A non-abelian quasi-particle model for gluon plasma

We propose a quasi-particle model for the thermodynamic description of the gluon plasma which takes into account non-abelian characteristics of the gluonic field. This is accomplished utilizing massive non-linear plane wave solutions of the classical equations of motion with a variable mass parameter, reflecting the scale invariance of the Yang-Mills Lagrangian. For the statistical description of the gluon plasma we interpret these non-linear waves as quasi-particles with a temperature dependent mass distribution. Quasi-Gaussian distributions with a common variance but different temperature dependent mean masses for the longitudinal and transverse modes are employed. We use recent Lattice results to fix the mean transverse and longitudinal masses while the variance is fitted to the equation of state of pure $SU(3)$ on the Lattice. Thus, our model succeeds to obtain both a consistent description of the gluon plasma energy density as well as a correct behaviour of the mass parameters near the critical point.Comment: 7 pages, 2 figure

Ship-hull shape optimization with a T-spline based BEM-isogeometric solver

In this work, we present a ship-hull optimization process combining a T-spline based parametric ship-hull model and an Isogeometric Analysis (IGA) hydrodynamic solver for the calculation of ship wave resistance. The surface representation of the ship-hull instances comprise one cubic T-spline with extraordinary points, ensuring C2 continuity everywhere except for the vicinity of extraordinary points where G1 continuity is achieved. The employed solver for ship wave resistance is based on the Neumann-Kelvin formulation of the problem, where the resulting Boundary Integral Equation is numerically solved using a higher order collocated Boundary Element Method which adopts the IGA concept and the T-spline representation for the ship-hull surface. The hydrodynamic solver along with the ship parametric model are subsequently integrated within an appropriate optimization environment for local and global ship-hull optimizations against the criterion of minimum resistance

Corticotropin-releasing hormone exerts direct effects on neuronal progenitor cells: implications for neuroprotection

Neurogenesis during embryonic and adult life is tightly regulated by a network of transcriptional, growth and hormonal factors. Emerging evidence indicates that activation of the stress response, via the associated glucocorticoid increase, reduces neurogenesis and contributes to the development of adult diseases.As corticotrophin-releasing hormone (CRH) or factor is the major mediator of adaptive response to stressors, we sought to investigate its involvement in this process. Accordingly, we found that CRH could reverse the damaging effects of glucocorticoid on neural stem/progenitor cells (NS/PCs), while its genetic deficiency results in compromised proliferation and enhanced apoptosis during neurogenesis. Analyses in fetal and adult mouse brain revealed significant expression of CRH receptors in proliferating neuronal progenitors. Furthermore, by using primary cultures of NS/PCs, we characterized the molecular mechanisms and identified CRH receptor-1 as the receptor mediating the neuroprotective effects of CRH. Finally, we demonstrate the expression of CRH receptors in human fetal brain from early gestational age, in areas of active neuronal proliferation. These observations raise the intriguing possibility for CRH-mediated pharmacological applications in diseases characterized by altered neuronal homeostasis, including depression, dementia, neurodegenerative diseases, brain traumas and obesity

Evidence for a bifactor structure of the Scales of Psychological Well-being using exploratory structural equation modeling

This research investigates the much-debated factor structure of the 54-item version of Ryff’s (1989) Scales of Psychological Well-being (SPWB). Using two samples (n1 = 573; n2 = 449) of undergraduate university students, we apply confirmatory factor analysis (CFA) along with recently developed exploratory structural equation modeling (ESEM) techniques to evaluate several unidimensional and multidimensional models identified in previous research, as well as a new bifactor model. In a bifactor model, items load directly on both a global and a specific factor; when tested using ESEM, cross-loadings on other specific factors are also permitted and are targeted to be as close to zero as possible. After comparing various ESEM and traditional CFA models, the results indicate that a bifactor model estimated using ESEM provided the best fit to the data. Most items were found to reflect the global factor, but some items failed to reflect the intended specific factor. Thus, the 54-item version of the SPWB appears to be a good measure of overall psychological well-being, but may need refinement as a measure of the intended specific factors, at least among young adults. The benefits of applying ESEM to investigate the factor structure of the SPWB in other populations are discussed

Modelling wake effects in large wind farms in complex terrain: the problem, the methods and the issues

Computational fluid dynamic (CFD) methods are used in this paper to predict the power production from entire wind farms in complex terrain and to shed some light into the wake flow patterns. Two full three-dimensional Navier–Stokes solvers for incompressible fluid flow, employing k − ϵ and k − ω turbulence closures, are used. The wind turbines are modeled as momentum absorbers by means of their thrust coefficient through the actuator disk approach. Alternative methods for estimating the reference wind speed in the calculation of the thrust are tested. The work presented in this paper is part of the work being undertaken within the UpWind Integrated Project that aims to develop the design tools for next generation of large wind turbines. In this part of UpWind, the performance of wind farm and wake models is being examined in complex terrain environment where there are few pre-existing relevant measurements. The focus of the work being carried out is to evaluate the performance of CFD models in large wind farm applications in complex terrain and to examine the development of the wakes in a complex terrain environment

CFD modelling of wind farms in complex terrain

Modelling of entire wind farms in flat and complex terrain using a full 3D Navier–Stokes solver for incompressible flow is presented in this paper. Numerical integration of the governing equations is performed using an implicit pressure correction scheme, where the wind turbines (W/Ts) are modelled as momentum absorbers through their thrust coefficient. The k–ω turbulence model, suitably modified for atmospheric flows, is employed for closure. A correction is introduced to account for the underestimation of the near wake deficit, in which the turbulence time scale is bounded using a general “realizability” constraint for the fluctuating velocities. The second modelling issue that is discussed in this paper is related to the determination of the reference wind speed for the thrust calculation of the machines. Dealing with large wind farms and wind farms in complex terrain, determining the reference wind speed is not obvious when a W/T operates in the wake of another WT and/or in complex terrain. Two alternatives are compared: using the wind speed value at hub height one diameter upstream of the W/T and adopting an induction factor-based concept to overcome the utilization of a wind speed at a certain distance upwind of the rotor. Application is made in two wind farms, a five-machine one located in flat terrain and a 43-machine one located in complex terrain

Simulation of wind farms in flat and complex terrain using CFD

Use of computational fluid dynamic (CFD) methods to predict the power production from wind entire wind farms in flat and complex terrain is presented in this paper. Two full 3D Navier–Stokes solvers for incompressible flow are employed that incorporate the k–ε and k–ω turbulence models respectively. The wind turbines (W/Ts) are modelled as momentum absorbers by means of their thrust coefficient using the actuator disk approach. The WT thrust is estimated using the wind speed one diameter upstream of the rotor at hub height. An alternative method that employs an induction-factor based concept is also tested. This method features the advantage of not utilizing the wind speed at a specific distance from the rotor disk, which is a doubtful approximation when a W/T is located in the wake of another and/or the terrain is complex. To account for the underestimation of the near wake deficit, a correction is introduced to the turbulence model. The turbulence time scale is bounded using the general “realizability” constraint for the turbulent velocities. Application is made on two wind farms, a five-machine one located in flat terrain and another 43-machine one located in complex terrain. In the flat terrain case, the combination of the induction factor method along with the turbulence correction provides satisfactory results. In the complex terrain case, there are some significant discrepancies with the measurements, which are discussed. In this case, the induction factor method does not provide satisfactory results

TOWARDS A PAN-EU BUILDING FOOTPRINT MAP BASED ON THE HIERARCHICAL CONFLATION OF OPEN DATASETS: THE DIGITAL BUILDING STOCK MODEL - DBSM

This paper presents a hierarchical conflation process applied to open datasets for the creation of a seamless pan-European map of building footprints in vector format, named Digital Building Stock Model – DBSM. The objective is the sequential addition of input components (which currently include OpenStreetMap, Microsoft GlobalML Building Footprints, European Settlement Map), taking into account their limitations, and aiming at the highest level of completeness possible, for planning and evaluating energy transition scenarios at the EU level. The results indicate how DBSM compares robustly against cadastral data from Estonia, used as reference area. The comparison of DBSM with GHS-BUILT-S, a 10 metres resolution grid with worldwide coverage that encodes the built-up surface in each pixel as derived from Sentinel-2 imagery for the year 2018, reveals a relative overestimation of the latter, factored by 0.68 at the EU scale for a sound match

VerdictDB: Universalizing Approximate Query Processing

Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products) with implementing standard features. Additionally, the few AQP engines that are available are each tied to a specific platform and require users to completely abandon their existing databases---an unrealistic expectation given the infancy of the AQP technology. Therefore, we argue that a universal solution is needed: a database-agnostic approximation engine that will widen the reach of this emerging technology across various platforms. Our proposal, called VerdictDB, uses a middleware architecture that requires no changes to the backend database, and thus, can work with all off-the-shelf engines. Operating at the driver-level, VerdictDB intercepts analytical queries issued to the database and rewrites them into another query that, if executed by any standard relational engine, will yield sufficient information for computing an approximate answer. VerdictDB uses the returned result set to compute an approximate answer and error estimates, which are then passed on to the user or application. However, lack of access to the query execution layer introduces significant challenges in terms of generality, correctness, and efficiency. This paper shows how VerdictDB overcomes these challenges and delivers up to 171$\times$ speedup (18.45$\times$ on average) for a variety of existing engines, such as Impala, Spark SQL, and Amazon Redshift, while incurring less than 2.6% relative error. VerdictDB is open-sourced under Apache License.Comment: Extended technical report of the paper that appeared in Proceedings of the 2018 International Conference on Management of Data, pp. 1461-1476. ACM, 201