16,486 research outputs found

    Composting paper and grass clippings with anaerobically treated palm oil mill effluent

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    Purpose The purpose of this study is to investigate the composting performance of anaerobically treated palm oil mill effluent (AnPOME) mixed with paper and grass clippings. Methods Composting was conducted using a laboratory scale system for 40 days. Several parameters were determined: temperature, mass reduction, pH, electrical conductivity, colour, zeta potential, phytotoxicity and final compost nutrients. Results The moisture content and compost mass were reduced by 24 and 18 %, respectively. Both final compost pH value and electrical conductivity were found to increase in value. Colour (measured as PtCo) was not suitable as a maturity indicator. The negative zeta potential values decreased from −12.25 to −21.80 mV. The phytotoxicity of the compost mixture was found to decrease in value during the process and the final nutrient value of the compost indicates its suitability as a soil conditioner. Conclusions From this study, we conclude that the addition of paper and grass clippings can be a potential substrate to be composted with anaerobically treated palm oil mill effluent (AnPOME). The final compost produced is suitable for soil conditioner

    Nicotine strongly activates dendritic cell-mediated adaptive immunity - potential role for progression of atherosclerotic lesions

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    Background - Antigen-presenting cells (APCs) such as monocytes and dendritic cells (DCs) stimulate T-cell proliferation and activation in the course of adaptive immunity. This cellular interaction plays a role in the growth of atherosclerotic plaques. Nicotine has been shown to increase the growth of atherosclerotic lesions. Therefore, we investigated whether nicotine can stimulate APCs and their T cell–stimulatory capacity using human monocyte–derived DCs and murine bone marrow–derived DCs as APCs. Methods and Results - Nicotine dose-dependently (10-8 to 10-4 mol/L) induced DC expression of costimulatory molecules (ie, CD86, CD40), MHC class II, and adhesion molecules (ie, LFA-1, CD54). Moreover, nicotine induced a 7.0-fold increase in secretion of the proinflammatory TH1 cytokine interleukin-12 by human DCs. These effects were abrogated by the nicotinic receptor antagonist -bungarotoxin and mecamylamine, respectively. The effects of nicotine were mediated in part by the phosphorylation of the PI3 kinase downstream target Akt and the mitogen-activated kinases ERK and p38 MAPK. Nicotine-stimulated APCs had a greater capacity to stimulate T-cell proliferation and cytokine secretion, as documented by mixed lymphocyte reactions and ovalbumin-specific assays with ovalbumin-transgenic DO10.11 mice. In a murine model of atherosclerosis, nicotine significantly enhanced the recruitment of DCs to atherosclerotic lesions in vivo. Conclusions - Nicotine activates DCs and augments their capacity to stimulate T-cell proliferation and cytokine secretion. These effects of nicotine may contribute to its influence on the progression of atherosclerotic lesions

    Asynchronous displays for multi-UV search tasks

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    Synchronous video has long been the preferred mode for controlling remote robots with other modes such as asynchronous control only used when unavoidable as in the case of interplanetary robotics. We identify two basic problems for controlling multiple robots using synchronous displays: operator overload and information fusion. Synchronous displays from multiple robots can easily overwhelm an operator who must search video for targets. If targets are plentiful, the operator will likely miss targets that enter and leave unattended views while dealing with others that were noticed. The related fusion problem arises because robots' multiple fields of view may overlap forcing the operator to reconcile different views from different perspectives and form an awareness of the environment by "piecing them together". We have conducted a series of experiments investigating the suitability of asynchronous displays for multi-UV search. Our first experiments involved static panoramas in which operators selected locations at which robots halted and panned their camera to capture a record of what could be seen from that location. A subsequent experiment investigated the hypothesis that the relative performance of the panoramic display would improve as the number of robots was increased causing greater overload and fusion problems. In a subsequent Image Queue system we used automated path planning and also automated the selection of imagery for presentation by choosing a greedy selection of non-overlapping views. A fourth set of experiments used the SUAVE display, an asynchronous variant of the picture-in-picture technique for video from multiple UAVs. The panoramic displays which addressed only the overload problem led to performance similar to synchronous video while the Image Queue and SUAVE displays which addressed fusion as well led to improved performance on a number of measures. In this paper we will review our experiences in designing and testing asynchronous displays and discuss challenges to their use including tracking dynamic targets. © 2012 by the American Institute of Aeronautics and Astronautics, Inc

    Zero-shot keyword spotting for visual speech recognition in-the-wild

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    Visual keyword spotting (KWS) is the problem of estimating whether a text query occurs in a given recording using only video information. This paper focuses on visual KWS for words unseen during training, a real-world, practical setting which so far has received no attention by the community. To this end, we devise an end-to-end architecture comprising (a) a state-of-the-art visual feature extractor based on spatiotemporal Residual Networks, (b) a grapheme-to-phoneme model based on sequence-to-sequence neural networks, and (c) a stack of recurrent neural networks which learn how to correlate visual features with the keyword representation. Different to prior works on KWS, which try to learn word representations merely from sequences of graphemes (i.e. letters), we propose the use of a grapheme-to-phoneme encoder-decoder model which learns how to map words to their pronunciation. We demonstrate that our system obtains very promising visual-only KWS results on the challenging LRS2 database, for keywords unseen during training. We also show that our system outperforms a baseline which addresses KWS via automatic speech recognition (ASR), while it drastically improves over other recently proposed ASR-free KWS methods.Comment: Accepted at ECCV-201
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