402 research outputs found

    B816: An Economic Analysis of a Maine Dairy Farm Anaerobic Digester

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    Anaerobic digestion is a method for decomposing organic matter, producing in the process, biogas, which is mostly methane. This process can be used to eliminate or reduce disagreeable and often environmentally harmful characteristics of wastes. During the autumn of 1984, the University of Maine began operation of an anaerobic digestion unit acquired from Agway, Inc., a large Northeastern agricultural cooperative. This system, installed at the Witter Animal Science Center, decomposes animal manures and ultimately produces electricity and hot water. A by-product of the system is a fertilizer with characteristics superior to fertilizers produced from biological wastes that have not undergone a process of anaerobic digestion. The research objectives were to (1) construct an economic-engineering model representing the waste to energy system, (2) quantify the benefits and costs of the system, (3) estimate the cash flows accruing over the lifespan of the system, (4) evaluate the model to determine the net present value of the system, and (5) evaluate alternative scenarios to determine the effect on economic feasibility.https://digitalcommons.library.umaine.edu/aes_bulletin/1051/thumbnail.jp

    Scaling Up Patient-Centered Psychological Treatments for Perinatal Depression in the Wake of a Global Pandemic

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    There is a call to action to reduce the public health burden of perinatal depression worldwide. The COVID-19 pandemic has further highlighted significant gaps in perinatal mental health care, especially among women who identify as Black, Indigenous, People of Color (BIPOC). While psychotherapeutic (cognitive, behavioral and interpersonal) interventions are endorsed for perinatal mood disorders, barriers to access and uptake contribute to inequitable access to treatment at the population level. To effectively address these barriers and increase the scalability of psychotherapy among perinatal women, we suggest four pragmatic questions to be answered from a patient-centered lens; namely, “who,” “what,” “how,” and “when.” Promising avenues include task-sharing among mental health non-specialists, an emphasis on culturally sensitive care, web-based delivery of psychotherapy with some caveats, and a lifespan approach to perinatal mental health. Innovative research efforts are seeking to validate these approaches in diverse contexts across North America and the UK, lending optimism toward scalable and long-term solutions for equitable perinatal mental health care

    Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning

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    A fundamental question in any peer-to-peer ridesharing system is how to, both effectively and efficiently, dispatch user's ride requests to the right driver in real time. Traditional rule-based solutions usually work on a simplified problem setting, which requires a sophisticated hand-crafted weight design for either centralized authority control or decentralized multi-agent scheduling systems. Although recent approaches have used reinforcement learning to provide centralized combinatorial optimization algorithms with informative weight values, their single-agent setting can hardly model the complex interactions between drivers and orders. In this paper, we address the order dispatching problem using multi-agent reinforcement learning (MARL), which follows the distributed nature of the peer-to-peer ridesharing problem and possesses the ability to capture the stochastic demand-supply dynamics in large-scale ridesharing scenarios. Being more reliable than centralized approaches, our proposed MARL solutions could also support fully distributed execution through recent advances in the Internet of Vehicles (IoV) and the Vehicle-to-Network (V2N). Furthermore, we adopt the mean field approximation to simplify the local interactions by taking an average action among neighborhoods. The mean field approximation is capable of globally capturing dynamic demand-supply variations by propagating many local interactions between agents and the environment. Our extensive experiments have shown the significant improvements of MARL order dispatching algorithms over several strong baselines on the gross merchandise volume (GMV), and order response rate measures. Besides, the simulated experiments with real data have also justified that our solution can alleviate the supply-demand gap during the rush hours, thus possessing the capability of reducing traffic congestion.Comment: 11 pages, 9 figure

    Low-dose aspirin for the prevention of preterm delivery in nulliparous women with a singleton pregnancy (ASPIRIN): a randomised, double-blind, placebo-controlled trial.

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    BACKGROUND: Preterm birth remains a common cause of neonatal mortality, with a disproportionately high burden in low-income and middle-income countries. Meta-analyses of low-dose aspirin to prevent pre-eclampsia suggest that the incidence of preterm birth might also be decreased, particularly if initiated before 16 weeks of gestation. METHODS: ASPIRIN was a randomised, multicountry, double-masked, placebo-controlled trial of low-dose aspirin (81 mg daily) initiated between 6 weeks and 0 days of pregnancy, and 13 weeks and 6 days of pregnancy, in nulliparous women with an ultrasound confirming gestational age and a singleton viable pregnancy. Participants were enrolled at seven community sites in six countries (two sites in India and one site each in the Democratic Republic of the Congo, Guatemala, Kenya, Pakistan, and Zambia). Participants were randomly assigned (1:1, stratified by site) to receive aspirin or placebo tablets of identical appearance, via a sequence generated centrally by the data coordinating centre at Research Triangle Institute International (Research Triangle Park, NC, USA). Treatment was masked to research staff, health providers, and patients, and continued until 36 weeks and 7 days of gestation or delivery. The primary outcome of incidence of preterm birth, defined as the number of deliveries before 37 weeks\u27 gestational age, was analysed in randomly assigned women with pregnancy outcomes at or after 20 weeks, according to a modified intention-to-treat (mITT) protocol. Analyses of our binary primary outcome involved a Cochran-Mantel-Haenszel test stratified by site, and generalised linear models to obtain relative risk (RR) estimates and associated confidence intervals. Serious adverse events were assessed in all women who received at least one dose of drug or placebo. This study is registered with ClinicalTrials.gov, NCT02409680, and the Clinical Trial Registry-India, CTRI/2016/05/006970. FINDINGS: From March 23, 2016 to June 30, 2018, 14 361 women were screened for inclusion and 11 976 women aged 14-40 years were randomly assigned to receive low-dose aspirin (5990 women) or placebo (5986 women). 5780 women in the aspirin group and 5764 in the placebo group were evaluable for the primary outcome. Preterm birth before 37 weeks occurred in 668 (11·6%) of the women who took aspirin and 754 (13·1%) of those who took placebo (RR 0·89 [95% CI 0·81 to 0·98], p=0·012). In women taking aspirin, we also observed significant reductions in perinatal mortality (0·86 [0·73-1·00], p=0·048), fetal loss (infant death after 16 weeks\u27 gestation and before 7 days post partum; 0·86 [0·74-1·00], p=0·039), early preterm delivery (\u3c34 \u3eweeks; 0·75 [0·61-0·93], p=0·039), and the incidence of women who delivered before 34 weeks with hypertensive disorders of pregnancy (0·38 [0·17-0·85], p=0·015). Other adverse maternal and neonatal events were similar between the two groups. INTERPRETATION: In populations of nulliparous women with singleton pregnancies from low-income and middle-income countries, low-dose aspirin initiated between 6 weeks and 0 days of gestation and 13 weeks and 6 days of gestation resulted in a reduced incidence of preterm delivery before 37 weeks, and reduced perinatal mortality. FUNDING: Eunice Kennedy Shriver National Institute of Child Health and Human Development

    The ADAMTS (A Disintegrin and Metalloproteinase with Thrombospondin motifs) family

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    The ADAMTS (A Disintegrin and Metalloproteinase with Thrombospondin motifs) enzymes are secreted, multi-domain matrix-associated zinc metalloendopeptidases that have diverse roles in tissue morphogenesis and patho-physiological remodeling, in inflammation and in vascular biology. The human family includes 19 members that can be sub-grouped on the basis of their known substrates, namely the aggrecanases or proteoglycanases (ADAMTS1, 4, 5, 8, 9, 15 and 20), the procollagen N-propeptidases (ADAMTS2, 3 and 14), the cartilage oligomeric matrix protein-cleaving enzymes (ADAMTS7 and 12), the von-Willebrand Factor proteinase (ADAMTS13) and a group of orphan enzymes (ADAMTS6, 10, 16, 17, 18 and 19). Control of the structure and function of the extracellular matrix (ECM) is a central theme of the biology of the ADAMTS, as exemplified by the actions of the procollagen-N-propeptidases in collagen fibril assembly and of the aggrecanases in the cleavage or modification of ECM proteoglycans. Defects in certain family members give rise to inherited genetic disorders, while the aberrant expression or function of others is associated with arthritis, cancer and cardiovascular disease. In particular, ADAMTS4 and 5 have emerged as therapeutic targets in arthritis. Multiple ADAMTSs from different sub-groupings exert either positive or negative effects on tumorigenesis and metastasis, with both metalloproteinase-dependent and -independent actions known to occur. The basic ADAMTS structure comprises a metalloproteinase catalytic domain and a carboxy-terminal ancillary domain, the latter determining substrate specificity and the localization of the protease and its interaction partners; ancillary domains probably also have independent biological functions. Focusing primarily on the aggrecanases and proteoglycanases, this review provides a perspective on the evolution of the ADAMTS family, their links with developmental and disease mechanisms, and key questions for the future
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