Increasing online shop revenues with web scraping: a case study for the wine sector

Purpose – Wine has been produced for thousands of years and nowadays we have seen a spread in the wine culture. E-commerce sales of wine have increased considerably and online customer’s satisfaction is influenced by quality and price. This paper presents a case study of the company “QuieroVinos, S.L.”, an online wine shop founded in 2015 that sells Spanish wines in two main marketplaces. Design/methodology/approach – With the final target of increasing the company profits it has been designed and developed an application to track the prices of competitors for a set of products. This information will be used to set the product prices in order to offer the products both competitively and profitably in each Marketplace. This application must check, by tacking into account information such as the product cost or the minimum product margin, if it is possible to decrease the price in order to reach the top cheapest position and as a consequence, increase the sales. Findings – The application improved in a notorious way the company’s results in terms of sales and shipping costs. It must be said that without the use of the presented application, performing the price comparison process within each one of the marketplaces would have taken a long time. Moreover, as prices change very frequently, the obtained information has a very limited time value, and the competitors prices should be analyzed daily in order to take accurate decisions regarding the company’s price policy. Originality/value – Although the application has been designed for the wine sector and the two named marketplace, it could be exported to other sectors. For that, it should be implemented new modules to collect information regarding the competitor’s price of the products selling on each corresponding marketplaceThis work was supported by the Ministerio de Economía y Competitividad under contract TIN2017- 84553-C2-2-R. Also, the authors are members of the research group 2017-SGR363, funded by the Generalitat de Catalunya

Management of Cloud systems applied to eHealth

This thesis explores techniques, models and algorithms for an efficient management of Cloud systems and how to apply them to the healthcare sector in order to improve current treatments. It presents two Cloud-based eHealth applications to telemonitor and control smoke-quitting and hypertensive patients. Different Cloud-based models were obtained and used to develop a Cloudbased infrastructure where these applications are deployed. The results show that these applications improve current treatments and that can be scaled as computing requirements grow. Multiple Cloud architectures and models were analyzed and then implemented using different techniques and scenarios. The Smoking Patient Control (S-PC) tool was deployed and tested in a real environment, showing a 28.4% increase in long-term abstinence. The Hypertension Patient Control (H-PC) tool, was successfully designed and implemented, and the computing boundaries were measuredAquesta tesi explora tèniques, models i algorismes per una gestió eficient en sistemes al Núvol i com aplicar-ho en el sector de la salut per tal de millorar els tractaments actuals. Presenta dues aplicacions de salut electrònica basades en el Núvol per telemonitoritzar i controlar pacients fumadors i hipertensos. S'ha obtingut diferents models basats en el Núvol i s'han utilitzat per a desenvolupar una infraestructura on desplegar aquestes aplicacions. Els resultats mostren que aquestes aplicacions milloren els tractaments actuals així com escalen a mesura que els requeriments computacionals augmenten. Múltiples arquitectures i models han estat analitzats i implementats utilitzant diferents tècniques i escenaris. L'aplicació Smoking Patient Control (S-PC) ha estat desplegada i provada en un entorn real, aconseguint un augment del 28,4% en l'absistinència a llarg termini de pacients fumadors. L'aplicació Hypertension Patient Control (H-PC) ha estat dissenyada i implementada amb èxit, i els seus límits computacionals han estat mesurats.Esta tesis explora ténicas, modelos y algoritmos para una gestión eficiente de sistemas en la Nube y como aplicarlo en el sector de la salud con el fin de mejorar los tratamientos actuales. Presenta dos aplicaciones de salud electrónica basadas en la Nube para telemonitorizar y controlar pacientes fumadores e hipertensos. Se han obtenido diferentes modelos basados en la Nube y se han utilizado para desarrollar una infraestructura donde desplegar estas aplicaciones. Los resultados muestran que estas aplicaciones mejoran los tratamientos actuales así como escalan a medida que los requerimientos computacionales aumentan. Múltiples arquitecturas y modelos han sido analizados e implementados utilizando diferentes técnicas y escenarios. La aplicación Smoking Patient Control (S-PC) se ha desplegado y provado en un entorno real, consiguiendo un aumento del 28,4% en la abstinencia a largo plazo de pacientes fumadores. La aplicación Hypertension Patient Control (H-PC) ha sido diseñada e implementada con éxito, y sus límites computacionales han sido medidos

EMPOWERING, a smart Big Data framework for sustainable electricity suppliers

This paper presents the EMPOWERING project, a Big Data environment aimed at helping domestic customers to save electricity by managing their consumption positively. This is achieved by improving the information received about energy bills and offering online tools. The main contributions of EMPOWERING are the creation of a novel workflow in the electricity utility sector regarding the implementation of data analytics for their customers and the fast implementation of data-mining techniques in massive datasets within a Big Data platform to achieve scalability. The results obtained show that EMPOWERING can be of use for customers of electrical suppliers by changing their energy habits to decrease consumption and so increase environmental sustainability

Fuji-SfM dataset: A collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry

The present dataset contains colour images acquired in a commercial Fuji apple orchard (Malus domestica Borkh. cv. Fuji) to reconstruct the 3D model of 11 trees by using structure-from-motion (SfM) photogrammetry. The data provided in this article is related to the research article entitled “Fruit detection and 3D location using instance segmentation neural networks and structure-from-motion photogrammetry” [1]. The Fuji-SfM dataset includes: (1) a set of 288 colour images and the corresponding annotations (apples segmentation masks) for training instance segmentation neural networks such as Mask-RCNN; (2) a set of 582 images defining a motion sequence of the scene which was used to generate the 3D model of 11 Fuji apple trees containing 1455 apples by using SfM; (3) the 3D point cloud of the scanned scene with the corresponding apple positions ground truth in global coordinates. With that, this is the first dataset for fruit detection containing images acquired in a motion sequence to build the 3D model of the scanned trees with SfM and including the corresponding 2D and 3D apple location annotations. This data allows the development, training, and test of fruit detection algorithms either based on RGB images, on coloured point clouds or on the combination of both types of data. Dades primàries associades a l'article http://hdl.handle.net/10459.1/68505This work was partly funded by the Secretaria d'Universitats i Recerca del Departament d'Empresa i Coneixement de la Generalitat de Catalunya (grant 2017 SGR 646), the Spanish Ministry of Economy and Competitiveness (project AGL2013-48297-C2-2-R) and the Spanish Ministry of Science, Innovation and Universities (project RTI2018-094222-B-I00). Part of the work was also developed within the framework of the project TEC2016-75976-R, financed by the Spanish Ministry of Economy, Industry and Competitiveness and the European Regional Development Fund (ERDF). The Spanish Ministry of Education is thanked for Mr. J. Gené’s pre-doctoral fellowships (FPU15/03355)

Exploring the elusive composition of corpora amylacea of human brain

In the current global climate change scenario, stressors overlap in space and time, and knowledge on the effects of their interaction is highly needed to understand and predict the response and resilience of organisms. Corals, among many other benthic organisms, are affected by an increasing number of global change-related stressors including warming and invasive species. In this study, the cumulative effects between warming and invasive algae were experimentally assessed on the temperate reef-builder coral Cladocora caespitosa. We first investigated the potential local adaptation to thermal stress in two distant populations subjected to contrasting thermal and necrosis histories. No significant differences were found between populations. Colonies from both populations suffered no necrosis after long-term exposure to temperatures up to 29 °C. Second, we tested the effects of the interaction of both warming and the presence of invasive algae. The combined exposure triggered critical synergistic effects on photosynthetic efficiency and tissue necrosis. At the end of the experiment, over 90% of the colonies subjected to warming and invasive algae showed signs of necrosis. The results are of particular concern when considering the predicted increase of extreme climatic events and the spread of invasive species in the Mediterranean and other seas in the future

New perspectives on corpora amylacea in the human brain

Corpora amylacea are structures of unknown origin and function that appear with age in human brains and are profuse in selected brain areas in several neurodegenerative conditions. They are constituted of glucose polymers and may contain waste elements derived from different cell types. As we previously found on particular polyglucosan bodies in mouse brain, we report here that corpora amylacea present some neo-epitopes that can be recognized by natural antibodies, a certain kind of antibodies that are involved in tissue homeostasis. We hypothesize that corpora amylacea, and probably some other polyglucosan bodies, are waste containers in which deleterious or residual products are isolated to be later eliminated through the action of the innate immune system. In any case, the presence of neo-epitopes on these structures and the existence of natural antibodies directed against them could become a new focal point for the study of both age-related and degenerative brain processes