PurposeTo determine the prevalence and factors associated with computer vision syndrome in medical students at a private university in Paraguay.MethodsA survey study was conducted in 2021 in a sample of 228 medical students from the Universidad del Pacífico, Paraguay. The dependent variable was CVS, measured with the Computer Visual Syndrome Questionnaire (CVS-Q). Its association with covariates (hours of daily use of notebook, smartphone, tablet and PC, taking breaks when using equipment, use of preventive visual measures, use of glasses, etc.) was examined.ResultsThe mean age was 22.3 years and 71.5% were women. CVS was present in 82.5% of participants. Higher prevalence of CVS was associated with wearing a framed lens (PR = 1.11, 95% CI: 1.03–1.20). In contrast, taking a break when using electronic equipment at least every 20 min and every 1 h reduced 7% (PR = 0.93, 95% CI: 0.87–0.99) and 6% (PR = 0.94, 95% CI: 0.89–0.99) the prevalence of CVS, respectively.ConclusionEight out of 10 students experienced CVS during the COVID-19 pandemic. The use of framed lenses increased the presence of CVS, while taking breaks when using electronic equipment at least every 20 min and every 1 h reduced CVS.

We examine the effect of using different halo finders and merger tree building algorithms on galaxy properties predicted using the GALFORM semi-analytical model run on a high resolution, large volume dark matter simulation. The halo finders/tree builders HBT, ROCKSTAR, SUBFIND and VELOCIRAPTOR differ in their definitions of halo mass, on whether only spatial or phase-space information is used, and in how they distinguish satellite and main haloes; all of these features have some impact on the model galaxies, even after the trees are post-processed and homogenised by GALFORM. The stellar mass function is insensitive to the halo and merger tree finder adopted. However, we find that the number of central and satellite galaxies in GALFORM does depend slightly on the halo finder/tree builder. The number of galaxies without resolved subhaloes depends strongly on the tree builder, with VELOCIRAPTOR, a phase-space finder, showing the largest population of such galaxies. The distributions of stellar masses, cold and hot gas masses, and star formation rates agree well between different halo finders/tree builders. However, because VELOCIRAPTOR has more early progenitor haloes, with these trees GALFORM produces slightly higher star formation rate densities at high redshift, smaller galaxy sizes, and larger stellar masses for the spheroid component. Since in all cases these differences are small we conclude that, when all of the trees are processed so that the main progenitor mass increases monotonically, the predicted GALFORM galaxy populations are stable and consistent for these four halo finders/tree builders.

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A key ingredient for semi-analytic models (SAMs) of galaxy formation is the mass assembly history of haloes, encoded in a tree structure. The most commonly used method to construct halo merger histories is based on the outcomes of high-resolution, computationally intensive N-body simulations. We show that machine learning (ML) techniques, in particular Generative Adversarial Networks (GANs), are a promising new tool to tackle this problem with a modest computational cost and retaining the best features of merger trees from simulations. We train our GAN model with a limited sample of merger trees from the EAGLE simulation suite, constructed using two halo finders–tree builder algorithms: SUBFIND–D-TREES and ROCKSTAR–ConsistentTrees. Our GAN model successfully learns to generate well-constructed merger tree structures with high temporal resolution, and to reproduce the statistical features of the sample of merger trees used for training, when considering up to three variables in the training process. These inputs, whose representations are also learned by our GAN model, are mass of the halo progenitors and the final descendant, progenitor type (main halo or satellite) and distance of a progenitor to that in the main branch. The inclusion of the latter two inputs greatly improves the final learned representation of the halo mass growth history, especially for SUBFIND-like ML trees. When comparing equally sized samples of ML merger trees with those of the EAGLE simulation, we find better agreement for SUBFIND-like ML trees. Finally, our GAN-based framework can be utilised to construct merger histories of low and intermediate mass haloes, the most abundant in cosmological simulations.

Many previous studies using semi-analytic models have focused predominantly on improving the phenomenological prescriptions of the baryonic physics governing galaxy formation as the vast uncertainties in these processes are critical in understanding the observable Universe. On the other hand, studies dedicated to "the dark universe" imply that technical issues concerning dark matter (DM) halo merger trees can also impose other uncertainties when modeling the observable universe. We examine the effect of using four different merger tree building algorithms, SUBFIND, HBT, ROCKSTAR and VELOCIraptor, on galaxy properties using the semi-analytic model of galaxy formation and evolution GALFORM with fixed model parameters, run on the EAGLE simulation in its dark matter only version. The construction of each Halo Finder causes some to be able to find some haloes that other finders do not and vice versa affecting the total number of haloes that are found. The differences in definitions or limits to distinguish the satellite and central haloes present in the Halo Finders, produce haloes which are found in two different halo catalogs with different central/satellite classification which impacts the way in which the galaxies that reside in these haloes evolve as, by construction in GALFORM, central galaxies retain their hot gas whereas satellite galaxies do not. The results of GALFORM show that the number of central and type 1 satellite galaxies (those that are hosted by a dark matter satellite subhalo) depend directly on the halo finder and its definition for central and satellite subhalo, which results in the ROCKSTAR run having a slight excess of galaxies. In contrast the number of type 2 satellite galaxies, i.e. galaxies that are not hosted by a resolved dark matter subhalo also called orphan galaxies, depends strongly on the tree builder. The number of type 2 satellite galaxies (orphans) resulting from the VELOCIraptor run is up to ∼ 2x higher than HBT, ROCKSTAR and SUBFIND, in agreement with the excess of merged dark matter satellite subhalo progenitors of VELOCIraptor; but these are only a small fraction of the galaxy population. When matching individual GALFORM galaxies in the four finders we find that the hot gas mass MHot Gas, cold gas mass MCold Gas and instantaneous star formation rates are significantly affected by the satellite and central subhalo type. When we have a central subhalo that has a satellite subhalo as its counterpart, the satellite galaxy has MHot Gas= 0 by definition and, consequently, lower MCold Gas and instantaneous SFR for MStellar< 108h −1M than the central that it is matched to, independently of the halo finder algorithm. However, when comparing matched centrals and matched satellites, their average properties agree between the outputs from the different finders. Even though the scatter can be quite high, we find that the distributions of different baryon properties do not vary significantly between the different runs, even including orphan galaxies. Overall, the run with the strongest differences in the stellar mass function is that with HBT which contains ∼ 30% less galaxies, while in the other runs the match is excellent. Another discrepancy is that GALFORM run on VELOCIraptor is accompanied by higher star formation rate density and smaller sizes. However, in all cases the amplitude of these differences is small and insufficient to make an impact on any comparison to observational properties. The reason behind the small impact of the choice of finder lies in the homogenising effect of GALFORM in the merger trees, which ensures a small uncertainty on the model results coming from the dark matter halo finding procedure.Many previous studies using semi-analytic models have focused predominantly on improving the phenomenological prescriptions of the baryonic physics governing galaxy formation as the vast uncertainties in these processes are critical in understanding the observable Universe. On the other hand, studies dedicated to "the dark universe" imply that technical issues concerning dark matter (DM) halo merger trees can also impose other uncertainties when modeling the observable universe. We examine the effect of using four different merger tree building algorithms, SUBFIND, HBT, ROCKSTAR and VELOCIraptor, on galaxy properties using the semi-analytic model of galaxy formation and evolution GALFORM with fixed model parameters, run on the EAGLE simulation in its dark matter only version. The construction of each Halo Finder causes some to be able to find some haloes that other finders do not and vice versa affecting the total number of haloes that are found. The differences in definitions or limits to distinguish the satellite and central haloes present in the Halo Finders, produce haloes which are found in two different halo catalogs with different central/satellite classification which impacts the way in which the galaxies that reside in these haloes evolve as, by construction in GALFORM, central galaxies retain their hot gas whereas satellite galaxies do not. The results of GALFORM show that the number of central and type 1 satellite galaxies (those that are hosted by a dark matter satellite subhalo) depend directly on the halo finder and its definition for central and satellite subhalo, which results in the ROCKSTAR run having a slight excess of galaxies. In contrast the number of type 2 satellite galaxies, i.e. galaxies that are not hosted by a resolved dark matter subhalo also called orphan galaxies, depends strongly on the tree builder. The number of type 2 satellite galaxies (orphans) resulting from the VELOCIraptor run is up to ∼ 2x higher than HBT, ROCKSTAR and SUBFIND, in agreement with the excess of merged dark matter satellite subhalo progenitors of VELOCIraptor; but these are only a small fraction of the galaxy population. When matching individual GALFORM galaxies in the four finders we find that the hot gas mass MHot Gas, cold gas mass MCold Gas and instantaneous star formation rates are significantly affected by the satellite and central subhalo type. When we have a central subhalo that has a satellite subhalo as its counterpart, the satellite galaxy has MHot Gas= 0 by definition and, consequently, lower MCold Gas and instantaneous SFR for MStellar< 108h −1M than the central that it is matched to, independently of the halo finder algorithm. However, when comparing matched centrals and matched satellites, their average properties agree between the outputs from the different finders. Even though the scatter can be quite high, we find that the distributions of different baryon properties do not vary significantly between the different runs, even including orphan galaxies. Overall, the run with the strongest differences in the stellar mass function is that with HBT which contains ∼ 30% less galaxies, while in the other runs the match is excellent. Another discrepancy is that GALFORM run on VELOCIraptor is accompanied by higher star formation rate density and smaller sizes. However, in all cases the amplitude of these differences is small and insufficient to make an impact on any comparison to observational properties. The reason behind the small impact of the choice of finder lies in the homogenising effect of GALFORM in the merger trees, which ensures a small uncertainty on the model results coming from the dark matter halo finding procedure.