804 resultados para Data mining and knowledge discovery


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In a cross-sectional study of 400 randomly selected smallholder dairy farms in the Tanga and Iringa regions of Tanzania, 14.2% (95% confidence interval (CI) = 11.6-17.3) of cows had developed clinical mastitis during the previous year. The point prevalence of subclinical mastitis, defined as a quarter positive by the California Mastitis Test (CMT) or by bacteriological culture, was 46.2% (95% Cl = 43.6-48.8) and 24.3% (95% Cl = 22.2-26.6), respectively. In a longitudinal disease study in Iringa, the incidence of clinical mastitis was 31.7 cases per 100 cow-years. A randomised intervention trial indicated that intramammary antibiotics significantly reduced the proportion of bacteriologically positive quarters in the short-term (14 days post-infusion) but teat dipping had no detectable effect on bacteriological infection and CMT positive quarters. Other risk and protective factors were identified from both the cross-sectional and longitudinal included animals with Boran breeding (odds ratio (OR) = 3,40, 95% CI = 1.00-11.57, P < 0.05 for clinical mastitis, and OR = 3.51, 95% CI = 1.299.55, P < 0.01 for a CMT positive quarter), while the practice of residual calf suckling was protective for a bacteriologically positive quarter (OR = 0.63, 95% Cl = 0.48-0.81, P <= 0.001) and for a CMT positive quarter (OR = 0.69, 95% Cl = 0.63-0.75, P < 0.001). A mastitis training course for farmers and extension officers was held, and the knowledge gained and use of different methods of dissemination were assessed over time. In a subsequent randomised controlled trial, there were strong associations between knowledge gained and both the individual question asked and the combination of dissemination methods (village meeting, video and handout) used. This study demonstrated that both clinical and subclinical mastitis is common in smallholder dairying in Tanzania, and that some of the risk and protective factors for mastitis can be addressed by practical management of dairy cows following effective knowledge transfer. (c) 2006 Elsevier B.V. All rights reserved.

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Physiological parameters measured by an embedded body sensor system were demonstrated to respond to changes of the air temperature in an office environment. The thermal parameters were monitored with the use of a wireless sensor system that made possible to turn any existing room into a field laboratory. Two human subjects were monitored over daily activities and at various steady-state thermal conditions when the air temperature of the room was altered from 22-23°C to 25-28°C. The subjects indicated their thermal feeling on questionnaires. The measured skin temperature was distributed close to the calculated mean skin temperature corresponding to the given activity level. The variation of Galvanic Skin Response (GSR) reflected the evaporative heat loss through the body surfaces and indicated whether sweating occurred on the subjects. Further investigations are needed to fully evaluate the influence of thermal and other factors on the output given by the investigated body sensor system.

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This paper proposes a new iterative algorithm for OFDM joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the problem of "overfitting" such that the iterative approach may converge to a trivial solution. Although it is essential for this joint approach, the overfitting problem was relatively less studied in existing algorithms. In this paper, specifically, we apply a hard decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the phase noise, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical simulations are also given to verify the proposed algorithm.

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This paper proposes a new iterative algorithm for orthogonal frequency division multiplexing (OFDM) joint data detection and phase noise (PHN) cancellation based on minimum mean square prediction error. We particularly highlight the relatively less studied problem of "overfitting" such that the iterative approach may converge to a trivial solution. Specifically, we apply a hard-decision procedure at every iterative step to overcome the overfitting. Moreover, compared with existing algorithms, a more accurate Pade approximation is used to represent the PHN, and finally a more robust and compact fast process based on Givens rotation is proposed to reduce the complexity to a practical level. Numerical Simulations are also given to verify the proposed algorithm. (C) 2008 Elsevier B.V. All rights reserved.

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This correspondence proposes a new algorithm for the OFDM joint data detection and phase noise (PHN) cancellation for constant modulus modulations. We highlight that it is important to address the overfitting problem since this is a major detrimental factor impairing the joint detection process. In order to attack the overfitting problem we propose an iterative approach based on minimum mean square prediction error (MMSPE) subject to the constraint that the estimated data symbols have constant power. The proposed constrained MMSPE algorithm (C-MMSPE) significantly improves the performance of existing approaches with little extra complexity being imposed. Simulation results are also given to verify the proposed algorithm.

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In recent years, there have been major developments in the understanding of the cell cycle. It is now known that normal cellular proliferation is tightly regulated by the activation and deactivation of a series of proteins that constitute the cell cycle machinery. The expression and activity of components of the cell cycle can be altered during the development of a variety of diseases where aberrant proliferation contributes to the pathology of the illness. Apart from yielding a new source of untapped therapeutic targets, it is likely that manipulating the activity of such proteins in diseased states will provide an important route for treating proliferative disorders, and the opportunity to develop a novel class of future medicines.

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Virtual reality has the potential to improve visualisation of building design and construction, but its implementation in the industry has yet to reach maturity. Present day translation of building data to virtual reality is often unidirectional and unsatisfactory. Three different approaches to the creation of models are identified and described in this paper. Consideration is given to the potential of both advances in computer-aided design and the emerging standards for data exchange to facilitate an integrated use of virtual reality. Commonalities and differences between computer-aided design and virtual reality packages are reviewed, and trials of current system, are described. The trials have been conducted to explore the technical issues related to the integrated use of CAD and virtual environments within the house building sector of the construction industry and to investigate the practical use of the new technology.

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