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3. Development and evaluation of an improved offline aerosol mass spectrometry techniqueChristina Vasilakopoulou, Kalliopi Florou, Christos Kaltsonoudis, Iasonas Stavroulas, Nikolaos Mihalopoulos, Spyros N. Pandis, 2023, original scientific article Abstract: Abstract. The offline aerosol mass spectrometry technique is
a useful tool for the source apportionment of organic aerosol (OA) in areas
and periods during which an aerosol mass spectrometer (AMS) is not available. However, the technique is
based on the extraction of aerosol samples in water, while several
atmospheric OA components are partially or fully insoluble in water. In this work an improved offline technique was developed and evaluated in an effort to capture most of the partially soluble and insoluble organic aerosol material, reducing significantly the uncertainty of the corresponding source
apportionment. A major advantage of the proposed approach is that no
corrections are needed for the offline analysis to account for the limited
water solubility of some OA components. The improved offline AMS analysis
was tested in three campaigns: two during winter and one during summer.
Collocated online AMS measurements were performed for the evaluation of the offline method. Source apportionment analysis was performed separately for the online and the offline measurements using positive matrix
factorization (PMF). The PMF results showed that the fractional contribution of each factor to the total OA differed between the online and the offline PMF results by less than 15 %. The differences in the AMS spectra of the
factors of the two approaches could be significant, suggesting that the use
of factor profiles from the literature in the offline analysis may lead to
complications. Part of the good agreement between the online and the
offline PMF results is due to the ability of the improved offline AMS
technique to capture a bigger part of the OA, including insoluble organic
material. This was evident by the significant fraction of submicrometer
suspended insoluble particles present in the water extract and by the
reduced insoluble material on the filters after the extraction process. More than half of the elemental carbon (EC) was on average missing from the
filters after the water extraction. Significant EC concentrations were
measured in the produced aerosol that was used as input to the AMS during
the offline analysis.
Keywords: organic aerosol, receptor modeling, offline PMF, Greece
Published in RUNG: 10.05.2024; Views: 372; Downloads: 2
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4. Affinity maturation of antibody fragments : a review encompassing the development from random approaches to computational rational optimizationJiaqi Liu, Guangbo Kang, Jiewen Wang, Haibin Yuan, Yili Wu, Shuxian Meng, Ping Wang, Miao Zhang, Yuli Wang, Yuanhang Feng, He Huang, Ario De Marco, 2023, review article Keywords: deep learning, protein modeling, random mutagenesis, rational mutagenesis
Published in RUNG: 17.07.2023; Views: 1255; Downloads: 7
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6. Comparative analysis of epidemiological models for COVID-19 pandemic predictionsRajan 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: 2607; Downloads: 33
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7. Effect of Humanizing Mutations on the Stability of the Llama Single-Domain Variable RegionMiguel Soler, Barbara Medagli, Jiewen Wang, Sandra Oloketuyi, Gregor Bajc, He Huang, Sara Fortuna, Ario De Marco, 2021, original scientific article Keywords: nanobody framework, modeling, nanobody humanization, CDR grafting
Published in RUNG: 27.01.2021; Views: 2668; Downloads: 65
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8. A study of stellar debris dynamics during a tidal disruption eventAurora 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: 3984; Downloads: 86
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