1,208 research outputs found
Stratospheric climate and variability from a general circulation model and observations. Part II: Results for March-May, June-August and September-November.
The climate and natural variability of the large-scale stratospheric circulation simulated by a newly developed general circulation model are evaluated against available global observations. The simulation consisted of a 30-year annual cycle integration performed with a comprehensive model of the troposphere and stratosphere. The observations consisted of a 15-year dataset from global operational analyses of the troposphere and stratosphere. The model evaluation concentrates on the simulation of the evolution of the extratropical stratospheric circulation in both hemispheres. The December-February climatology of the observed zonal mean winter circulation is found to be reasonably well captured by the model, although in the Northern Hemisphere upper stratosphere the simulated westerly winds are systematically stronger and a cold bias is apparent in the polar stratosphere. This Northern Hemisphere stratospheric cold bias virtually disappears during spring (March-May), consistent with a realistic simulation of the spring weakening of the mean westerly winds in the model. A considerable amount of monthly interannual variability is also found in the simulation in the Northern Hemisphere in late winter and early spring. The simulated interannual variability is predominantly caused by polar warmings of the stratosphere, in agreement with observations. The breakdown of the Northern Hemisphere stratospheric polar vortex appears therefore to occur in a realistic way in the model. However, in early winter the model severely underestimates the interannual variability, especially in the upper troposphere. The Southern Hemisphere winter (June-August) zonal mean temperature is systematically colder in the model, and the simulated winds are somewhat too strong in the upper stratosphere. Contrary to the results for the Northern Hemisphere spring, this model cold bias worsens during the Southern Hemisphere spring (September-November). Significant discrepancies between the model results and the observations are therefore found during the breakdown of the Southern Hemisphere polar vortex. For instance, the simulated Southern Hemisphere stratospheric westerly jet continuously decreases in intensity more or less in situ from June to November, while the observed stratospheric jet moves downward and poleward
A Decision-Making Machine Learning Approach in Hermite Spectral Approximations of Partial Differential Equations
The accuracy and effectiveness of Hermite spectral methods for the numerical discretization of partial differential equations on unbounded domains are strongly affected by the amplitude of the Gaussian weight function employed to describe the approximation space. This is particularly true if the problem is under-resolved, i.e., there are no enough degrees of freedom. The issue becomes even more crucial when the equation under study is time-dependent, forcing in this way the choice of Hermite functions where the corresponding weight depends on time. In order to adapt dynamically the approximation space, it is here proposed an automatic decision-making process that relies on machine learning techniques, such as deep neural networks and support vector machines. The algorithm is numerically tested with success on a simple 1D problem, but the main goal is its exportability in the context of more serious applications. As a matter of fact we also show at the end an application in the framework of plasma physics
A Data Architecture to aid Life Cycle Assessment in closed-loop Reusable Plastic Container networks
Abstract Returnable container networks have caught the eye of those companies that aim to reduce waste generation and environmental impact. The literature already includes studies on the environmental impact (i.e. Life Cycle Assessment, LCA) of these networks. However, the major part is based on secondary data since the collection of primary data is complex and time-intensive. This paper proposes an object-relational database dedicated to the storage of data from a closed-loop reusable plastic crates (RPC) networks for fruits and vegetables. The goal is supporting scholars and managers during the LCA through a user-friendly data architecture, while suggesting structured guidelines for the primary data collection. Each node of the RPC network is characterized by a similar set of entity types, such as machines, which allows to process the RPCs with respect to specific cycles. Each entity, process and cycle are therefore reflected in the database by objects that are connected with relations
Loss of the Intellectual Disability and Autism Gene Cc2d1a and Its Homolog Cc2d1b Differentially Affect Spatial Memory, Anxiety, and Hyperactivity
Hundreds of genes are mutated in non-syndromic intellectual disability (ID) and autism spectrum disorder (ASD), with each gene often involved in only a handful of cases. Such heterogeneity can be daunting, but rare recessive loss of function (LOF) mutations can be a good starting point to provide insight into the mechanisms of neurodevelopmental disease. Biallelic LOF mutations in the signaling scaffold CC2D1Acause a rare form of autosomal recessive ID, sometimes associated with ASD and seizures. In parallel, we recently reported that Cc2d1a-deficient mice present with cognitive and social deficits, hyperactivity and anxiety. In Drosophila, loss of the only ortholog of Cc2d1a, lgd, is embryonically lethal, while in vertebrates, Cc2d1a has a homolog Cc2d1b which appears to be compensating, indicating that Cc2d1a and Cc2d1b have a redundant function in humans and mice. Here, we generate an allelic series of Cc2d1a and Cc2d1b LOF to determine the relative role of these genes during behavioral development. We generated Cc2d1b knockout (KO), Cc2d1a/1b double heterozygous and double KO mice, then performed behavioral studies to analyze learning and memory, social interactions, anxiety, and hyperactivity. We found that Cc2d1a and Cc2d1b have partially overlapping roles. Overall, loss of Cc2d1b is less severe than loss of Cc2d1a, only leading to cognitive deficits, while Cc2d1a/1b double heterozygous animals are similar to Cc2d1a-deficient mice. These results will help us better understand the deficits in individuals with CC2D1A mutations, suggesting that recessive CC2D1B mutations and trans-heterozygous CC2D1A and CC2D1B mutations could also contribute to the genetics of ID
Interstellar Comet 2I/Borisov exhibits a structure similar to native Solar System comets
We processed images taken with the Hubble Space Telescope (HST) to
investigate any morphological features in the inner coma suggestive of a
peculiar activity on the nucleus of the interstellar comet 2I/Borisov. The coma
shows an evident elongation, in the position angle (PA) ~0-180d direction,
which appears related to the presence of a jet originating from a single active
source on the nucleus. A counterpart of this jet directed towards PA ~10d was
detected through analysis of the changes of the inner coma morphology on HST
images taken in different dates and processed with different filters. These
findings indicate that the nucleus is probably rotating with a spin axis
projected near the plane of the sky and oriented at PA ~100d-280d, and that the
active source is lying in a near-equatorial position. Subsequent observations
of HST allowed us to determine the direction of the spin axis at
RA=17h20m+/-15d and Dec = -35d+\-10d.Comment: 5 pages, 6 figures (low resolution). Full-resolution figures and
bonus material at this url
https://web.oapd.inaf.it/bedin/files/PAPERs_eMATERIALs/BORISOV_comet/
Accepted for publication in MNRAS Letters on 2020 April 1st (not a joke ;
Identification of candidate biomarkers of the exposure to PCBs in contaminated cattle: A gene expression- and proteomic-based approach
Compressed Data Structures for Dynamic Sequences
We consider the problem of storing a dynamic string over an alphabet
in compressed form. Our representation
supports insertions and deletions of symbols and answers three fundamental
queries: returns the -th symbol in ,
counts how many times a symbol occurs among the
first positions in , and finds the position
where a symbol occurs for the -th time. We present the first
fully-dynamic data structure for arbitrarily large alphabets that achieves
optimal query times for all three operations and supports updates with
worst-case time guarantees. Ours is also the first fully-dynamic data structure
that needs only bits, where is the -th order
entropy and is the string length. Moreover our representation supports
extraction of a substring in optimal time
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