A Semantic Method to Information Extraction for Decision Support Systems

In this paper, we describe a novel schema for a more semantic text mining process which results in more comprehensive decision making activity by decision support systems via providing more effective and accurate textual information. The utility of two semantic lexical resources; Frame Net and Word Net, in extracting required text snippets from unstructured free texts yields a better and more accurate information extraction process to deliver more precise information either to a DSS or to a decision maker. We explain how the usage of these lexical resources could elevate a focused text mining process which could be applied to an information provider system in a decision support paradigm. The preliminary results obtained after a starter experiment show that the hybrid information extraction schema performs well on some semantic failure situations

Optimization of nitrogen and phosphorus removal from meat processing wastewaters using microalgal biofilms

Background: The conventional activated sludge processes are not able to completely treat the nitrogen and phosphorus compounds of meat processing wastewaters. Microalgal biofilm, which has the advantages of easiness and inexpensiveness, can be applied as a post-treatment method. Methods: The optimal levels of micronutrients and N/P ratio were aimed in the cultivation of Scenedesmus quadricauda as a biofilm formed on the two types of commercially available bed packing.For this purpose, the Factorial design was run to maximize the removal of ammonium, nitrate and phosphate concentration in the synthetic meat processing wastewater. Results: Experimental data showed better results for the removal of biofilm formed on the sponge filter. In addition, a maximum removal efficiency of 55.6%, 47% and 48.% was respectively achieved for ammonium, nitrate and phosphate at optimal N/P ratio of 10.5 and volumetric percentage of 50% of micronutrients. Conclusion: The results demonstrated that microalgal biofilm of Scenedesmus is able to remove the nutritional components of meat processing wastewaters. The process is needed to be optimized for the effective treatment. Keywords: Meat, Biofilms, Microalgae, Wastewater, Scenedesmu

Qualitative and Quantitative Study of Quercetin and Glycyrrhizin in In Vitro Culture of Liquorice (Glycyrrhiza glabra L.) and Elicitation with AgNO3

Liquorice (Glycyrrhiza glabra L.) is a plant that has been considered for a long time due to its valuable secondary metabolites. This study was conducted to obtain quercetin and glycyrrhizin under controlled conditions and the use of silver nitrate (AgNO3) as an elicitor to increase their production. For this purpose, the seeds were cultured in MS media containing various concentrations of AgNO3 (0, 2, 4, 8 and 10 mg L-1). Quercetin in the aerial parts extract of three-month seedlings prepared with methanol solvent 95% and acetic acid (9:1), and glycyrrhizin in the root extract of four-month seedlings prepared with ethanolic extract (30%) were evaluated qualitatively and quantitatively using HPLC. The results obtained from three replications showed the presence of quercetin and glycyrrhizin in all samples. The amount of quercetin in all samples treated with AgNO3 was significantly higher than control (P≤0.05) and this increase was higher at concentrations of 8 and 10 mg L-1 in comparison with other concentrations. Glycyrrhizin content increased under the influence of different concentrations of AgNO3 as compared to the control; however, this increase was not significant. Our results clearly showed that this method is a practical method to produce and elicit more these compounds with medicinal value

Enhancing factoid question answering using frame semantic-based approaches

FrameNet is used to enhance the performance of semantic QA systems. FrameNet is a linguistic resource that encapsulates Frame Semantics and provides scenario-based generalizations over lexical items that share similar semantic backgrounds.Doctor of Philosoph

The elimination race in track cycling : patterns and predictors of performance

The track cycling Omnium is a multi-event competition that has recently been expanded to include the Elimination Race (ER), which presents a unique set of physical and tactical demands. The purpose of this research was to characterise the performance attributes of successful and unsuccessful cyclists in the ER, that are also predictive of performance. Video recordings of four international level ERs were analysed. The performance attributes measured related to the cyclists’ velocity and two dimensional position in the peloton. The average velocity of the peloton up to lap 30 (of 50) was relatively high and consistent (52.2±1.5 km/h). After lap 30, there was a significant (p<0.001) change in velocity (49.9±2.4 km/h), characterised by more fluctuations in lap-to-lap velocity. Successful ER cyclists adopted a tactic of remaining in the middle of the peloton, in the lower lanes of the velodrome, thus avoiding the risk of elimination at the rear and the extra effort required to remain on the front of the peloton. Unsuccessful cyclists tended to reside in the rear and upper (higher) portions of the peloton, risking elimination more often and having to ride faster than those in the lower lanes of the velodrome. The physiological demands of the Elimination Race that are determined by velocity, vary throughout the Elimination Race and the pattern of movement within the peloton is different for successful and unsuccessful cyclists. The findings of the present study may confirm some aspects of race tactics that are currently thought to be optimal, but they also reveal novel information that is useful to coaches and cyclists who compete in the Elimination Race