610 research outputs found

    Exploring Protein–Nanoparticle Interactions with Coarse‐Grained Protein Folding Models

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136668/1/smll201603748.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136668/2/smll201603748-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136668/3/smll201603748_am.pd

    Lidar Based Emissions Measurement at the Whole Facility Scale: Method and Error Analysis

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    Particulate emissions from agricultural sources vary from dust created by operations and animal movement to the fine secondary particulates generated from ammonia and other emitted gases. The development of reliable facility emission data using point sampling methods designed to characterize regional, well-mixed aerosols are challenged by changing wind directions, disrupted flow fields caused by structures, varied surface temperatures, and the episodic nature of the sources found at these facilities. We describe a three-wavelength lidar-based method, which, when added to a standard point sampler array, provides unambiguous measurement and characterization of the particulate emissions from agricultural production operations in near real time. Point-sampled data are used to provide the aerosol characterization needed for the particle concentration and size fraction calibration, while the lidar provides 3D mapping of particulate concentrations entering, around, and leaving the facility. Differences between downwind and upwind measurements provide an integrated aerosol concentration profile, which, when multiplied by the wind speed profile, produces the facility source flux. This approach assumes only conservation of mass, eliminating reliance on boundary layer theory. We describe the method, examine measurement error, and demonstrate the approach using data collected over a range of agricultural operations, including a swine grow-finish operation, an almond harvest, and a cotton gin emission study

    The global carbon budget 1959-2011

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    Accurate assessments of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere is important to better understand the global carbon cycle, support the climate policy process, and project future climate change. Present-day analysis requires the combination of a range of data, algorithms, statistics and model estimates and their interpretation by a broad scientific community. Here we describe datasets and a methodology developed by the global carbon cycle science community to quantify all major components of the global carbon budget, including their uncertainties. We discuss changes compared to previous estimates, consistency within and among components, and methodology and data limitations. CO2 emissions from fossil fuel combustion and cement production (EFF) are based on energy statistics, while emissions from Land-Use Change (ELUC), including deforestation, are based on combined evidence from land cover change data, fire activity in regions undergoing deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. Finally, the global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms. For the last decade available (2002–2011), EFF was 8.3 ± 0.4 PgC yr−1, ELUC 1.0 ± 0.5 PgC yr−1, GATM 4.3 ± 0.1PgC yr−1, SOCEAN 2.5 ± 0.5 PgC yr−1, and SLAND 2.6 ± 0.8 PgC yr−1. For year 2011 alone, EFF was 9.5 ± 0.5 PgC yr−1, 3.0 percent above 2010, reflecting a continued trend in these emissions; ELUC was 0.9 ± 0.5 PgC yr−1, approximately constant throughout the decade; GATM was 3.6 ± 0.2 PgC yr−1, SOCEAN was 2.7 ± 0.5 PgC yr−1, and SLAND was 4.1 ± 0.9 PgC yr−1. GATM was low in 2011 compared to the 2002–2011 average because of a high uptake by the land probably in response to natural climate variability associated to La Niña conditions in the Pacific Ocean. The global atmospheric CO2 concentration reached 391.31 ± 0.13 ppm at the end of year 2011. We estimate that EFF will have increased by 2.6% (1.9–3.5%) in 2012 based on projections of gross world product and recent changes in the carbon intensity of the economy. All uncertainties are reported as ±1 sigma (68% confidence assuming Gaussian error distributions that the real value lies within the given interval), reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. This paper is intended to provide a baseline to keep track of annual carbon budgets in the future

    DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis

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    Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS), especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently large and comprehensive datasets is currently a bottleneck for the progress of DMS development, crucial for the transition of automated driving from SAE Level-2 to SAE Level-3. In this paper, we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which includes real and simulated driving scenarios: distraction, gaze allocation, drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A comparison with existing similar datasets is included, which shows the DMD is more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated by extracting a subset of it, the dBehaviourMD dataset, containing 13 distraction activities, prepared to be used in DL training processes. Furthermore, we propose a robust and real-time driver behaviour recognition system targeting a real-world application that can run on cost-efficient CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated with different types of fusion strategies, which all reach enhanced accuracy still providing real-time response.Comment: Accepted to ECCV 2020 workshop - Assistive Computer Vision and Robotic

    “Not a good look”: impossible dilemmas for young women negotiating the culture of intoxication in the United Kingdom.

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    This paper investigates young women's alcohol consumption in the United Kingdom within a widespread culture of intoxication in relation to recent debates about postfeminism and contemporary femininity. Young women are faced with an “impossible dilemma,” arising from the contradiction between a hedonistic discourse of alcohol consumption and postfeminist discourse around attaining and maintaining the “right” form of hypersexual heterosexual femininity. Drawing on a recent interview study with 24 young white working-class and middle-class women in the South-West of England, we explore how young women inhabit the dilemmas of contemporary femininity in youth drinking cultures, striving to achieve the “right” form of hypersexual femininity and an “optimum” level of drunkenness

    Proactive and politically skilled professionals: What is the relationship with affective occupational commitment?

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    The aim of this study is to extend research on employee affective commitment in three ways: (1) instead of organizational commitment the focus is on occupational commitment; (2) the role of proactive personality on affective occupational commitment is examined; and (3) occupational satisfaction is examined as a mediator and political skills as moderator in the relationship between proactive personality and affective occupational commitment. Two connected studies, one in a hospital located in the private sector and one in a university located in the public sector, are carried out in Pakistan, drawing on a total sample of over 400 employees. The results show that proactive personality is positively related to affective occupational commitment, and that occupational satisfaction partly mediates the relationship between proactive personality and affective occupational commitment. No effect is found for a moderator effect of political skills in the relationship between proactive personality and affective occupational commitment. Political skills however moderate the relationship between proactive personality and affective organizational commitment

    Genome-wide diversity and phylogeography of Mycobacterium avium subsp. paratuberculosis in Canadian dairy cattle

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    Mycobacterium avium subsp. paratuberculosis (MAP) is the causative bacterium of Johne’s disease (JD) in ruminants. The control of JD in the dairy industry is challenging, but can be improved with a better understanding of the diversity and distribution of MAP subtypes. Previously established molecular typing techniques used to differentiate MAP have not been sufficiently discriminatory and/or reliable to accurately assess the population structure. In this study, the genetic diversity of 182 MAP isolates representing all Canadian provinces was compared to the known global diversity, using single nucleotide polymorphisms identified through whole genome sequencing. MAP isolates from Canada represented a subset of the known global diversity, as there were global isolates intermingled with Canadian isolates, as well as multiple global subtypes that were not found in Canada. One Type III and six “Bison type” isolates were found in Canada as well as one Type II subtype that represented 86% of all Canadian isolates. Rarefaction estimated larger subtype richness in Québec than in other Canadian provinces using a strict definition of MAP subtypes and lower subtype richness in the Atlantic region using a relaxed definition. Significant phylogeographic clustering was observed at the inter-provincial but not at the intra-provincial level, although most major clades were found in all provinces. The large number of shared subtypes among provinces suggests that cattle movement is a major driver of MAP transmission at the herd level, which is further supported by the lack of spatial clustering on an intra-provincial scale
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