy. For males, no correlation was observed between the practical and didactic scores for all subjects.Conclusion: In the present sample, didactic performance did not correlate well with the students' psychomotor performance.Keywords: psychomotor performance, didactic performance, dental students, correlation study, dental education, practical performanc

A Study of Heuristic Evaluation Measures in Fuzzy Rule Induction

Part 12: FuzzyInternational audienceThe rule induction process could be conceived as a search process, and hence an evaluation metric is needed to estimate the quality of rules found in the search space and to direct the search towards the best rule. The evaluation measure is the most influential inductive bias in rule learning. It is therefore important to investigate its influence on the induction process and to compare the behaviour of different evaluation measures. Many different evaluation measures have been used to score crisp rules. For some of these measures, fuzzy variations have been designed and used to score fuzzy rules. This paper examines the most popular crisp evaluation measures and demonstrates how they can be adapted into the fuzzy domain. The paper also studies the performance of these measures on a large number of data sets when used in a recently developed fuzzy rule induction algorithm. Results show that there are no universally applicable evaluation measures and the choice of the best measure depends on the type of the data set and the learning problem