2,860 research outputs found

    Polymers for DNA Binding

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    Models of preconception care implementation in selected countries.

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    Globally, maternal and child health faces diverse challenges depending on the status of the development of the country. Some countries have introduced or explored preconception care for various reasons. Falling birth rates and increasing knowledge about risk factors for adverse pregnancy outcomes led to the introduction of preconception care in Hong Kong in 1998, and South Korea in 2004. In Hong Kong, comprehensive preconception care including laboratory tests are provided to over 4000 women each year at a cost of 75perperson.InKorea,about6075 per person. In Korea, about 60% of the women served have known medical risk history, and the challenge is to expand the program capacity to all women who plan pregnancy, and conducting social marketing. Belgium has established an ad hoc-committee to develop a comprehensive social marketing and professional training strategy for pilot testing preconception care models in the French speaking part of Belgium, an area that represents 5 million people and 50,000 births per year using prenatal care and pediatric clinics, gynecological departments, and the genetic centers. In China, Guangxi province piloted preconceptional HIV testing and counseling among couples who sought the then mandatory premarital medical examination as a component of the three-pronged approach to reduce mother to child transmission of HIV. HIV testing rates among couples increased from 38% to 62% over one year period. In October 2003, China changed the legal requirement of premarital medical examination from mandatory to "voluntary." This change was interpreted by most women that the premarital health examination was "unnecessary" and overall premarital health examination rates dropped. Social marketing efforts piloted in 2004 indicated that 95% of women were willing to pay up to RMB 100 (US12) for preconception health care services. These case studies illustrate programmatic feasibility of preconception care services to address maternal and child health and other public health challenges in developed and emerging economies

    Gallium Nitride: An Overview of Structural Defects

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    Invited - Temporal information processing for in-sensor computing based on amorphous IGZO phototransistor

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    On facing the massive and unstructured data processing, it is imperative to emulate artificial neural networks with new physical hardware architectures in addition to software-based approaches, to overcome the barrier of the von Neumann bottleneck. By mimicking the human visual sensing system, the optoelectronic devices, which can perform data compression and reduce the network size through the reconstruction of input signals, are promising to develop the neuromorphic in-sensor computing for minimizing the time latency as well as improving the energy efficiency. In this work, we demonstrate an amorphous indium-gallium-zinc-oxide (a-IGZO) phototransistor with ZrOx high-k dielectric layer with distinct responses to various optical stimulation inputs. Due to the persistent photoconductivity (PPC) effect of a-IGZO after lighting, our device is able to exhibit synaptic functions via the application of 405 nm light spikes, such as paired-pulse facilitation (PPF) and short-term memory (STM). Furthermore, in order to perform the temporal optical signals processing, the a-IGZO phototransistor is stimulated by four-timeframe temporal pulse streams composed of 405 nm light spikes and it expresses the different temporal responses. The distinct output photocurrent response reveals that the a-IGZO phototransistor can be applied to distinguish the time-series input light signals. Accordingly, the a-IGZO phototransistor have a promising potential for processing optical temporal information and can possibly be implemented for visual in-sensor computing techniques. Please click Download on the upper right corner to see the full abstract

    Modular dynamic RBF neural network for face recognition

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    Over the years, we have seen an increase in the use of RBF neural networks for the task of face recognition. However, the use of second order algorithms as the learning algorithm for all the adjustable parameters in such networks are rare due to the high computational complexity of the calculation of the Jacobian and Hessian matrix. Hence, in this paper, we propose a modular structural training architecture to adapt the Levenberg-Marquardt based RBF neural network for the application of face recognition. In addition to the proposal of the modular structural training architecture, we have also investigated the use of different front-end processors to reduce the dimension size of the feature vectors prior to its application to the LM-based RBF neural network. The investigative study was done on three standard face databases; ORL, Yale and AR databases

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