941 resultados para SUPPORT GROUPS


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This article reports the HPV status and cervical cytological abnormalities in patients attended at public and private gynecological services from Rio de Janeiro State. It also comments the performance of each HPV DNA tests used. A set of 454 women from private health clinics was tested by routine Capture Hybrid II HPV DNA assay. Among these, 58.4% presented HPV and nearly 90% of them were infected by high risk HPV types. However, this group presented few premalignant cervical lesions and no invasive cervical cancer was registered. We also studied 220 women from low income class attended at public health system. They were HPV tested by polymerase chain reaction using My09/11 primers followed by HPV typing with E6 specific primers. The overall HPV prevalence was 77.3%. They also showed a high percentage of high squamous intraepithelial lesion-HSIL (26.3%), and invasive cervical carcinoma (16.3%). HPV infection was found in 93.1% and 94.4% of them, respectively. The mean ages in both groups were 31.5 and 38 years, respectively. In series 1, HPV prevalence declined with age, data consistent with viral transient infection. In series 2, HPV prevalence did not decline, independent of age interval, supporting not only the idea of viral persistence into this group, but also regional epidemiological variations in the same geographic area. Significant cytological differences were seen between both groups. Normal and benign cases were the most prevalent cytological findings in series 1 while pre-malignant lesions were the most common diagnosis in the series 2. HPV prevalence in normal cases were statistically higher than those from series 1 (p < 0.001), indicating a higher exposure to HPV infection. Women from both samples were referred for previous abnormal cytology. However, socio-demographic evidence shows that women from series 1 have access to treatment more easily and faster than women from series 2 before the development of pre-malignant lesions. These data provides baseline support for the role of social inequalities linked to high risk HPV infection leading to cervical cancer. Broadly screening programs and the development of safe and effective vaccines against HPV would diminish the toll of this disease that affect mainly poor women.

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Presented at INForum - Simpósio de Informática (INFORUM 2015). 7 to 8, Sep, 2015. Covilhã, Portugal.

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Presented at Work in Progress Session, IEEE Real-Time Systems Symposium (RTSS 2015). 1 to 3, Dec, 2015. San Antonio, U.S.A..

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Presented at Work in Progress Session, IEEE Real-Time Systems Symposium (RTSS 2015). 1 to 3, Dec, 2015. San Antonio, U.S.A..

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Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.

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This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

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The energy sector has suffered a significant restructuring that has increased the complexity in electricity market players' interactions. The complexity that these changes brought requires the creation of decision support tools to facilitate the study and understanding of these markets. The Multiagent Simulator of Competitive Electricity Markets (MASCEM) arose in this context, providing a simulation framework for deregulated electricity markets. The Adaptive Learning strategic Bidding System (ALBidS) is a multiagent system created to provide decision support to market negotiating players. Fully integrated with MASCEM, ALBidS considers several different strategic methodologies based on highly distinct approaches. Six Thinking Hats (STH) is a powerful technique used to look at decisions from different perspectives, forcing the thinker to move outside its usual way of thinking. This paper aims to complement the ALBidS strategies by combining them and taking advantage of their different perspectives through the use of the STH group decision technique. The combination of ALBidS' strategies is performed through the application of a genetic algorithm, resulting in an evolutionary learning approach.

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Health promotion in hospital environments can be improved using the most recent information and communication technologies. The Internet connectivity to small sensor nodes carried by patients allows remote access to their bio-signals. To promote these features the healthcare wireless sensor networks (HWSN) are used. In these networks mobility support is a key issue in order to keep patients under realtime monitoring even when they move around. To keep sensors connected to the network, they should change their access points of attachment when patients move to a new coverage area along an infirmary. This process, called handover, is responsible for continuous network connectivity to the sensors. This paper presents a detailed performance evaluation study considering three handover mechanisms for healthcare scenarios (Hand4MAC, RSSI-based, and Backbone-based). The study was performed by simulation using several scenarios with different number of sensors and different moving velocities of sensor nodes. The results show that Hand4MAC is the best solution to guarantee almost continuous connectivity to sensor nodes with less energy consumption.

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Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental Engineering

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Dissertation to obtain the Master degree in Electrical Engineering and Computer Science

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Objective: To study the influence of prolonged wearing of unstable shoes on standing postural control in prolonged standing workers. Methods: The participants were divided into two groups: one wore unstable shoes while the other wore conventional shoes for 8 weeks. Stabilometry parameters related to centre of pressure (CoP), rambling (RM) and trembling (TR) as well as the total agonist/antagonist muscle activity, antagonist co-activation and reciprocal activation were evaluated during upright standing, before and after the 8 week period. In both moments, the subjects were evaluated wearing the unstable shoes and in barefoot. Results: The unstable shoe condition presented increased CoP displacement related variables and decreased co-activation command compared to barefoot before and after the intervention. The prolonged wearing of unstable shoes led to: (1) reduction of medial-lateral CoP root mean square and area; (2) decreased anteroposterior RM displacement; (3) increased anteroposterior RM mean velocity and mediolateral RM displacement; (4) decreased anteroposterior TR RMS; and (5) increased thigh antagonist co-activation in the unstable shoe condition. Conclusion: The unstable shoe condition is associated to a higher destabilizing effect that leads to a selection of more efficient and accurate postural commands compared to barefoot. Prolonged wearing of unstable shoes provides increased effectiveness and performance of the postural control system, while wearing of unstable shoes in upright standing, that are reflected by changes in CoP related variables and by a reorganization of postural control commands.

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Introduction: Lesions at ipsilateral systems related to postural control at ipsilesional side, may justify the lower performance of stroke subjects during walking. Purpose: To analyse bilateral ankle antagonist coactivation during double-support in stroke subjects. Methods: Sixteen (8 females; 8 males) subjects with a first isquemic stroke, and twenty two controls (12 females; 10 males) participated in this study. The double support phase was assessed through ground reaction forces and electromyography of ankle muscles was assessed in both limbs. Results: Ipsilesional limb presented statistical significant differences from control when assuming specific roles during double support, being the tibialis anterior and soleus pair the one in which this atypical behavior was more pronounced. Conclusion: The ipsilesional limb presents a dysfunctional behavior when a higher postural control activity was demanded.

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Real-time monitoring applications may be used in a wireless sensor network (WSN) and may generate packet flows with strict quality of service requirements in terms of delay, jitter, or packet loss. When strict delays are imposed from source to destination, the packets must be delivered at the destination within an end-to-end delay (EED) hard limit in order to be considered useful. Since the WSN nodes are scarce both in processing and energy resources, it is desirable that they only transport useful data, as this contributes to enhance the overall network performance and to improve energy efficiency. In this paper, we propose a novel cross-layer admission control (CLAC) mechanism to enhance the network performance and increase energy efficiency of a WSN, by avoiding the transmission of potentially useless packets. The CLAC mechanism uses an estimation technique to preview packets EED, and decides to forward a packet only if it is expected to meet the EED deadline defined by the application, dropping it otherwise. The results obtained show that CLAC enhances the network performance by increasing the useful packet delivery ratio in high network loads and improves the energy efficiency in every network load.

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In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.