Management Driven Hybrid Multicast Framework for Content Aware Networks

The need for better adaptation of networks to transported flows has led to research on new approaches such as content aware networks and network aware applications. In parallel, recent developments of multimedia and content oriented services and applications such as IPTV, video streaming, video on demand, and Internet TV reinforced interest in multicast technologies. IP multicast has not been widely deployed due to interdomain and QoS support problems; therefore, alternative solutions have been investigated. This article proposes a management driven hybrid multicast solution that is multi-domain and media oriented, and combines overlay multicast, IP multicast, and P2P. The architecture is developed in a content aware network and network aware application environment, based on light network virtualization. The multicast trees can be seen as parallel virtual content aware networks, spanning a single or multiple IP domains, customized to the type of content to be transported while fulfilling the quality of service requirements of the service provider.

Immunohistochemical molecular phenotypes of gastric cancer based on SOX2 and CDX2 predict patient outcome

Background Gastric cancer remains a serious health concern worldwide. Patients would greatly benefit from the discovery of new biomarkers that predict outcome more accurately and allow better treatment and follow-up decisions. Here, we used a retrospective, observational study to assess the expression and prognostic value of the transcription factors SOX2 and CDX2 in gastric cancer. Methods SOX2, CDX2, MUC5AC and MUC2 expression were assessed in 201 gastric tumors by immunohistochemistry. SOX2 and CDX2 expression were crossed with clinicopathological and follow-up data to determine their impact on tumor behavior and outcome. Moreover, SOX2 locus copy number status was assessed by FISH (N = 21) and Copy Number Variation Assay (N = 62). Results SOX2 was expressed in 52% of the gastric tumors and was significantly associated with male gender, T stage and N stage. Moreover, SOX2 expression predicted poorer patient survival, and the combination with CDX2 defined two molecular phenotypes, SOX2+CDX2- versus SOX2-CDX2+, that predict the worst and the best long-term patients’ outcome. These profiles combined with clinicopathological parameters stratify the prognosis of patients with intestinal and expanding tumors and in those without signs of venous invasion. Finally, SOX2 locus copy number gains were found in 93% of the samples reaching the amplification threshold in 14% and significantly associating with protein expression. Conclusions We showed, for the first time, that SOX2 combined with CDX2 expression profile in gastric cancer segregate patients into different prognostic groups, complementing the clinicopathological information. We further demonstrate a molecular mechanism for SOX2 expression in a subset of gastric cancer cases.

An Approach To Publish a Data Warehouse Content as Linked Data

Mestrado em Engenharia Informática - Área de Especialização em Tecnologias do Conhecimento e Decisão

Media Brokerage: Agent-Based SLA Negotiation

Media content personalisation is a major challenge involving viewers as well as media content producer and distributor businesses. The goal is to provide viewers with media items aligned with their interests. Producers and distributors engage in item negotiations to establish the corresponding service level agreements (SLA). In order to address automated partner lookup and item SLA negotiation, this paper proposes the MultiMedia Brokerage (MMB) platform, which is a multiagent system that negotiates SLA regarding media items on behalf of media content producer and distributor businesses. The MMB platform is structured in four service layers: interface, agreement management, business modelling and market. In this context, there are: (i) brokerage SLA (bSLA), which are established between individual businesses and the platform regarding the provision of brokerage services; and (ii) item SLA (iSLA), which are established between producer and distributor businesses about the provision of media items. In particular, this paper describes the negotiation, establishment and enforcement of bSLA and iSLA, which occurs at the agreement and negotiation layers, respectively. The platform adopts a pay-per-use business model where the bSLA define the general conditions that apply to the related iSLA. To illustrate this process, we present a case study describing the negotiation of a bSLA instance and several related iSLA instances. The latter correspond to the negotiation of the Electronic Program Guide (EPG) for a specific end viewer.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Human capital in the knowledge-based society: comparative analysis: Romania-Portugal

The current economic crisis has rushed even more the economists’ concerns to identify new directions for the sustainable development of the society. In this context, the human capital is crystallised as the key variable of the creative economy and of the knowledge-based society. As such, we have directed the research underlying this paper to identifying the most eloquent indicators of human capital to meet the demands of the knowledge-based society and sustainable development as well as towards achieving a comprehensive analysis of the human capital in the EU countries, respectively of a comparative analysis: Romania - Portugal. To carry out this paper, the methodology used is based on the interdisciplinary triangulation involving approaches from the perspective of human resource management, economy and economic statistics. The research techniques used consist of the content analysis and investigation of secondary data of international organisations accredited in the field of this research, such as: the United Nation Development Programme - Human Development Reports, World Bank - World Development Reports, International Labour Organisation, Eurostat, European Commission’s Eurobarometer surveys and reports on human capital. The research results emphasise both similarities and differences between the two countries under the comparative analysis and the main directions in which one has to invest for the development of human capital.

Development of 2-oxazoline-based hydrogels and porous polyester microparticles in scCO2

Thesis submitted to Faculdade de Ciências e Tecnologia from Universidade Nova de Lisboa in partial fulfillment of the requirements for the obtention of the degree of Master of Science in Biotechnology

Protein–polyphenol interaction on silica beads for astringency tests based on eye, photography or reflectance detection modes

Astringency is an organoleptic property of beverages and food products resulting mainly from the interaction of salivary proteins with dietary polyphenols. It is of great importance to consumers, but the only effective way of measuring it involves trained sensorial panellists, providing subjective and expensive responses. Concurrent chemical evaluations try to screen food astringency, by means of polyphenol and protein precipitation procedures, but these are far from the real human astringency sensation where not all polyphenol–protein interactions lead to the occurrence of precipitate. Here, a novel chemical approach that tries to mimic protein–polyphenol interactions in the mouth is presented to evaluate astringency. A protein, acting as a salivary protein, is attached to a solid support to which the polyphenol binds (just as happens when drinking wine), with subsequent colour alteration that is fully independent from the occurrence of precipitate. Employing this simple concept, Bovine Serum Albumin (BSA) was selected as the model salivary protein and used to cover the surface of silica beads. Tannic Acid (TA), employed as the model polyphenol, was allowed to interact with the BSA on the silica support and its adsorption to the protein was detected by reaction with Fe(III) and subsequent colour development. Quantitative data of TA in the samples were extracted by colorimetric or reflectance studies over the solid materials. The analysis was done by taking a regular picture with a digital camera, opening the image file in common software and extracting the colour coordinates from HSL (Hue, Saturation, Lightness) and RGB (Red, Green, Blue) colour model systems; linear ranges were observed from 10.6 to 106.0 μmol L−1. The latter was based on the Kubelka–Munk response, showing a linear gain with concentrations from 0.3 to 10.5 μmol L−1. In either of these two approaches, semi-quantitative estimation of TA was enabled by direct eye comparison. The correlation between the levels of adsorbed TA and the astringency of beverages was tested by using the assay to check the astringency of wines and comparing these to the response of sensorial panellists. Results of the two methods correlated well. The proposed sensor has significant potential as a robust tool for the quantitative/semi-quantitative evaluation of astringency in wine.

A framework for supporting knowledge representation – an ontological based approach

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Kalman tracking linear predictor for vowel intelligibility enhancement on european portuguese HMM based speech synthesis

The recent developments on Hidden Markov Models (HMM) based speech synthesis showed that this is a promising technology fully capable of competing with other established techniques. However some issues still lack a solution. Several authors report an over-smoothing phenomenon on both time and frequencies which decreases naturalness and sometimes intelligibility. In this work we present a new vowel intelligibility enhancement algorithm that uses a discrete Kalman filter (DKF) for tracking frame based parameters. The inter-frame correlations are modelled by an autoregressive structure which provides an underlying time frame dependency and can improve time-frequency resolution. The system’s performance has been evaluated using objective and subjective tests and the proposed methodology has led to improved results.

Gesture based interface for image annotation

Dissertação apresentada para obtenção do Grau de Mestre em Engenharia Informática pela Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia

Edible coatings based on chitosan-beeswax emulsions

Dissertação para obtenção do Grau de Mestre em Engenharia Química e Bioquímica

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Location-based Marketing: the academic framework

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.