The Rebirth of Urban Subcenters: How Subway Expansion Impacts the Spatial Structure and Mix of Amenities in European Cities

Why do some neighborhoods thrive, and others do not? While the importance of the local amenity mix has been established as a key determinant of local livability, its link to urban transport infrastructure remains understudied, partially due to a lack of data. Using spatiotemporal social media data from Foursquare, we analyze the impact of metro stations which opened between 2014 and 2017 on the amenity mix of surrounding neighborhoods in nine European cities: Rome, Milan, Barcelona, Budapest, Warsaw, Sofia, Vienna, Helsinki, and Stuttgart. Thereby, we study three properties of the local amenity mix: its density, multifunctionality, and the heterogeneity between amenity types. For this purpose, we propose a new measurement of multifunctionality, which calculates the entropy of the locally present amenity set incorporating the degree of similarity between amenity types. For causal inference, we use Difference-in-Difference Regression based on Propensity Score Matching and Entropy Balancing. Our findings show that in most cities, subway expansion had a significant positive impact on the local amenity density and multifunctionality and that especially the social amenities—Arts & Entertainment, Restaurants and Nightlife—responded strongly. Moreover, considerable agglomeration forces seem to prevail, causing existing subcenters to benefit most from new metro stations

GenSynthPop : generating a spatially explicit synthetic population of individuals and households from aggregated data

Peer reviewe

Agent-based Modeling of Urban Exposome Interventions: Prospects, Model Architectures and Methodological Challenges

With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on livability, health behaviors and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen, impacts. Hence, it is crucial to assess the health impact, cost-effectiveness, and social distributional impacts of possible urban exposome interventions before implementing them. Spatial agent-based modeling can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. This paper discusses model architectures and methodological challenges for successfully modeling urban exposome interventions using spatial agent-based modeling. We review the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and social costs benefit analysis; strategies for model calibration. Major challenges for a successful application of agent-based modeling to urban exposome intervention assessment are (1) the design of realistic behavioral models that can capture different types of exposure and that respond to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure-response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting agent-based model. Although challenges exist, strategies are proposed to improve the implementation of ABM in exposome research

Validating and constructing behavioral models for simulation and projection using automated knowledge extraction

Human behavior may be one of the most challenging phenomena to model and validate. This paper proposes a method for automatically extracting and compiling evidence on human behavior determinants into a knowledge graph. The method (1) extracts associations of behavior determinants and choice options in relation to study groups and moderators from published studies using Natural Language Processing and Deep Learning, (2) synthesizes the extracted evidence into a knowledge graph, and (3) sub-selects the model components and relationships that are relevant and robust. The method can be used to either (4a) construct a structurally valid simulation model before proceeding with calibration or (4b) to validate the structure of existing simulation models. To demonstrate the feasibility of the method, we discuss an example implementation with mode of transport as behavior choice. We find that including non-frequently studied significant behavior determinants drastically improves the model's explanatory power in comparison to only including frequently studied variables. The paper serves as a proof-of-concept which can be reused, extended or adapted for various purposes

Validating and constructing behavioral models for simulation and projection using automated knowledge extraction

Human behavior may be one of the most challenging phenomena to model and validate. This paper proposes a method for automatically extracting and compiling evidence on human behavior determinants into a knowledge graph. The method (1) extracts associations of behavior determinants and choice options in relation to study groups and moderators from published studies using Natural Language Processing and Deep Learning, (2) synthesizes the extracted evidence into a knowledge graph, and (3) sub-selects the model components and relationships that are relevant and robust. The method can be used to either (4a) construct a structurally valid simulation model before proceeding with calibration or (4b) to validate the structure of existing simulation models. To demonstrate the feasibility of the method, we discuss an example implementation with mode of transport as behavior choice. We find that including non-frequently studied significant behavior determinants drastically improves the model's explanatory power in comparison to only including frequently studied variables. The paper serves as a proof-of-concept which can be reused, extended or adapted for various purposes

Premature stroke and cardiovascular risk in primary Sjögren's syndrome

IntroductionPrimary Sjögren's syndrome (pSS) is associated with an increased prevalence of traditional risk factors and cardiovascular diseases (CVDs). The study aimed to identify specific risk factors for CVD in pSS patients.MethodsPSS patients with and without CVD were compared. All patients fulfilled the EULAR/ACR classification criteria. Patients with CVD presented at least one of the following manifestations: myocardial infarction, transient ischemic attacks, ischemic or hemorrhagic stroke, peripheral artery disease, coronary artery disease, and carotid plaques. Data were collected by a standardized protocol and review of medical records.Results61/312 (19.6%) pSS patients presented with CVD. Traditional risk factors such as hypertension, hypercholesterinemia and diabetes (p < 0.05), pSS manifestations, in particular vasculitis (p = 0.033) and Raynaud's phenomenon (p = 0.018) were associated with CVD. Among patients with ischemic events (28/312, 9%), particularly cerebrovascular disease (n = 12/28, 42.9%), correlations with increased EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) (p = 0.039) and EULAR Sjögren's Syndrome Patient Reported Index (ESSPRI) (p = 0.048) were observed. Age at first cerebrovascular event was 55.2 [48.9–69.6] years. Multivariate analysis confirmed hypertension [odds ratio (OR) 3.7, 95% confidence interval (CI) 1.87–7.18, p < 0.001], hypercholesterinemia (OR 3.1, 95% CI 1.63–5.72, p < 0.001), male gender (OR 0.4, 95% CI 0.17–0.78, p = 0.009), Raynaud's phenomenon (OR 2.5, 95% CI 1.28–4.82, p = 0.007), and CNS involvement (OR 2.7, 95% CI 1.00–7.15, p = 0.048) as independent CVD predictors.ConclusionRaynaud's phenomen as well as vasculitis and high ESSDAI have shown a significant association to CVD. PSS patients with cerebrovascular events were younger than expected. Knowledge about risk factors may help clinicians to identify pSS patients at risk for CVD. After diagnosis of pSS, patients should be screened for risk factors such as hypertension and receive appropriate therapy to prevent or at least reduce sequelae such as infarction. However, further investigations are necessary in order to achieve a reliable risk stratification for these patients

Environmental risk factors of type 2 diabetes-an exposome approach

Type 2 diabetes is one of the major chronic diseases accounting for a substantial proportion of disease burden in Western countries. The majority of the burden of type 2 diabetes is attributed to environmental risks and modifiable risk factors such as lifestyle. The environment we live in, and changes to it, can thus contribute substantially to the prevention of type 2 diabetes at a population level. The ‘exposome’ represents the (measurable) totality of environmental, i.e. nongenetic, drivers of health and disease. The external exposome comprises aspects of the built environment, the social environment, the physico-chemical environment and the lifestyle/food environment. The internal exposome comprises measurements at the epigenetic, transcript, proteome, microbiome or metabolome level to study either the exposures directly, the imprints these exposures leave in the biological system, the potential of the body to combat environmental insults and/or the biology itself. In this review, we describe the evidence for environmental risk factors of type 2 diabetes, focusing on both the general external exposome and imprints of this on the internal exposome. Studies provided established associations of air pollution, residential noise and area-level socioeconomic deprivation with an increased risk of type 2 diabetes, while neighbourhood walkability and green space are consistently associated with a reduced risk of type 2 diabetes. There is little or inconsistent evidence on the contribution of the food environment, other aspects of the social environment and outdoor temperature. These environmental factors are thought to affect type 2 diabetes risk mainly through mechanisms incorporating lifestyle factors such as physical activity or diet, the microbiome, inflammation or chronic stress. To further assess causality of these associations, future studies should focus on investigating the longitudinal effects of our environment (and changes to it) in relation to type 2 diabetes risk and whether these associations are explained by these proposed mechanisms. Graphical abstract: [Figure not available: see fulltext.

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Search for heavy resonances decaying to two Higgs bosons in final states containing four b quarks

A search is presented for narrow heavy resonances X decaying into pairs of Higgs bosons (H) in proton-proton collisions collected by the CMS experiment at the LHC at root s = 8 TeV. The data correspond to an integrated luminosity of 19.7 fb(-1). The search considers HH resonances with masses between 1 and 3 TeV, having final states of two b quark pairs. Each Higgs boson is produced with large momentum, and the hadronization products of the pair of b quarks can usually be reconstructed as single large jets. The background from multijet and t (t) over bar events is significantly reduced by applying requirements related to the flavor of the jet, its mass, and its substructure. The signal would be identified as a peak on top of the dijet invariant mass spectrum of the remaining background events. No evidence is observed for such a signal. Upper limits obtained at 95 confidence level for the product of the production cross section and branching fraction sigma(gg -> X) B(X -> HH -> b (b) over barb (b) over bar) range from 10 to 1.5 fb for the mass of X from 1.15 to 2.0 TeV, significantly extending previous searches. For a warped extra dimension theory with amass scale Lambda(R) = 1 TeV, the data exclude radion scalar masses between 1.15 and 1.55 TeV

Measurement of the top quark mass using charged particles in pp collisions at root s=8 TeV

Peer reviewe