Towards a journalist-based news recommendation system: The Wesomender approach

The present paper introduces a context-aware recommendation system for journalists to enable the identification of similar topics across different sources. More specifically a journalist-based recommendation system that can be automatically configured is presented to exploit news according to expert preferences. News contextual features are also taken into account due to the their special nature: time, current user interests, location or existing trends are combined with traditional recommendation techniques to provide an adaptive framework that deals with heterogeneous data providing an enhanced collaborative filtering system. Since the Wesomender approach is able to generate context-aware recommendations in the journalism field, a quantitative evaluation with the aim of comparing Wesomender results with the expectations of a team of experts is also performed to show that a context-aware adaptive recommendation engine can fulfil the needs of journalists daily work when retrieving timely and primary information is required.     

The roles of textural images in improving land-cover classification in the Brazilian Amazon

Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l’Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving land-cover classification. The classification accuracy can be improved by 5.2–13.4% as the pixel size changes from 30 to 0.6 m.     

Hydroelastic optimization of a keel fin of a sailing boat: a multidisciplinary robust formulation for ship design

The paper presents a formulation for multidisciplinary design optimization of vessels, subject to uncertain operating conditions. The formulation couples the multidisciplinary design analysis with the Bayesian approach to decision problems affected by uncertainty. In the present context, the design specifications are no longer given in terms of a single operating design point, but in terms of probability density function of the operating scenario. The optimal configuration is that which maximizes the performance expectation over the uncertain parameters variation. In this sense, the optimal solution is “robust” within the stochastic scenario assumed. Theoretical and numerical issues are addressed and numerical results in the hydroelastic optimization of a keel fin of a sailing yacht are presented.     

Visual Zero: A persistent and interactive object-oriented programming environment

In this article, an ongoing research project held in the Computer Science department of the University of Vigo is described. Its main objective is to develop an interactive object-oriented environment, serving as a vehicle for learning object-oriented programming. It currently consists of a virtual machine, an assembler, compilers for two high-level programming languages, and an educational visual programming environment. Its main characteristics are (a) the support of prototype-based object orientation, which is a model of object orientation that actually wraps the class-based model; (b) the support for object persistence, which simplifies all input/output issues to the minimum, and (c) the availability of a new visual programming environment is an invaluable help. We the authors think that the combination of all of these possibilities, will lead to an interesting, useful tool that would be recommended for object-oriented teaching. The whole system has been successfully employed in a number of different courses, allowing students to concentrate on objects and their relationships from the very beginning, and thus helping them to achieve a high degree of knowledge about the object-oriented programming paradigm.     

Isotropic Image Analysis for Improving CBR Forecasting

A novel hybrid forecasting Case-Based Reasoning (CBR) system is presented in this interdisciplinary study in which an isotropic buffer operator is applied for case-based creation. Commonly used as an image analysis technique by commercial Geographic Information Systems (GIS), the buffer operator in this particular system calculates the area of an oil slick for prediction and visualization tasks. The use of the buffer operator improves the quality of the data used by the system and in consequence the quality of the results obtained. The system generates predictions by using historical data on oil-slick formation following a spill.     

Novel Esterification of Glycerol Catalysed by Tin Chloride (II): A Recyclable and Less Corrosive Process for Production of Bio-Additives

Abstract  

In this work, a novel process based on use of a SnCl₂·2H₂O catalyst which is less corrosive, inexpensive, and a water tolerant Lewis acid was employed for synthesis of fuel bio-additives from glycerol. High yields and selectivities were achieved for glycerol esterification with acetic acid under mild reaction conditions. The SnCl₂ catalyst showed to be as active as p-toluene sulfonic and sulfuric acid, catalysts commonly used in acid-catalysed esterification reactions. However, its use has significant advantages in comparison to these Br?nsted acid catalysts, including lower reactor corrosion and unnecessary neutralization at the end reaction. The SnCl₂ catalyst can also be recovered and reused without loss of catalytic activity. Additionally, effects of reaction temperature, HOAc:glycerol molar ratio and catalyst concentration on both selectivity and yield of glycerol acetates were also investigated. The lower corrosiveness, facilitated handling, as well as potential for reuse without activity loss after simple recycle protocols are positive aspects of SnCl₂ catalysts.