A non-parametric data envelopment analysis approach for improving energy efficiency of grape production

Grape is one of the world's largest fruit crops with approximately 67.5 million tonnes produced each year and energy is an important element in modern grape productions as it heavily depends on fossil and other energy resources. Efficient use of these energies is a necessary step toward reducing environmental hazards, preventing destruction of natural resources and ensuring agricultural sustainability. Hence, identifying excessive use of energy as well as reducing energy resources is the main focus of this paper to optimize energy consumption in grape production.In this study we use a two-stage methodology to find the association of energy efficiency and performance explained by farmers' specific characteristics. In the first stage a non-parametric Data Envelopment Analysis is used to model efficiencies as an explicit function of human labor, machinery, chemicals, FYM (farmyard manure), diesel fuel, electricity and water for irrigation energies. In the second step, farm specific variables such as farmers' age, gender, level of education and agricultural experience are used in a Tobit regression framework to explain how these factors influence efficiency of grape farming.The result of the first stage shows substantial inefficiency between the grape producers in the studied area while the second stage shows that the main difference between efficient and inefficient farmers was in the use of chemicals, diesel fuel and water for irrigation. The use of chemicals such as insecticides, herbicides and fungicides were considerably less than inefficient ones. The results revealed that the more educated farmers are more energy efficient in comparison with their less educated counterparts. © 2013.

Low level of cross-resistance between triclosan and antibiotics in Escherichia coli K-12 and E. coli O55 compared to E-coli O157

Misuse of biocides has encouraged the emergence of resistance and cross-resistance in certain strains. This study investigated resistance of triclosan-adapted Escherichia coli K-12 and E. coli O55 to antimicrobial agents and compared these to E. coli O157:H7. Cross-resistance in E. coli K-12 and E. coli O55 was observed however to a lesser extent than in E. coli O157:H7. Triclosan-adapted E. coli K-12 demonstrated cross-resistance to chloramphenicol, whereas triclosan-adapted E. coli O55 exhibited resistance to trimethoprim. In comparison, E. coli O157:H7 was resistant to chloramphenicol, tetracycline, amoxicillin, amoxicillin/clavulanic acid, trimethoprim, benzalkonium chloride and chlorohexidine suggesting strain specific rather than general resistance mechanisms. © 2004 Federation of European Microbiological Societies. Published by Elsevier B.V. All rights reserved.

Handling high level of censoring for endovascular aortic repair risk prediction

Feature selection is important in medical field for many reasons. However, selecting important variables is a difficult task with the presence of censoring that is a unique feature in survival data analysis. This paper proposed an approach to deal with the censoring problem in endovascular aortic repair survival data through Bayesian networks. It was merged and embedded with a hybrid feature selection process that combines cox's univariate analysis with machine learning approaches such as ensemble artificial neural networks to select the most relevant predictive variables. The proposed algorithm was compared with common survival variable selection approaches such as; least absolute shrinkage and selection operator LASSO, and Akaike information criterion AIC methods. The results showed that it was capable of dealing with high censoring in the datasets. Moreover, ensemble classifiers increased the area under the roc curves of the two datasets collected from two centers located in United Kingdom separately. Furthermore, ensembles constructed with center 1 enhanced the concordance index of center 2 prediction compared to the model built with a single network. Although the size of the final reduced model using the neural networks and its ensembles is greater than other methods, the model outperformed the others in both concordance index and sensitivity for center 2 prediction. This indicates the reduced model is more powerful for cross center prediction.