Time filter

Source Type

Quinta do Anjo, Portugal

Morales J.,University of Vigo | Manso J.A.,University of Vigo | Cid A.,University of Vigo | Lodeiro C.,University of Vigo | Mejuto J.C.,CITI
Journal of Colloid and Interface Science

The effect of sodium bis(2-ethylhexyl)sulfosuccinate/isooctane/water microemulsions on the stability of 2,2-dimethyl-2,3-dihydro-1-benzofuran-7-yl methylcarbamate (carbofuran, CF), 3-hydroxy-2,3-dihydro-2,2-dimethylbenzofuran-7-yl methylcarbamate (3-hydroxycarbofuran, HCF) and 3-keto-2,3-dihydro-2,2-dimethylbenzofuran-7-yl methylcarbamate (3-ketocarbofuran, KCF) in basic media has been studied. The presence of these microheterogeneous media implies a large basic hydrolysis of CF and HCF on increasing surfactant concentration and, also, on increasing water content in the microemulsion. The hydrolysis rate constants are approximately 2- and 10-fold higher than those in pure water for HCF and CF, respectively. In contrast, a steep descent in the rate of decomposition for KCF was observed. These behaviours can be ascribed to the presence of CF derivatives both in the hydrophilic phase and in the lipophilic phase, while the hydroxyl ions are only restricted to the water pool of the microemulsion (hydrophilic phase). The kinetic rate constants for the basic hydrolysis in AOT-based microemulsions have been obtained on the basis of a pseudophase model. Taking into account that an important part of soils are colloids, the possibility of the presence of restricted water environments implies that soil composition and its structure will play an important role in the stability of these carbamates. In fact, we observed that the presence of these restricted aqueous media in the environment, in particular in watersheds and in wastewaters, could reduce significantly the half-life of these pesticides (33% and 91% for HCF and CF, respectively). © 2012 Elsevier Inc.. Source

Goulao M.,New University of Lisbon | Fonte N.,New University of Lisbon | Wermelinger M.,Open University Milton Keynes | Abreu F.B.,CITI
Proceedings of the European Conference on Software Maintenance and Reengineering, CSMR

Prediction models of software change requests are useful for supporting rational and timely resource allocation to the evolution process. In this paper we use a time series forecasting model to predict software maintenance and evolution requests in an open source software project (Eclipse), as an example of projects with seasonal release cycles. We build an ARIMA model based on data collected from Eclipse's change request tracking system since the project's start. A change request may refer to defects found in the software, but also to suggested improvements in the system under scrutiny. Our model includes the identification of seasonal patterns and tendencies, and is validated through the forecast of the change requests evolution for the next 12 months. The usage of seasonal information significantly improves the estimation ability of this model, when compared to other ARIMA models found in the literature, and does so for a much longer estimation period. Being able to accurately forecast the change requests' evolution over a fairly long time period is an important ability for enabling adequate process control in maintenance activities, and facilitates effort estimation and timely resources allocation. The approach presented in this paper is suitable for projects with a relatively long history, as the model building process relies on historic data. © 2012 IEEE. Source

Realinho V.,Escola Superior de Tecnologia e Gestao de Portalegre | Romao T.,CITI | Birra F.,CITI | Dias A.E.,CITI
ACM International Conference Proceeding Series

This paper presents a mobile context-aware tourist guide application created with the IVO platform. IVO (Integrated Virtual Operator) enables end-users to quickly build and deploy context-aware applications without the need to write any programming code, and using smartphones as the ubiquitous interaction device. Aiming at exploring the use of the IVO platform to build mobile leisure and entertainment applications, the developed application makes use of most features available in IVO. Using only the tools provided by the platform, IVO Builder and IVO Outlook, users can define temporal and spatial conditions and associate them with workflows of available activities. The paper also presents the user studies performed to evaluate the application's usefulness and usability. Copyright 2011 ACM. Source

Realinho V.,Escola Superior de Tecnologia e Gestao de Portalegre | Romao T.,CITI | Birra F.,CITI | Dias A.E.,CITI
ACM International Conference Proceeding Series

IVO (Integrated Virtual Operator) is a platform to build and deploy context-aware applications using mobile devices, like smartphones and tablets, as the ubiquitous interaction device. It is designed to enable non-programmers to create and run new mobile applications. Applications are built from workflows of activities available in the platform. These workflows are triggered automatically by the device, when their temporal and spatial conditions are satisfied and with no user intervention. The applications are developed by end-users in a distributed web platform. Copyright 2011 ACM. Source

Del Bello S.,CITI | Hawkey J.,CADA | Oliveira S.,CADA | Perriquet O.,CADA | Correia N.,CITI
Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering

The paper presents contributions in the area of location data processing for pattern discovery. This work forms part of a project which explores an ambient intelligence application designed to present individual users with an overview of their time usage patterns. The application uses location data to build interfaces and visualizations which highlight changes in personal routines, with the aim of stimulating reflection. Data is processed to extract significant places and temporal information about them. The paper presents the questions that can be answered by a data processing layer and the strategy to handle the different types of queries. Location data is processed to identify significant locations, discover patterns and predict future behavior. © 2011 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering. Source

Discover hidden collaborations