No abstract

Garden biomass (GB) is defined as low density and heterogeneous waste fraction of garden rubbish like grass clippings, pruning, flowers, branches, weeds; roots. GB is generally different from other types of biomass. GB is mostly generated through maintenance of green areas. GB can be processed for bio energy production as it contains considerably good amount of cellulose and hemicellulose. However, pretreatment is necessary to delignify and facilitate disruption of cellulosic moiety. The aim of the present investigation was to pretreat GB using Fenton’s reagent and to study the influence of Fe2+ and H2O2 concentrations on degradation of lignin and cellulose. The data were statistically analyzed using ANOVA and numerical point prediction tool of MINITAB RELEASE 14 to optimize different process variables such as temperature, concentration of Fe2+ and H2O2. The results of the present investigation showed that Fenton’s reagent was effective on GB, however, concentration of Fe2+ and H2O2 play crucial role in determining the efficiency of pretreatment. An increase in H2O2 concentration in Fenton’s reagent significantly increased the rate of cellulose and lignin degradation in contrast to increasing concentration of Fe2+ ion which led to a decrease in lignocellulosic degradation.

Data Mining is a field in which hidden information is extracted from a large database by using some algorithms implementation. These algorithms are further divided into some categories like classification, clustering, association rule mining etc according to information we want to extract. Data mining is a field which is widely spread over different areas like telecommunication, marketing, operation, hospitals, hotel industry, education etc. Predicting the academic’s performance and progress of the students has revealed the attention of the young researchers. To facilitate the task of building an academic prediction model, historical student academic dataset is used. In this paper, the contributions are exhibited in two different folds. In the first fold, the main aim is to build the prediction model by different families of the Machine Learning Techniques on the selected dataset for consideration. In the second fold, implementations of different ensemble meta-based model are presented by combining with different classification algorithms of Machine Learning Techniques. Different ensemble meta-based model taken into consideration for implementation are Bagging, AdaBoostM1, RandomSubSpace. The implementation results demonstrate that the ensemble meta-based technique (AdaBoostM1) gained a superior accuracy performance with MultilayerPerceptron Machine Learning technique reaching up to 80.33%.

Despite technological progresses and improved understanding of biological systems, discovery of novel drugs is an inefficient, arduous and expensive process. Research and development cost of drugs is unreasonably high, largely attributed to the high attrition rate of candidate drugs due to adverse drug reactions. Computational methods for accurate prediction of drug side effects, rooted in empirical data of drugs, have the potential to enhance the efficacy of the drug discovery process. Identification of features critical for specifying side effects would facilitate efficient computational procedures for their prediction. We devised a generalized ordinary canonical correlation model for prediction of drug side effects based on their chemical properties as well as their target profiles. While the former is based on 2D and 3D chemical features, the latter enumerates a systems-level property of drugs. We find that the model incorporating chemical features outperforms that incorporating target profiles. Furthermore we identified the 2D and 3D chemical properties that yield best results, thereby implying their relevance in specifying adverse drug reactions.