Empirical study on the maintainability of Web applications: Model-driven Engineering vs Code-centric

Model-driven Engineering (MDE) approaches are often acknowledged to improve the maintainability of the resulting applications. However, there is a scarcity of empirical evidence that backs their claimed benefits and limitations with respect to code-centric approaches. The purpose of this paper is to compare the performance and satisfaction of junior software maintainers while executing maintainability tasks on Web applications with two different development approaches, one being OOH4RIA, a model-driven approach, and the other being a code-centric approach based on Visual Studio .NET and the Agile Unified Process. We have conducted a quasi-experiment with 27 graduated students from the University of Alicante. They were randomly divided into two groups, and each group was assigned to a different Web application on which they performed a set of maintainability tasks. The results show that maintaining Web applications with OOH4RIA clearly improves the performance of subjects. It also tips the satisfaction balance in favor of OOH4RIA, although not significantly. Model-driven development methods seem to improve both the developers’ objective performance and subjective opinions on ease of use of the method. This notwithstanding, further experimentation is needed to be able to generalize the results to different populations, methods, languages and tools, different domains and different application sizes.

Applying semantic knowledge to the automatic processing of temporal expressions and events in natural language

This paper addresses the problem of the automatic recognition and classification of temporal expressions and events in human language. Efficacy in these tasks is crucial if the broader task of temporal information processing is to be successfully performed. We analyze whether the application of semantic knowledge to these tasks improves the performance of current approaches. We therefore present and evaluate a data-driven approach as part of a system: TIPSem. Our approach uses lexical semantics and semantic roles as additional information to extend classical approaches which are principally based on morphosyntax. The results obtained for English show that semantic knowledge aids in temporal expression and event recognition, achieving an error reduction of 59% and 21%, while in classification the contribution is limited. From the analysis of the results it may be concluded that the application of semantic knowledge leads to more general models and aids in the recognition of temporal entities that are ambiguous at shallower language analysis levels. We also discovered that lexical semantics and semantic roles have complementary advantages, and that it is useful to combine them. Finally, we carried out the same analysis for Spanish. The results obtained show comparable advantages. This supports the hypothesis that applying the proposed semantic knowledge may be useful for different languages.

Electrocatalytic Performance of SiO2-SWCNT Nanocomposites Prepared by Electroassisted Deposition

Composite materials made of porous SiO2 matrices filled with single-walled carbon nanotubes (SWCNTs) were deposited on electrodes by an electroassisted deposition method. The synthesized materials were characterized by several techniques, showing that porous silica prevents the aggregation of SWCNT on the electrodes, as could be observed by transmission electron microscopy and Raman spectroscopy. Different redox probes were employed to test their electrochemical sensing properties. The silica layer allows the permeation of the redox probes to the electrode surface and improves the electrochemical reversibility indicating an electrocatalytic effect by the incorporation of dispersed SWCNT into the silica films.

Optimizing the performance of catalytic traps for hydrocarbon abatement during the cold-start of a gasoline engine

A key target to reduce current hydrocarbon emissions from vehicular exhaust is to improve their abatement under cold-start conditions. Herein, we demonstrate the potential of factorial analysis to design a highly efficient catalytic trap. The impact of the synthesis conditions on the preparation of copper-loaded ZSM-5 is clearly revealed by XRD, N2 sorption, FTIR, NH3-TPD, SEM and TEM. A high concentration of copper nitrate precursor in the synthesis improves the removal of hydrocarbons, providing both strong adsorption sites for hydrocarbon retention at low temperature and copper oxide nanoparticles for full hydrocarbon catalytic combustion at high temperature. The use of copper acetate precursor leads to a more homogeneous dispersion of copper oxide nanoparticles also providing enough catalytic sites for the total oxidation of hydrocarbons released from the adsorption sites, although lower copper loadings are achieved. Thus, synthesis conditions leading to high copper loadings jointly with highly dispersed copper oxide nanoparticles would result in an exceptional catalytic trap able to reach superior hydrocarbon abatement under highly demanding operational conditions.

A three-stage DEA model to evaluate learning-teaching technical efficiency: Key performance indicators and contextual variables

This study evaluates the technical efficiency of the learning-teaching process in higher education using a three-stage procedure that offers advances in comparison to previous studies and improves the quality of the results. First, it utilizes a multiple stage Data Envelopment Analysis (DEA) with contextual variables. Second, the levels of super efficiency are calculated in order to prioritize the efficiency units. And finally, through sensitivity analysis, the contribution of each key performance indicator (KPI) is established with respect to the efficiency levels without omission of variables. The analytical data was collected from a survey completed by 633 tourism students during the 2011/12, 2012/13 and 2013/14 academic course years. The results suggest that level of satisfaction with the course, diversity of materials and satisfaction with the teacher were the most important factors affecting teaching performance. Furthermore, the effect of the contextual variables was found to be significant.