A travel time-based variable grid approach for an activity-based cellular automata model

Urban growth and population growth are used in numerous models to determine their potential impacts on both the natural and the socio-economic systems. Cellular automata (CA) land-use models became popular for urban growth modelling since they predict spatial interactions between different land uses in an explicit and straightforward manner. A common deficiency of land-use models is that they only deal with abstract categories, while in reality, several activities are often hosted at one location (e.g. population, employment, agricultural yield, nature…). Recently, a multiple activity-based variable grid CA model was proposed to represent several urban activities (population and economic activities) within single model cells. The distance-decay influence rules of the model included both short- and long-distance interactions, but all distances between cells were simply Euclidean distances. The geometry of the real transportation system, as well as its interrelations with the evolving activities, were therefore not taken into account. To improve this particular model, we make the influence rules functions of time travelled on the transportation system. Specifically, the new algorithm computes and stores all travel times needed for the variable grid CA. This approach provides fast run times, and it has a higher resolution and more easily modified parameters than the alternative approach of coupling the activity-based CA model to an external transportation model. This paper presents results from one Euclidean scenario and four different transport network scenarios to show the effects on land-use and activity change in an application to Belgium. The approach can add value to urban scenario analysis and the development of transport- and activity-related spatial indicators, and constitutes a general improvement of the activity-based CA model

High-resolution simulations of population-density change with an activity-based cellular automata land-use model

The MOLAND model is a cellular automata (CA) land-use change model that has often been applied to simulate urban growth. A more recent alternative model makes the simulations more multifunctional by also computing different activities (population and employment) for every cell. However, the equation to update population density in time in this activity-based CA model could not deal with high population growth rates in some existing urban centres. Therefore, we experimented with two alternative equations. A semi-automated calibration routine was used to compare errors of the different model versions at a continuous range of resolutions in two study areas: the Greater Dublin Region, Ireland, and Flanders and Brussels, Belgium. The two new population density equations turn out to solve the particular problem of fast changes in high-density neighbourhoods and generally improve regional errors in the Belgian application, but can unfortunately introduce larger errors in low-density areas or in the land-use simulations

Polynomial scaling approximations and dynamic correlation corrections to doubly occupied configuration interaction wave functions

A class of polynomial scaling methods that approximate Doubly Occupied Configuration Interaction (DOCI) wave functions and improve the description of dynamic correlation is introduced. The accuracy of the resulting wave functions is analysed by comparing energies and studying the overlap between the newly developed methods and full configuration interaction wave functions, showing that a low energy does not necessarily entail a good approximation of the exact wave function. Due to the dependence of DOCI wave functions on the single-particle basis chosen, several orbital optimisation algorithms are introduced. An energy-based algorithm using the simulated annealing method is used as a benchmark. As a computationally more affordable alternative, a seniority number minimising algorithm is developed and compared to the energy based one revealing that the seniority minimising orbital set performs well. Given a well-chosen orbital basis, it is shown that the newly developed DOCI based wave functions are especially suitable for the computationally efficient description of static correlation and to lesser extent dynamic correlation.Fil: Van Raemdonck, Mario. Ghent University; BélgicaFil: Alcoba, Diego Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Poelmans, Ward. Ghent University; BélgicaFil: De Baerdemacker, Stijn. Ghent University; BélgicaFil: Torre, Alicia. Universidad del País Vasco; EspañaFil: Lain, Luis. Universidad del País Vasco; EspañaFil: Massaccesi, Gustavo Ernesto. Universidad de Barcelona. Facultad de Física. Departamento de Física Fomental; EspañaFil: Van Neck, D.. Ghent University; BélgicaFil: Bultinck, P.. Ghent University; Bélgic

Eutrophication problems, causes and potential solutions, and exchange of reusable model building components for the integrated simulation of coastal eutrophication. ISECA Final Report D3.2

This report summarizes the stages of coastal and offshore eutrophication, followed by a description of the European indicators and institutional framework for marine eutrophication assessment. A summary is given of a number of biogeochemical models available to describe the process of eutrophication in the North Sea, and the model for atmospheric inputs which was developed in the ISECA project (see the Action 3 Report – Atmospheric Modelling for more details on this work). Furthermore, the report compares different solutions aimed at reducing the nitrogen inputs from the Scheldt basin, using the nitrogen apportionment model which was developed in the EU-FP6 project SPICOSA (www.spicosa.eu). The report is concluded with a discussion on the principles of component-based modelling and model libraries, using examples for the Scheldt model, and a general discussion on some challenges of modelling marine eutrophication

Variational two-particle density matrix calculation for the Hubbard model below half filling using spin-adapted lifting conditions

The variational determination of the two-particle density matrix is an interesting, but not yet fully explored technique that allows to obtain ground-state properties of a quantum many-body system without reference to an $N$-particle wave function. The one-dimensional fermionic Hubbard model has been studied before with this method, using standard two- and three-index conditions on the density matrix [J. R. Hammond {\it et al.}, Phys. Rev. A 73, 062505 (2006)], while a more recent study explored so-called subsystem constraints [N. Shenvi {\it et al.}, Phys. Rev. Lett. 105, 213003 (2010)]. These studies reported good results even with only standard two-index conditions, but have always been limited to the half-filled lattice. In this Letter we establish the fact that the two-index approach fails for other fillings. In this case, a subset of three-index conditions is absolutely needed to describe the correct physics in the strong-repulsion limit. We show that applying lifting conditions [J.R. Hammond {\it et al.}, Phys. Rev. A 71, 062503 (2005)] is the most economical way to achieve this, while still avoiding the computationally much heavier three-index conditions. A further extension to spin-adapted lifting conditions leads to increased accuracy in the intermediate repulsion regime. At the same time we establish the feasibility of such studies to the more complicated phase diagram in two-dimensional Hubbard models.Comment: 10 pages, 2 figure

Insulinopathies of the brain? Genetic overlap between somatic insulin-related and neuropsychiatric disorders

The prevalence of somatic insulinopathies, like metabolic syndrome (MetS), obesity, and type 2 diabetes mellitus (T2DM), is higher in Alzheimer’s disease (AD), autism spectrum disorder (ASD), and obsessive-compulsive disorder (OCD). Dysregulation of insulin signalling has been implicated in these neuropsychiatric disorders, and shared genetic factors might partly underlie this observed multimorbidity. We investigated the genetic overlap between AD, ASD, and OCD with MetS, obesity, and T2DM by estimating pairwise global genetic correlations using the summary statistics of the largest available genome-wide association studies for these phenotypes. Having tested these hypotheses, other potential brain “insulinopathies” were also explored by estimating the genetic relationship of six additional neuropsychiatric disorders with nine insulin-related diseases/traits. Stratified covariance analyses were then performed to investigate the contribution of insulin-related gene sets. Significant negative genetic correlations were found between OCD and MetS (rg = −0.315, p = 3.9 × 10−8), OCD and obesity (rg = −0.379, p = 3.4 × 10−5), and OCD and T2DM (rg = −0.172, p = 3 × 10−4). Significant genetic correlations with insulin-related phenotypes were also found for anorexia nervosa (AN), attention-deficit/hyperactivity disorder (ADHD), major depressive disorder, and schizophrenia (p < 6.17 × 10−4). Stratified analyses showed negative genetic covariances between AD, ASD, OCD, ADHD, AN, bipolar disorder, schizophrenia and somatic insulinopathies through gene sets related to insulin signalling and insulin receptor recycling, and positive genetic covariances between AN and T2DM, as well as ADHD and MetS through gene sets related to insulin processing/secretion (p < 2.06 × 10−4). Overall, our findings suggest the existence of two clusters of neuropsychiatric disorders, in which the genetics of insulin-related diseases/traits may exert divergent pleiotropic effects. These results represent a starting point for a new research line on “insulinopathies” of the brain

Подсистема автономного программно-аппаратного комплекса для индуктивного долгосрочного прогноза осредненных значений метеопараметров

The research of the inductive method of long-term (forestalling to 0,5 year) prognosis of average decade air s temperature on the basis of principle of analogies was executed and it s sufficient was shown. The research of the offered approach was also conducted: in the base of spatial models without principle of analogies; in the polynomial harmonic base; the analysis of middle quality of the inductive prognostic method for cases of the analogue principle usage and without it

Integrated molecular landscape of Parkinson’s disease

Parkinson’s disease is caused by a complex interplay of genetic and environmental factors. Although a number of independent molecular pathways and processes have been associated with familial Parkinson’s disease, a common mechanism underlying especially sporadic Parkinson’s disease is still largely unknown. In order to gain further insight into the etiology of Parkinson’s disease, we here conducted genetic network and literature analyses to integrate the top-ranked findings from thirteen published genome-wide association studies of Parkinson’s disease (involving 13.094 cases and 47.148 controls) and other genes implicated in (familial) Parkinson’s disease, into a molecular interaction landscape. The molecular Parkinson’s disease landscape harbors four main biological processes—oxidative stress response, endosomal-lysosomal functioning, endoplasmic reticulum stress response, and immune response activation—that interact with each other and regulate dopaminergic neuron function and death, the pathological hallmark of Parkinson’s disease. Interestingly, lipids and lipoproteins are functionally involved in and influenced by all these processes, and affect dopaminergic neuron-specific signaling cascades. Furthermore, we validate the Parkinson’s disease -lipid relationship by genome-wide association studies data-based polygenic risk score analyses that indicate a shared genetic risk between lipid/lipoprotein traits and Parkinson’s disease. Taken together, our findings provide novel insights into the molecular pathways underlying the etiology of (sporadic) Parkinson’s disease and highlight a key role for lipids and lipoproteins in Parkinson’s disease pathogenesis, providing important clues for the development of disease-modifying treatments of Parkinson’s disease