Miracle-dz/UniFAR-FTUs huggingface dataset:
Mindfulness supports emotional resilience in children during the COVID-19 pandemic

An important aspect of mental health in children is emotional resilience, the capacity to adapt to, and recover from, stressors and emotional challenges. Variation in trait mindfulness, ones disposition to attend to experiences with an open and nonjudgmental attitude, may be an important individual difference in children that supports emotional resilience. In this study, we investigated whether trait mindfulness was related to emotional resilience in response to stressful changes in education and home-life during the COVID-19 pandemic in the United States. We conducted a correlational study examining self-report data from July 2020 to February 2021, from 163 eight-to-ten-year-old children living in the US. Higher trait mindfulness scores correlated with less stress, anxiety, depression, and negative affect in children, and lower ratings of COVID-19 impact on their lives. Mindfulness moderated the relationship between COVID-19 child impact and negative affect. Children scoring high on mindfulness showed no correlation between rated COVID-19 impact and negative affect, whereas those who scored low on mindfulness showed a positive correlation between child COVID-19 impact and negative affect. Higher levels of trait mindfulness may have helped children to better cope with a wide range of COVID-19 stressors. Future studies should investigate the mechanisms by which trait mindfulness supports emotional resilience in children.
Galaxy Groups Within 3500 km s−1

A study of the group properties of galaxies in our immediate neighborhood provides a singular opportunity to observationally constrain the halo mass function, a fundamental characterization of galaxy formation. Detailed studies of individual groups have provided the coefficients of scaling relations between a proxy for the virial radius, velocity dispersion, and mass that usefully allow groups to be defined over the range M⊙. At a second hierarchical level, associations are defined as regions around collapsed halos extending to the zero-velocity surface at the decoupling from cosmic expansion. The most remarkable result of the study emerges from the construction of the halo mass function from the sample. At ∼1012 M⊙, there is a jog from the expectation Sheth-Tormen function, such that halo counts drop by a factor ∼3 in all lower mass bins.
Recherche dans les bases de données satellitaires des paysages et application au milieu urbain : clustering, consensus et catégorisation

Remote sensed satellite images have found a wide application for analysing and managing natural resources and human activities. Satellite images of high resolution, e. G. , SPOT5, have large sizes and are very numerous. This gives a large interest to develop new theoretical aspects and practical tools for satellite image mining. The objective of the thesis is unsupervised satellite image mining and includes three main parts. In the first part of the thesiswe demonstrate content of high resolution optical satellite images. We describe image zones by texture and geometrical features. Unsupervised clustering algorithms are presented in the second part of the thesis. A review of validity criteria and information measures is given in order to estimate the quality of clustering solutions. A new criterion based on Minimum Description Length (MDL) is proposed for estimating the optimal number of clusters. In addition, we propose a new kernel hierarchical clustering algorithm based on kernel MDL criterion. A new method of ”clustering combination” is presented in the thesis in order to benefit from several clusterings issued from different algorithms. We develop a hierarchical algorithm to optimise the objective function based on a co-association matrix. A second method is proposed which converges to a global solution. We prove that the global minimum may be found using the gradient density function estimation by the mean shift procedure. Advantages of this method are a fast convergence and a linear complexity. In the third part of the thesis a complete protocol of unsupervised satellite images mining is proposed. Different clustering results are represented via semantic relations between concepts.
The Number of Dwarf Satellites of Disk Galaxies versus their Bulge Mass in the Standard Model of Cosmology

There is a correlation between the bulge mass of the three main galaxies of the Local Group (LG), i.e., M31, Milky Way (MW), and M33, and the number of their dwarf spheroidal galaxies. A similar correlation has also been reported for spiral galaxies with comparable luminosities outside the LG. These correlations do not appear to be expected in standard hierarchical galaxy formation. In this paper, and for the first time, we present a quantitative investigation of the expectations of the standard model of cosmology for this possible relation using a galaxy catalog based on the Millennium-II simulation. Our main sample consists of disk galaxies at the centers of halos with a range of virial masses similar to M33, MW, and M31. For this sample, we find an average trend (though with very large scatter) similar to that observed in the LG; disk galaxies in heavier halos on average host heavier bulges and a larger number of satellites. In addition, we study sub-samples of disk galaxies with very similar stellar or halo masses (but spanning a range of 2–3 orders of magnitude in bulge mass) and find no obvious trend in the number of satellites versus bulge mass. We conclude that, while for a wide galaxy mass range a relation arises (which seems to be a manifestation of the satellite number–halo mass correlation), for a narrow range there is no relation between number of satellites and bulge mass in the standard model. Further studies are needed to better understand the expectations of the standard model for this possible relation.
Lattice distortion and magnetic ground state of YTiO 3 and LaTiO 3

Effects of lattice distortion on the magnetic ground state of ${\mathrm{YTiO}}_{3}$ and ${\mathrm{LaTiO}}_{3}$ are investigated on the basis of accurate tight-binding parametrization of the ${t}_{2g}$ electronic structure extracted from the local-density approximation. The complexity of these compounds is related with the fact that the ${t}_{2g}$-level splitting, caused by lattice distortions, is comparable with the energies of superexchange and spin-orbit interactions. Therefore, all these interactions are equally important and should be treated on an equal footing. The Hartree-Fock approximation fails to provide a coherent description simultaneously for ${\mathrm{YTiO}}_{3}$ and ${\mathrm{LaTiO}}_{3},$ and it is essential to go beyond.
Analysis of the Lagrangian path structures in fluid turbulence

Because in the Lagrangian frame the time scale separation has a stronger Reynolds number dependence than the length scale case in the Eulerian frame, it is more difficult to reveal inertial range scaling laws, as predicted from dimensional arguments. The present work introduces a newly defined trajectory segment structure to tentatively understand Lagrangian statistics. When a fluid particle evolves in space, its Lagrangian trajectory encounters regions of different dynamics, which can be characterized by the magnitude of material acceleration, i.e., |a|, in certain time span. The extrema of |a| are considered as the representative markers along the Lagrangian trajectories. A trajectory segment is defined as the part bounded by two adjacent extrema of |a|. The time difference and magnitude of the velocity difference at the two ends of each segment are chosen as the characteristic parameters. It shows that such structure reveals interesting turbulence physics, such as the scaling of the structure functi.
Nodal quasiparticle dispersion in strongly correlated d-wave superconductors

We analyze the effects of a $\mathbf{k}$-dependent self-energy on the photoemission momentum distribution curve (MDC) line shape and dispersion and, in particular, show that this modifies the ``high-energy'' dispersion ${v}_{F}^{\mathrm{high}}$ of the MDC's in an essential way. These general results are illustrated by a detailed examination of nodal quasiparticles in the superconducting state of the high-${T}_{c}$ cuprates. Using variational results for the nodal quasiparticle weight Z, which vanishes with the hole doping x, and the low energy dispersion ${v}_{F}^{\mathrm{low}},$ which is independent of x, we show that the ``high-energy'' dispersion ${v}_{F}^{\mathrm{high}}{=v}_{F}^{\mathrm{low}}/Z$ is much larger than the band structure value and exhibits a striking doping dependence in good agreement with recent data. These results provide strong evidence for an electronic origin of all the nodal dispersion anomalies, including the much-studied kink.
Proposing FDA consideration for the treatment and prophylaxis of opioid and psychostimulant abuse to incorporate the induction of DNA guided dopamine homeostasis: Anti-reward deficiency restoration solution (ARDS)

Background: In face of an American opioid/psychostimulant crisis with overdose fatalities, due, in part, to the COVOD 19 pandemic, we are proposing a paradigm shift in response. Currently, The FDA has approved pharmaceuticals for the treatment of opioids, alcohol, and nicotine but not for psychostimulants or even cannabis. Proposition: To respond to the deadly overdose issue globally, we are proposing that the FDA embrace, for the treatment and prophylaxis of opioid and psychostimulant abuse, induction of DNA-guided, dopamine homeostasis. We refer to this novel therapeutic target as the Anti-Reward Deficiency Restoration Solution (ARDS). Expert opinion: This futuristic proposal regarding the FDA will provide important information that may ultimately lead to significant improvement in the recovery of individuals with opioid/psychostimulant and polydrug abuse issues, especially, those with genetically-induced dopamine deficiency. Conclusion: With large populations supporting these initial results, and possibly even additional candidate genes and single nucleotide polymorphisms, the neuroscience and neurological community may eventually have the clinical ability to classify addiction severity, according to genotype and possession of risk alleles. A promising goal is the identification of high risk vulnerability, along with the provision of a safe, non-addicting ARDS natural nutrigenomic, involving a therapeutic model that potentially up-regulates instead of down-regulates dopaminergic receptors, preferably, the D2 subtype, is one laudable goal.
Providing a detailed estimate of mortality using a simulation-based collision risk model

Marine renewables could form a significant part of the green energy mix. However, a potential environmental impact of tidal energy converters (TECs) is collision risk between a device and animal, which has been a significant barrier in the consenting process. While it is important to understand the number of collisions of an animal with the device, the relative speed at which an animal collides with the device, and the point on the animal where collision occurs, will determine whether a collision is fatal, which is important in understanding population-level impacts. Using a simulation-based collision risk model, this paper demonstrates a novel method for producing estimates of mortality. Extracting both the speed and the location of collisions between an animal and TEC, in this instance a seal and horizontal axis turbine, collision speed and location of collision are used to produce probabilities of mortality. To provide a hypothetical example we quantified the speed and position at which a collision occurs to estimate mortality and, using collision position, we determine all predicted collisions with the head of the animal as fatal, for example, whilst deeming other collisions non-fatal. This is the first collision risk model to incorporate speed at the point of contact and the location where the collision occurs on the animal, to estimate the probability of mortality resulting from a collision. The hypothetical scenarios outline how these important variables extracted from the model can be used to predict the proportion of fatal events. This model enables a comprehensive approach that ultimately provides advancements in collision risk modelling for use in the consenting process of TECs. Furthermore, these methods can easily be adapted to other renewable energy devices and receptors, such as wind and birds.
Phi-meson--nuclear bound states

$\phi$-meson--nucleus bound state energies and absorption widths are calculated for seven selected nuclei by solving the Klein-Gordon equation with complex optical potentials. Essential input for the calculations, namely the medium-modified $K$ and $\overline{K}$ meson masses, as well as the density distributions in nuclei, are obtained from the quark-meson coupling model. The attractive potential for the $\phi$-meson in the nuclear medium originates from the in-medium enhanced $K\overline{K}$ loop in the $\phi$-meson self-energy. The results suggest that the $\phi$-meson should form bound states with all the nuclei considered. However, the identification of the signal for these predicted bound states will need careful investigation because of their sizable absorption widths.
Diabetes medications and associations with Covid-19 outcomes in the N3C database: A national retrospective cohort study

Background While vaccination is the most important way to combat the SARS-CoV-2 pandemic, there may still be a need for early outpatient treatment that is safe, inexpensive, and currently widely available in parts of the world that do not have access to the vaccine. There are in-silico, in-vitro, and in-tissue data suggesting that metformin inhibits the viral life cycle, as well as observational data suggesting that metformin use before infection with SARS-CoV2 is associated with less severe COVID-19. Previous observational analyses from single-center cohorts have been limited by size. Methods Conducted a retrospective cohort analysis in adults with type 2 diabetes (T2DM) for associations between metformin use and COVID-19 outcomes with an active comparator design of prevalent users of therapeutically equivalent diabetes monotherapy: metformin versus dipeptidyl-peptidase-4-inhibitors (DPP4i) and sulfonylureas (SU). This took place in the National COVID Cohort Collaborative (N3C) longitudinal U.S. cohort of adults with +SARS-CoV-2 result between January 1 2020 to June 1 2021. Findings included hospitalization or ventilation or mortality from COVID-19. Back pain was assessed as a negative control outcome. Results 6,626 adults with T2DM and +SARS-CoV-2 from 36 sites. Mean age was 60.7 +/- 12.0 years; 48.7% male; 56.7% White, 21.9% Black, 3.5% Asian, and 16.7% Latinx. Mean BMI was 34.1 +/- 7.8kg/m2. Overall 14.5% of the sample was hospitalized; 1.5% received mechanical ventilation; and 1.8% died. In adjusted outcomes, compared to DPP4i, metformin had non-significant associations with reduced need for ventilation (RR 0.68, 0.32–1.44), and mortality (RR 0.82, 0.41–1.64). Compared to SU, metformin was associated with a lower risk of ventilation (RR 0.5, 95% CI 0.28–0.98, p = 0.044) and mortality (RR 0.56, 95%CI 0.33–0.97, p = 0.037). There was no difference in unadjusted or adjusted results of the negative control. Conclusions There were clinically significant associations between metformin use and less severe COVID-19 compared to SU, but not compared to DPP4i. New-user studies and randomized trials are needed to assess early outpatient treatment and post-exposure prophylaxis with therapeutics that are safe in adults, children, pregnancy and available worldwide.
Computational analysis of 5-fluorouracil anti-tumor activity in colon cancer using a mechanistic pharmacokinetic/pharmacodynamic model

5-Fluorouracil (5-FU) is a standard chemotherapeutic agent to treat solid cancers such as breast, colon, head, and neck. Computational modeling plays an essential role in predicting the outcome of chemotherapy and developing optimal dosing strategies. We developed an integrated mechanistic pharmacokinetics/pharmacodynamics (PK/PD) model examining the influence of 5-FU, as an S-phase specific double-strand break (DSB)-inducing agent, on tumor proliferation. The proposed mechanistic PK/PD model simulates the dynamics of critical intermediate components and provides the accurate tumor response prediction. The integrated model is composed of PK, cellular, and tumor growth inhibition (TGI) sub-models, quantitatively capturing the essential drug-related physiological processes. In the cellular model, thymidylate synthase (TS) inhibition, resultant deoxynucleoside triphosphate (dNTP) pool imbalance, and DSB induction are considered, as well as 5-FU incorporation into RNA and DNA. The amount of 5-FU anabolites and DSBs were modeled to drive the kinetics of the pharmacological tumor response. Model parameters were estimated by fitting to literature data. Our simulation results successfully describe the kinetics of the intermediates regulating the 5-FU cytotoxic events and the pattern of tumor suppression. The comprehensive model simulated the tumor volume change under various dose regimens, and its generalizability was attested by comparing it with literature data. The potential causes of the tumor resistance to 5-FU are also investigated through Monte Carlo analysis. The simulation of various dosage regimens helps quantify the relationship between treatment protocols and chemotherapy potency, which will lead to the development of efficacy optimization.
Psychological distress, employment, and family functioning during the COVID-19 outbreak among recent immigrant families in Israel: Moderating roles of COVID-19 prevalence

Grounded in an ecodevelopment perspective, in the current study we examined unique and moderating effects of daily COVID-19 prevalence (social contexts) on effects of COVID-19 related risk and protective factors such as emotional distress (individual contexts) and employment (working from home and unemployment status; family contexts) on family functioning among 160 recent immigrant families in Israel. In general, results indicate several unique effects of COVID-19 related factors (such as COVID-19 emotional distress, unemployment, and remote work arrangements) on both parents’ and adolescents’ reports of family functioning. However, results indicated that there were more significant associations between COVID-19 factors (e.g., emotional distress and COVID-19 prevalence) and family functioning indicators with adolescents, than with parents. The effects of COVID-19 factors (e.g., emotional distress and remote work arrangements) were moderated by daily COVID-19 prevalence (new cases and deaths). We discuss ways in which interventionists can contribute to pandemic-related research to promote optimal family functioning among immigrant families.
Impact of liquidity spillovers among industrial sectors on stock markets during crisis periods: Evidence from the S&P 500 index

We investigate liquidity spillovers among industry sectors in the S&P 500 index to explain the interconnection dynamics in the US stock market. To do so, we define a sectoral liquidity measure based on the Amihud liquidity measure. Employing the spillover model, we further examine US sectors’ liquidity spillovers during the global financial crisis (GFC) and the COVID-19 pandemic. Based on the relationship between liquidity in financial markets and business cycles, our findings show that (i) liquidity connections became stronger during both crises, (ii) in the GFC period, the material sector was the primary transmitter of total liquidity spillovers, whereas in the COVID-19 pandemic period, the consumer discretionary sector was the main conveyor of total liquidity spillovers and the real estate sector was the dominant recipient of total liquidity spillovers, and (iii) net liquidity spillovers between all sectors fluctuated notably during the GFC, while the industrial, consumer staples, and healthcare sectors had the largest net liquidity spillovers during the COVID-19 crisis. These findings have important implications for portfolio managers and policymakers.
A Calculus of Parallel Continuations and its Monad Models(Concurrency Theory and Applications '96)

The importance of continuation is increasing in computer science. Because continuation is an abstraction of single thread, however, we need a substantial extension of continuation in order to directly manipulate parallelism using continuation. In this paper, we $ \mathrm{r}$ -deﬁne continuation with anonymous returns and compositions which make the algebra of continuations tractable. We give a monad model to a subclass of continuations. Moreover, we introduce a parallel construct to continuations, i.e., extension with pairing $( \mathrm{t}^{-}$ , – $)$ ). We show that the synchronization can be represented in this framework. We also give its model in monoidal categories. Furthermore, we discuss a gap between cartesian categories in which value-based model can be constructed, and monoidal categories in which parallel continuations can be represented.
An Introduction to Generalized Linear Mixed Models

The generalized linear mixed model (GLMM) generalizes the standard linear model in three ways: accommodation of non-normally distributed responses, specification of a possibly non-linear link between the mean of the response and the predictors, and allowance for some forms of correlation in the data. As such, GLMMs have broad utility and are of great practical importance. Two special cases of the GLMM are the linear mixed model (LMM) and the generalized linear model (GLM). Despite the utility of such models, their use has been limited due to the lack of reliable, well-tested estimation and testing methods. I first describe and give examples of GLMMs and then discuss methods of estimation including maximum likelihood, generalized estimating equations, and penalized quasi-likelihood. Finally I briefly survey current research efforts in GLMMs.
Study of a New Target Design with an Additional Horn for NuMI Beam

This paper describes the simulation studies done for the standard NOvA target and the proposed new minimal NOvA target design, in forward and reverse horn current for studying the neutrino and antineutrino event yield and the background contamination at the near and far detector. The standard NOvA target is segmented into 48 graphite segments (fins) with two magnetic horns, Horn 1 placed at MCZERO and Horn 2 placed at 19.18 m from MCZERO. The new minimal target design has 24 graphite fins extended into Horn 1. It shows the study for the position optimization of the Horn 2 relative to Horn 1 for the standard and the new minimal target design. The effect of introducing an additional horn (identical to Horn 2) with the new minimal target design, on the neutrino yield and the energy spectra, has also been explored.
Enhancing entanglement generation of two atoms in a cavity with white noise using classical driving fields

We investigate the entanglement dynamics of a quantum system consisting of two two-level atoms in a cavity with classical driving fields in the presence of white noise. The cavity is initially prepared in the vacuum state. Generally, the entanglement of two atoms decreases with the intensity of the thermal fields and the coupling strength of the two-level atoms to the thermal fields. However, we find that the entanglement of the quantum system can be enhanced by adjusting the frequency and the strength of the classical driving fields in the presence of white noise.