Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Motivation of University Students in Online and Classroom Teaching: Insights from Teaching during Covid-19

In this paper, I present and discuss the findings of an intentional and systematic inquiry into the learning motivation of university master’s students taking the same course in online and classroom-based formats during the Covid-19 pandemic. Through capturing the students’ motivation using Keller’s Instructional Materials Motivation Survey at two points during the semester and analyzing the students’ course evaluations and the reflective reports of tutors, I uncover various factors influencing students’ motivation. Among these are the structuredness of instructions, technical difficulties, perceived effort of the teachers, novelty of tools, and the social relationships between students. I conclude my discussion with a proposition for designing motivating blends for online higher education students.
Laser-driven hole boring and gamma-ray emission in high-density plasmas

Ion acceleration in laser-produced dense plasmas is a key topic of many recent investigations thanks to its potential applications. Indeed, at forthcoming laser intensities (I ≳ 1023 W cm−2) interaction of laser pulses with plasmas can be accompanied by copious gamma-ray emission. Here we demonstrate the mutual influence of gamma-ray emission and ion acceleration during relativistic hole boring in high-density plasmas with ultra-intense laser pulses. If the gamma-ray emission is abundant, laser pulse reflection and hole-boring velocity are lower and gamma-ray radiation pattern is narrower than in the case of low emission. Conservation of energy and momentum allows one to elucidate the effects of the gamma-ray emission which are more pronounced at higher hole-boring velocities.
Half-Heusler topological insulators: A first-principles study with the Tran-Blaha modified Becke-Johnson density functional

We systematically investigate the topological band structures of half-Heusler compounds using first-principles calculations. The modified Becke-Johnson exchange potential together with local-density approximation for the correlation potential (MBJLDA) has been used here to obtain accurate band inversion strength and band order. Our results show that a large number of half-Heusler compounds are candidates for three-dimensional topological insulators. The difference between band structures obtained using the LDA and MBJLDA potential is also discussed.
Magnetotransport in mesoscopic rings and cylinders: effects of electron-electron interaction and spin-orbit coupling

Abstract We undertake an in-depth analysis of the magnetotransport properties in mesoscopic single-channel rings and multichannel cylinders within a tight-binding (TB) formalism. The main focus of this review is to illustrate how the long-standing anomalies between the calculated and measured current amplitudes carried by a small conducting ring upon the application of a magnetic flux ϕcan be removed. We discuss two different cases. First, we examine the combined effect of second-neighbor hopping integral and Hubbard correlation on the enhancement of persistent current in the presence of disorder. A significant change in current amplitude is observed compared to the traditional nearest-neighbor hopping model, and the current amplitude becomes quite comparable to experimental realizations. In the other case, we verify that in the presence of spin-orbit interaction, a considerable enhancement of persistent current amplitude takes place, and the current amplitude in a disordered ring becomes almost comparable to that of an ordered one. In addition to these, we also present the detailed band structures and some other related issues to get a complete picture of the phenomena at the microscopic level.
Tight-binding models for the iron-based superconductors

The rich materials class of iron-based superconductors turned out to exhibit a very complex electronic structure despite the simplicity of their crystal structures. For various approaches to study the instability against magnetic order or superconductivity, a real-space description of the electronic structure is required. Here, the bonding situation and the orbital structure of the electronic state are analyzed and minimum tight-binding models quantitatively correctly describing the low-energy electronic structure are provided.
A NEW TIMESCALE FOR PERIOD CHANGE IN THE PULSATING DA WHITE DWARF WD 0111+0018

We report the most rapid rate of period change measured to date for a pulsating DA (hydrogen atmosphere) white dwarf (WD), observed in the 292.9 s mode of WD 0111+0018. The observed period change, faster than 10−12 s s−1, exceeds by more than two orders of magnitude the expected rate from cooling alone for this class of slow and simply evolving pulsating WDs. This result indicates the presence of an additional timescale for period evolution in these pulsating objects. We also measure the rates of period change of nonlinear combination frequencies and show that they share the evolutionary characteristics of their parent modes, confirming that these combination frequencies are not independent modes but rather artifacts of some nonlinear distortion in the outer layers of the star.
A Kalman Filter based Approach for Markerless Pose Tracking and Assessment

The assessment and treatment of diseases that causes movement impairments typically rely upon clinical information obtained from self-reported rating scales and clinical observation from health professionals. Currently, objective clinical gait analysis requires the use of expensive cameras or wearable sensors, which may be too time-consuming for routine clinical usage. Therefore, an assessment system that can provide non-invasive gait analysis tool is desired. In this paper, we propose a cost-efficient assessment system that combines computer vision and artificial intelligence technology to analyse human gait, which can provide a basic clinical report for clinicians to evaluate the patients’ recovery to facilitate clinical decision making. And a series of experiments is taken to improve this assessment system performance. Those experiments showed that when the visibility threshold (VT) is set to a relatively high level (VT =0.4), the postprocessing part, which includes a Kalman filter and a FDF, can improve the human pose detection model (BlazePose)’s joint angle prediction accuracy by 10%. This post-processing method can be applied to other human body detection models to achieve filtering and feature extraction from joint angle signals for clinical gait analysis.
ESTIMATING THE GALACTIC MASS PROFILE IN THE PRESENCE OF INCOMPLETE DATA

A powerful method to measure the mass profile of a galaxy is through the velocities of tracer particles distributed through its halo. Transforming this kind of data accurately to a mass profile M ( r ) ? > , however, is not a trivial problem. In particular, limited or incomplete data may substantially affect the analysis. In this paper we develop a Bayesian method to deal with incomplete data effectively; we have a hybrid-Gibbs sampler that treats the unknown velocity components of tracers as parameters in the model. We explore the effectiveness of our model using simulated data and then apply our method to the Milky Way (MW) using velocity and position data from globular clusters and dwarf galaxies. We find that, in general, missing velocity components have little effect on the total mass estimate. However, the results are quite sensitive to the outer cluster Pal 3. Using a basic Hernquist model with an isotropic velocity dispersion, we obtain credible regions for the cumulative mass profile M ( r ) ? > of the MW and provide estimates for the model parameters with 95% Bayesian credible intervals. The mass contained within 260 kpc is 1.37 × 10 12 ? > M ⊙ ? > , with a 95% credible interval of ( 1.27 , 1.51 ) × 10 12 ? > M ⊙ ? > . The Hernquist parameters for the total mass and scale radius are 1.55 − 0.13 + 0.18 × 10 12 ? > M ⊙ ? > and 16.9 − 4.1 + 4.8 ? > kpc, where the uncertainties span the 95% credible intervals. The code we developed for this work, Galactic Mass Estimator (GME), will be available as an open source package in the R Project for Statistical Computing.
Quantifying Plant Species α-Diversity Using Normalized Difference Vegetation Index and Climate Data in Alpine Grasslands

Quantitative plant species α-diversity of grasslands at multiple spatial and temporal scales is important for investigating the responses of biodiversity to global change and protecting biodiversity under global change. Potential plant species α-diversity (i.e., SRp, Shannonp, Simpsonp and Pieloup: potential species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by climate data (i.e., annual temperature, precipitation and radiation) and actual plant species α-diversity (i.e., SRa, Shannona, Simpsona and Pieloua: actual species richness, Shannon index, Simpson index and Pielou index, respectively) were quantified by normalized difference vegetation index and climate data. Six methods (i.e., random forest, generalized boosted regression, artificial neural network, multiple linear regression, support vector machine and recursive regression trees) were used in this study. Overall, the constructed random forest models performed the best among the six algorithms. The simulated plant species α-diversity based on the constructed random forest models can explain no less than 96% variation of the observed plant species α-diversity. The RMSE and relative biases between simulated α-diversity based on the constructed random forest models and observed α-diversity were ≤1.58 and within ±4.49%, respectively. Accordingly, plant species α-diversity can be quantified from the normalized difference vegetation index and climate data using random forest models. The random forest models of plant α-diversity build by this study had enough predicting accuracies, at least for alpine grassland ecosystems, Tibet. The proposed random forest models of plant α-diversity by this current study can help researchers to save time by abandoning plant community field surveys, and facilitate researchers to conduct studies on plant α-diversity over a long-term temporal scale and larger spatial scale under global change.
Topical Antibiotic Prophylaxis and Intravitreal Injections: Impact on the Incidence of Acute Endophthalmitis—A Nationwide Study in France from 2009 to 2018

Background: The dramatic increase in intravitreal injections (IVTs) has been accompanied by a greater need for safer procedures. The ongoing debate about topical antibiotic prophylaxis after IVTs emphasizes the importance of large-scale studies. We aimed to study the role of topical antibiotic prophylaxis in reducing the risk of acute endophthalmitis after IVTs. Methods: Population-based cohort study, in France, from 2009 to 2018, including all French patients receiving IVTs of corticosteroids or anti-VEGF agents. Results: A total of 5,291,420 IVTs were performed on 605,434 patients. The rate of topical antibiotic prophylaxis after IVTs progressively decreased during the study period, with a sharp drop in 2014 (from 84.6% in 2009 to 27.4% in 2018). Acute endophthalmitis occurred in 1274 cases (incidence rate = 0.0241%). Although antibiotic prophylaxis did not alter the risk of endophthalmitis (p = 0.06), univariate analysis showed an increased risk after fluoroquinolone and aminoglycoside prophylaxis. This increased risk was not found in multivariate analysis. However, we observed an increased risk related to the use of fixed combinations of fluoroquinolones and aminoglycosides with corticosteroids (IRR = 1.89; 95% CI = 1.57–2.27%, antibiotics combined with corticosteroids). Conclusion: These results are consistent with the literature. Endophthalmitis rates after IVTs did not decrease with topical antibiotic prophylaxis. The use of a combination of antibiotics and corticosteroids doubles the risk of endophthalmitis and should be avoided. Avoiding antibiotic prophylaxis would reduce the costs and the potential risks of antibiotic resistance.
Decay of a Yukawa fermion at finite temperature and applications to leptogenesis

We calculate the decay rate of a Yukawa fermion in a thermal bath using finite-temperature cutting rules and effective Green's functions according to the hard thermal loop resummation technique. We apply this result to the decay of a heavy Majorana neutrino in leptogenesis. Compared to the usual approach where thermal masses are inserted into the kinematics of final states, we find that deviations arise through two different leptonic dispersion relations. The decay rate differs from the usual approach by more than 1 order of magnitude in the temperature range which is interesting for the weak washout regime. We discuss how to arrive at consistent finite-temperature treatments of leptogenesis.
Comparison of systematic randomized 12-core transrectal ultrasonography-guided prostate biopsy with magnetic resonance imaging-transrectal ultrasonography fusion-targeted prostate biopsy

We aimed to compare the complications and pathological outcomes between systematic 12-core transrectal ultrasonography guided prostate biopsy (TRUS-PB) and magnetic resonance imaging-TRUS fusion targeted prostate biopsy (MRI-TRUS FTPB). We examined 10,901 patients who underwent prostate biopsy from May 2003 to December 2017 retrospectively. Among them, 10,325 patients underwent 12-core TRUS-PB and 576 patients underwent MRI-TRUS FTPB. The clinicopathological features and complications in both groups were compared. After propensity score matching, there were no significant differences in the clinical features and complication rates between both groups (P > .05). In the multivariate analyses, the prostate volume was shown to be the only significant predictor of overall complications, infectious complications, bleeding related complications, and Clavien-Dindo grade ≥ 2 complications after prostate biopsy (P < .001). The present study demonstrates the safety of MRI-TRUS FTPB in terms of complications, compared with that of TRUS-PB. Although the combination of MRI-TRUS FTPB and 12-core TRUS-PB provides enhanced diagnostic power, MRI-TRUS FGB alone could provide a reasonable diagnostic value for prostate cancer if the apparent diffusion coefficient suspicious grade of prostate cancer is ≥4. When the Likert suspicious grade of prostate cancer on the apparent diffusion coefficient map of multiparametric MRI was 3, 13.9% (27/194) of the patients were diagnosed with clinically significant prostate cancer (csPCa); 44.4% (12/27) of them were confirmed as csPCa at the MRI-targeted cores. When the apparent diffusion coefficient suspicious grade was ≥4, 43.0% (108/251) were diagnosed with csPCa; 76.8% (83/108) of them were confirmed to have csPCa at the MRI-targeted cores.
Sieve Estimation of Time-Varying Panel Data Models With Latent Structures

We propose a heterogeneous time-varying panel data model with a latent group structure that allows the coefficients to vary over both individuals and time. We assume that the coefficients change smoothly over time and form different unobserved groups. When treated as smooth functions of time, the individual functional coefficients are heterogeneous across groups but homogeneous within a group. We propose a penalized-sieve-estimation-based classifier-Lasso (C-Lasso) procedure to identify the individuals’ membership and to estimate the group-specific functional coefficients in a single step. The classification exhibits the desirable property of uniform consistency. The C-Lasso estimators and their post-Lasso versions achieve the oracle property so that the group-specific functional coefficients can be estimated as well as if the individuals’ membership were known. Several extensions are discussed. Simulations demonstrate excellent finite sample performance of the approach in both classification and estimation. We apply our method to study the heterogeneous trending behavior of GDP per capita across 91 countries for the period 1960–2012 and find four latent groups.
Polysorbates versus Hydroxypropyl Beta-Cyclodextrin (HPβCD): Comparative Study on Excipient Stability and Stabilization Benefits on Monoclonal Antibodies

Polysorbates (PS 20 and PS 80) are the most widely used surfactants in biopharmaceutical formulations to protect proteins from denaturation, aggregation, and surface adsorption. To date, around 70% of marketed therapeutic antibodies contain either PS 20 or PS 80 in their formulations. However, polysorbates are chemically diverse mixtures, which are prone to degradation by oxidation and hydrolysis to produce peroxides and fatty acids, which, in turn, induce protein oxidation, aggregation, and insoluble particle formation. These will negatively impact protein quality and stability. Thus, polysorbate degradation has emerged as one of the major challenges in the development and commercialization of therapeutic protein products. KLEPTOSE® HPβCD (hydroxypropyl beta-cyclodextrin), a new multifunctional excipient, has been shown to provide protein stabilization functions in biopharmaceutical downstream processes and in their final formulations. This study aims to evaluate HPβCD, a new molecule of its class, against polysorbates as a stabilizer in biologics formulations. In this study, the chemical stability of KLEPTOSE® HPβCDs is compared with polysorbates (20 and 80) under various stress conditions. When subjected to heat stress, HPβCDs show little change in product recovery (90.7–100.7% recovery for different HPβCDs), while polysorbates 20 and 80 show significant degradation, with only 11.5% and 7.3% undegraded product remaining, respectively. When subjected to other chemical stressors, namely, autoclave, light, and oxidative stresses, HPβCD remains almost stable, while polysorbates show more severe degradation, with 95.5% to 98.8% remaining for polysorbate 20 and 85.5% to 97.4% remaining for polysorbate 80. Further, profiling characterization and degradation analysis reveal that chemical structures of HPβCDs remain intact, while polysorbates undergo significant hydrolytic degradation and oxidation. Lastly, the physicochemical stability of monoclonal antibodies in formulations is investigated. When subjected to light stress, adalimumab, as a model mAb, formulated in the presence of HPβCD, shows a significant decrease in protein aggregation, and superior monomer and total protein recovery compared to PS 80-containing formulations. HPβCD also reduces both agitation and thermal stress-induced protein aggregation and prevents subvisible particle formation compared to PS 80.
SELF-REPORTED INDUSTRIAL ACCIDENTS AND A SAFETY CLIMATE ANALYSIS OF A FOOD MANUFACTURING COMPANY IN NIGERIA

Background: The study examines the relationship between employees’ safety climate and industrial accidents in a food manufacturing company in Nigeria. Scholars identify safety climate as a plausible determinant of positive safety performance. The relationship between safety climate and industrial accidents has not attracted enough research in the food-manufacturing sector in Nigeria. Methods: The study employed a quantitative approach, utilizing a survey research design to administer structured questionnaires to employees. The questionnaires assessed respondents’ views on organizational safety climate and self-reported accidents. Results: The findings reveal a weak positive relationship between employees’ perceptions of safety and self-reported accident rate; and a difference in the perceptions of the six safety climate dimensions. Effective communication and leadership skills are the two leading factors that accounted for the variation in accident rates in the company. Findings show a high perception of the safety climate among the employees, but room for improvement in the subdimensions. Conclusion: This study confirms there is no substitute for effective communication and leadership to achieve positive safety performance. Top management, should incorporate safety predictors into their Safety Management System to focus on regular and continuous training to promote excellent safety performance in the industries.
Minimal models of weighted scale-free networks

We consider a class of simple, non-trivial models of evolving weighted scale-free networks. The network evolution in these models is determined by attachment of new vertices to ends of preferentially chosen weighted edges. Resulting networks have scale-free distributions of the edge weight, of the vertex degree, and of the vertex strength. We discuss situations where this mechanism operates. Apart of stochastic models of weighted networks, we introduce a wide class of deterministic, scale-free, weighted graphs with the small-world effect. We show also how one can easily construct an equilibrium weighted network by using a generalization of the configuration model.
THE MASS DEPENDENCE BETWEEN PROTOPLANETARY DISKS AND THEIR STELLAR HOSTS

We present a substantial extension of the millimeter (mm) wave continuum photometry catalog for circumstellar dust disks in the Taurus star-forming region, based on a new “snapshot” λ = 1.3 mm survey with the Submillimeter Array. Combining these new data with measurements in the literature, we construct a mm-wave luminosity distribution, f(Lmm), for Class II disks that is statistically complete for stellar hosts with spectral types earlier than M8.5 and has a 3σ depth of roughly 3 mJy. The resulting census eliminates a longstanding selection bias against disks with late-type hosts, and thereby demonstrates that there is a strong correlation between Lmm and the host spectral type. By translating the locations of individual stars in the Hertzsprung–Russell diagram into masses and ages, and adopting a simple conversion between Lmm and the disk mass, Md, we confirm that this correlation corresponds to a statistically robust relationship between the masses of dust disks and the stars that host them. A Bayesian regression technique is used to characterize these relationships in the presence of measurement errors, data censoring, and significant intrinsic scatter: the best-fit results indicate a typical 1.3 mm flux density of ∼25 mJy for 1 M☉ hosts and a power-law scaling . We suggest that a reasonable treatment of dust temperature in the conversion from Lmm to Md favors an inherently linear Md∝M* scaling, with a typical disk-to-star mass ratio of ∼0.2%–0.6%. The measured rms dispersion around this regression curve is ±0.7 dex, suggesting that the combined effects of diverse evolutionary states, dust opacities, and temperatures in these disks imprint a full width at half-maximum range of a factor of ∼40 on the inferred Md (or Lmm) at any given host mass. We argue that this relationship between Md and M* likely represents the origin of the inferred correlation between giant planet frequency and host star mass in the exoplanet population, and provides some basic support for the core accretion model for planet formation. Moreover, we caution that the effects of incompleteness and selection bias must be considered in comparative studies of disk evolution, and illustrate that fact with statistical comparisons of f(Lmm) between the Taurus catalog presented here and incomplete subsamples in the Ophiuchus, IC 348, and Upper Sco young clusters.