3,828 research outputs found

    Impact of AQUA Satellite Data on Hurricane Forecast: Danielle 2010

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    This study focuses on the impact of AQUA satellite data from AIRS and AMSU on the forecast of hurricane Danielle by the Global Forecast System (GFS) model. The data assimilation method adopted to ingest the data is the Gridpoint Statistical method (GSI) which is based on the three dimensional variational (3DVAR) data assimilation technique. Two experiments were carried out to investigate the impact of AQUA satellite radiance observation on the forecast of hurricane Danielle. The first experiment (Control) assimilated all the available data while the second experiment (No AQUA) incorporated all the observations but the AQUA satellite data. Data assimilation cycling started one week prior to hurricane genesis, on 15 August 2010 06 UTC. The root mean square track forecast error shows slightly negative impact at the early lead time and slightly positive impact at later lead time. However, the root mean square intensity forecast errors by the Control are shown to be lower than No AQUA for all forecast hours, indicating positive impact of the AQUA data on the intensity forecast

    Long term in vitro expansion of epithelial stem cells enabled by pharmacological inhibition of PAK1-ROCK-Myosin II and TGF-β signaling

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    Summary: Despite substantial self-renewal capability in vivo, epithelial stem and progenitor cells located in various tissues expand for a few passages in vitro in feeder-free condition before they succumb to growth arrest. Here, we describe the EpiX method, which utilizes small molecules that inhibit PAK1-ROCK-Myosin II and TGF-β signaling to achieve over one trillion-fold expansion of human epithelial stem and progenitor cells from skin, airway, mammary, and prostate glands in the absence of feeder cells. Transcriptomic and epigenomic studies show that this condition helps epithelial cells to overcome stresses for continuous proliferation. EpiX-expanded basal epithelial cells differentiate into mature epithelial cells consistent with their tissue origins. Whole-genome sequencing reveals that the cells retain remarkable genome integrity after extensive in vitro expansion without acquiring tumorigenicity. EpiX technology provides a solution to exploit the potential of tissue-resident epithelial stem and progenitor cells for regenerative medicine. : Zhang et al. screen a small-molecule collection and find that pharmacologic inhibition of TGF-β and PAK1-ROCK-Myosin II, in low calcium conditions, supports extended expansion of epithelial stem cells in 2D format. This approach enhances the potential of tissue-resident epithelial stem cells for cell therapy. Keywords: epithelial stem and progenitor cells, cell culture method, TGF-β, PAK1/ROCK/Myosin II, feeder-free, regenerative medicine, cell therap

    A Comparison of Statistical Approaches for Predicting Stream Temperatures Across Heterogeneous Landscapes 1

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    Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73725/1/j.1752-1688.2009.00341.x.pd

    A River Valley Segment Classification of Michigan Streams Based on Fish and Physical Attributes

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    Water resource managers are frequently interested in river and stream classification systems to generalize stream conditions and establish management policies over large spatial scales. We used fish assemblage data from 745 river valley segments to develop a two‐level, river valley segment‐scale classification system of rivers and streams throughout Michigan. Regression tree analyses distinguished 10 segment types based on mean July temperature and network catchment area and 26 segment types when channel gradient was also considered. Nonmetric multidimensional scaling analyses suggested that fish assemblages differed among segment types but were only slightly influenced by channel gradient. Species that were indicative of specific segment types generally had habitat requirements that matched segment attributes. A test of classification strength using fish assemblage data from an additional 77 river valley segments indicated that the classification system performed significantly better than random groupings of river valley segments. Our classification system for river valley segments overcomes several weaknesses of the classifications previously used in Michigan, and our approach may prove beneficial for developing classifications elsewhere.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141625/1/tafs1621.pd

    Why people adopt VR English language learning systems: An extended perspective of task-technology fit

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    Virtual Reality (VR) techniques involving immersion, interaction, and imagination, not only can improve conventional teaching methods, but also can enhance the transmission of education training contents through the interaction and simulation characteristics of VR. Incorporating information technology (IT) with English teaching has become an important issue in the academic field. Emerging after computer-assisted teaching, interactive network learning, distance education, and mobile learning in the early days, virtual reality techniques have been regarded as a new trend of merging technology with education. To explore the factors affecting users’ adoption intention of VR English language learning systems (VRELLS), this study has sought to build a theoretical framework based on the task-technology fit theory (extrinsic motivation) combining users’ needs (internal and external needs) and satisfaction to put forward an integrated research model (perceived needs-technology fit model), which explicates people’s adoption behaviors of VRELLS. An online questionnaire was employed to collect empirical data. A total of 291 samples were analyzed using a structural equation modeling (SEM) approach. The results of the study showed that both perceived needs-technology fit and satisfaction play a significant role in the user’ adoption intention of VRELLS services. In addition, the utilitarian and hedonic needs have a positive impact on the user’s perceived needs-technology fit. Also, it was found that relative advantage, service compatibility and complexity are important factors in influencing individuals’ perceived needs-technology fit. The implications of these findings are discussed along with suggestions for future research

    A Link Transmission Model with Variable Speed Limits and Turn-Level Queue Transmission at Signalized Intersections

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    The link transmission model (LTM) is an efficient and widely used macro-level approach for simulating traffic flow. However, the state-of-the-art LTMs usually focused on segment-level modelling, in which the traffic operation differences among multiple turning directions are neglected. Such models are incapable of differentiating the turn-level queue transmission, resulting in underrepresented queue spillbacks and misidentification of bottlenecks. Moreover, a constant free-flow speed is usually assumed to formulate LTMs, restricting their applications to model dynamic traffic management strategies involving variable speed limits (VSL) and connected and automated vehicles. This study proposed an extended LTM with VSL and turn-level queue transmission to capture the traffic flow propagation at signalized intersections. First, each road segment (RS) with multiple turning directions is divided into many free-flow and queueing parts characterized by the triangular fundamental diagrams. Then, the vehicle propagation within the link is described by the turn-level link inflow, queue inflow, and outflow, which depends on the free-flow speed changes. A node model involving an iterative procedure is further defined to capture the potential queue spillback, which estimates the actual flow propagation between the adjacent RSs. Simulated and field data were used to verify the proposed model performance. Results reveal that the proposed LTM predict traffic operations of complex intersections with multiple turning movements, VSL and signal control schemes, and enables an accurate yet computationally tractable representation of flow propagation

    On Sampling Top-K Recommendation Evaluation

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    Recently, Rendle has warned that the use of sampling-based top-kk metrics might not suffice. This throws a number of recent studies on deep learning-based recommendation algorithms, and classic non-deep-learning algorithms using such a metric, into jeopardy. In this work, we thoroughly investigate the relationship between the sampling and global top-KK Hit-Ratio (HR, or Recall), originally proposed by Koren[2] and extensively used by others. By formulating the problem of aligning sampling top-kk (SHR@kSHR@k) and global top-KK (HR@KHR@K) Hit-Ratios through a mapping function ff, so that SHR@k≈HR@f(k)SHR@k\approx HR@f(k), we demonstrate both theoretically and experimentally that the sampling top-kk Hit-Ratio provides an accurate approximation of its global (exact) counterpart, and can consistently predict the correct winners (the same as indicate by their corresponding global Hit-Ratios)
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