Repository of University of Nova Gorica

Search the repository
A+ | A- | Help | SLO | ENG

Query: search in
search in
search in
search in
* old and bologna study programme

Options:
  Reset


1 - 10 / 14
First pagePrevious page12Next pageLast page
1.
2.
3.
Comparative analysis of epidemiological models for COVID-19 pandemic predictions
Rajan Gupta, Gaurav Pandey, Saibal K. Pal, 2021, original scientific article

Abstract: Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt’s exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9; the values for the mean absolute percentage error were 0.005 and 6.63; and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region’s growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.
Keywords: epidemic modeling, machine learning, neural networks, pandemic forecasting, time-series forecasting
Published in RUNG: 15.07.2021; Views: 2227; Downloads: 33
URL Link to full text
This document has many files! More...

4.
5.
A study of stellar debris dynamics during a tidal disruption event
Aurora Clerici, 2020, doctoral dissertation

Abstract: The number of observed tidal disruption events is increasing rapidly with the advent of new surveys. Thus, it is becoming increasingly important to improve TDE models using different stellar and orbital parameters. We study the dynamical behaviour of tidal disruption events produced by a massive black hole like Sgr A* by changing different initial orbital parameters, taking into account the observed orbits of S stars. Investigating different types of orbits and penetration factors is important since their variations lead to different timescales of the tidal disruption event debris dynamics, making mechanisms such as self-crossing and pancaking act strongly or weakly, thus affecting the circularisation and accretion disk formation. We have performed smoothed particle hydrodynamics simulations. Each simulation consists in modelling the star with $10^5$ particles, and the density profile is described by a polytrope with $\gamma$ = 5/3. The massive black hole is modelled with a generalised post-Newtonian potential, which takes into account relativistic effects of the Schwarzschild space-time. Our analyses find that mass return rate distributions of solar-like stars and S-like stars with same eccentricity have similar durations, but S-like stars have higher mass return rate, as expected due to their larger mass. Regarding debris circularisation, we identify four types of evolution, related to the mechanisms and processes involved during circularisation: in type 1 the debris does not circularise efficiently, hence a disk is not formed or is formed after relatively long time; in type 2 the debris slowly circularises and eventually forms a disk with no debris falling back; in type 3 the debris relatively quickly circularises and forms a disk while there is still debris falling back; finally, in type 4 the debris quickly and efficiently circularises, mainly through self-crossings and shocks, and forms a disk with no debris falling back. Finally, we find that the standard relation of circularisation radius $r_{\rm circ} = 2r_{\rm t}$ holds only for $\beta = 1$ and eccentricities close to parabolic.
Keywords: 07.05.Tp Computer modeling and simulation, 95.30.Lz Hydrodynamics, 98.35.Jk Galactic center, bar, circumnuclear matter, and bulge, 98.62.Js Galactic nuclei (including black holes), circumnuclear matter, and bulges, 98.62.Mw Infall, accretion, and accretion disks
Published in RUNG: 29.09.2020; Views: 3560; Downloads: 77
.pdf Full text (37,55 MB)

6.
Nanobodies isolated from pre-immune libraries as tunable biotechnological tools
Ario De Marco, invited lecture at foreign university

Keywords: nanobodies, panning, modeling
Published in RUNG: 27.02.2020; Views: 2416; Downloads: 0
This document has many files! More...

7.
8.
Nanobodies: towards rational design of immune-reagents
Ario De Marco, 2017, published scientific conference contribution abstract (invited lecture)

Abstract: Antibodies are irreplaceable reagents in both research and clinical practice. Despite their relevance, the structural complexity of conventional mono- and polyclonal antibodies (IgG) has always been a limit for their engineering towards reagents optimized for specific applications, such as in vivo diagnostics and therapy. Furthermore, their isolation is time consuming, their production expensive, and their functionalization results often in heterogeneous macromolecule populations. These drawbacks promoted the search for both innovative antibody isolation strategies and alternative scaffolds. In vitro panning of pre-immune collections of recombinant antibody fragments allows for the simple and fast recovery of binders. Since they did not undergo somatic maturation, their affinity for targets can be insufficient but on the other hand they can be rapidly mutated by standard molecular biology techniques to generate second-generation antibodies among which to identify clones with improved characteristics. Both stochastic and rational methods have been proposed for the optimization process. Random mutagenesis followed by panning at stringent conditions has been successful used to select binders with improved physical characteristics. Rational methods try to identify in silico key residues involved in the regulation of specific antibody features, such as stability or binding affinity. The accuracy of these methods usually depends on the calculation resources. In this perspective, smaller molecules can be analyzed “better” than larger because of their restricted number of residues. Nanobodies small dimensions have been long appreciated since enable better tissue penetration, shorter clearance time, higher yields. Now it becomes evident that this characteristic makes them also optimal objects for modeling.
Keywords: recombinant antibody modeling, nanobody engineering, molecular dynamics and docking
Published in RUNG: 21.03.2018; Views: 4427; Downloads: 0
This document has many files! More...

9.
10.
Is there an approach for optimizing the specific binding of antibodies to target cells?
Ario De Marco, invited lecture at foreign university

Keywords: Nanobodies, avidity, modeling, off-target accumulation
Published in RUNG: 31.01.2017; Views: 4266; Downloads: 0
This document has many files! More...

Search done in 0.06 sec.
Back to top