Computational insight into materials properties

The aim of the work was to explore the practical applicability of molecular dynamics at different length and time scales. From nanoparticles system over colloids and polymers to biological systems like membranes and finally living cells, a broad range of materials was considered from a theoretical standpoint. In this dissertation five chemistry-related problem are addressed by means of theoretical and computational methods. The main results can be outlined as follows. (1) A systematic study of the effect of the concentration, chain length, and charge of surfactants on fullerene aggregation is presented. The long-discussed problem of the location of C60 in micelles was addressed and fullerenes were found in the hydrophobic region of the micelles. (2) The interactions between graphene sheet of increasing size and phospholipid membrane are quantitatively investigated. (3) A model was proposed to study structure, stability, and dynamics of MoS2, a material well-known for its tribological properties. The telescopic movement of nested nanotubes and the sliding of MoS2 layers is simulated. (4) A mathematical model to gain understaning of the coupled diffusion-swelling process in poly(lactic-co-glycolic acid), PLGA, was proposed. (5) A soft matter cell model is developed to explore the interaction of living cell with artificial surfaces. The effect of the surface properties on the adhesion dynamics of cells are discussed

2000 land-use regressions for road traffic noise predictions – how sample selection affects extrapolation weights

The awareness that noise exposure is critical for human health is growing around the globe, and land-use regressions (LURs) are becoming a popular tool for producing noise exposure maps. One important factor for noise emissions is road traffic. The propagation in this regard is determined by the spatial layout of road infrastructure and the surrounding environment, respectively. LURs use geostatistical models and allow to extrapolate microphone measurements. In this study, we investigated whether models are prone to sampling artifacts. We used yearly averaged Lden simulations, compliant to the European noise directive 2002/49/EG, as input for 2000 virtual field campaigns. We permuted different sampling schemes (random, systematic, stratified) and sizes (n = 50, 100, 200, 500 to 1000) 100 times. The overall model performances varied substantially between 0.61 – 0.95 for R², 1.94 – 7.46 dB(A) for mean absolute error and 2.47 – 10.03 dB(A) for root mean squared error. Comparing the eventual model terms using variance analyses (ANOVA), we found significant differences between the sampling schemes for traffic information and land cover (e.g. vegetated surfaces) features. Simultaneously, less than half of the LURs’ weights differed significantly depending on the sampling size. Thus, our experiments give an in-depth view on the mechanics of LUR and their sensitivity with respect to sampled training data

The CMS Drift Tube Trigger Track Finder

Muons are among the decay products of many new particles that may be discovered at the CERN Large Hadron Collider. At the first trigger level the identification of muons and the determination of their transverse momenta and location is performed by the Drift Tube Trigger Track Finder in the central region of the Compact Muon Solenoid experiment, using track segments detected in the Drift Tube muon chambers. Track finding is performed both in pseudorapidity and azimuth. Track candidates are ranked and sorted, and the best four are delivered to the subsequent high level trigger stage. The concept, design, control and simulation software as well as the expected performance of the system are described. Prototyping, production and tests are also summarized

A machine learning framework for cardiovascular health prediction modeling the interplay between various environmental, neighborhood and socio-economic features: a German-wide application

Environmental exposures and socio-economic neighborhood characteristics have a major impact on human health and well-being. However, little is known about their interplay. Machine Learning (ML) methodologies go beyond the conventional statistical approaches and help us towards identifying the driving contextual factors and assessing their predictive ability for various health outcomes even under high complexity. In this study, we first compared multiple ML techniques, from neighbor-based to deep learning approaches for the prediction of cardiovascular disease (CVD) mortality in 5×5 km grid cells across Germany during 2017. The models performed well in the training phase [R² ≥ 0.85, mean squared error (MSE) ≤ 0.005], and moderate to well in the testing set (0.27 ≤ R² ≤ 0.66, 0.011 ≤ MSE ≤ 0.024). All models were highly correlated (0.69 ≤ Spearman r ≤ 0.82) and identified similar predictors as the main drivers for CVD mortality (e.g., the deprivation index, proportion of foreigners and air pollution), though prediction maps indicated spatial heterogeneity across the country. Currently, we aim to extend this analysis on the prediction of hypertension, an important risk factor for CVD morbidity and mortality, by using advanced and highly resolved environmental maps and recent health data from the largest German cohort, the NAKO study. The work is still in progress and the results will be presented at the conference

Un lampo obliquo. Luigi Bernardi, i suoi libri e il suo immaginario

In occasione del decennale della morte di Luigi Bernardi (Ozzano dell’Emilia, 1953 - Bologna, 2013), il volume - a cura di Filippo Milani e Alberto Sebastiani - raccoglie gli atti relativi all’evento di inaugurazione del Fondo “Luigi Bernardi”, che si è svolto il 17 gennaio 2020 presso gli spazi del Dipartimento di Filologia classica e Italianistica dell’Università di Bologna, poche settimane prima che la pandemia sconvolgesse le vite di tutte e tutti noi. Si è trattato della prima iniziativa volta a valorizzare il Fondo archivistico e librario dello scrittore emiliano, che merita di certo ulteriori studi e approfondimenti, volti a indagare sia l’attività creativa di Bernardi sia quella di editore, traduttore, promotore culturale e scopritore di talenti letterari e fumettistici, soprattutto nell’ambito del genere noir. In quell’occasione era stata allestita anche una mostra virtuale - tuttora accessibile - che offre una prima visione panoramica sugli interessi dell’autore e sulla composizione della sua biblioteca

A facility to Search for Hidden Particles (SHiP) at the CERN SPS

A new general purpose fixed target facility is proposed at the CERN SPS accelerator which is aimed at exploring the domain of hidden particles and make measurements with tau neutrinos. Hidden particles are predicted by a large number of models beyond the Standard Model. The high intensity of the SPS 400~GeV beam allows probing a wide variety of models containing light long-lived exotic particles with masses below ${\cal O}$(10)~GeV/c$^2$, including very weakly interacting low-energy SUSY states. The experimental programme of the proposed facility is capable of being extended in the future, e.g. to include direct searches for Dark Matter and Lepton Flavour Violation.Comment: Technical Proposa

Search for the Standard Model Higgs Boson with the OPAL Detector at LEP

This paper summarises the search for the Standard Model Higgs boson in e+e- collisions at centre-of-mass energies up to 209 GeV performed by the OPAL Collaboration at LEP. The consistency of the data with the background hypothesis and various Higgs boson mass hypotheses is examined. No indication of a signal is found in the data and a lower bound of 112.7GeV/C^2 is obtained on the mass of the Standard Model Higgs boson at the 95% CL.Comment: 51 pages, 21 figure