Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of self-construal on rater severity in peer assessments of oral presentations

ABSTRACT Raters vary from each other in their severity and leniency in rating performance. This study examined the factors affecting rater severity in peer assessments of oral presentations in English as a Foreign Language (EFL), focusing on peer raters’ self-construal and presentation abilities. Japanese university students enrolled in EFL classes responded to self-construal questionnaires and gave and assessed in-class presentations. The results of path analysis revealed that peer raters’ self-construal and presentation abilities might be potential sources of subtle biases in peer assessments. Whereas peer raters who placed a high value on maintaining harmonious relationships with others were more likely to give lenient ratings over time, those who were sensitive about being evaluated by others tended to become gradually more severe in their ratings. Furthermore, a peer rater’s presentation ability positively predicted rater severity, indicating that individuals with better presentation abilities were more severe in their peer assessments.
Localization of energy in FPU chains

We revisit the celebrated model of Fermi, Pasta and Ulam with the aim of investigating, by numerical computations, the trend towards equipartition in the thermodynamic limit. We concentrate our attention on a particular class of initial conditions, namely, with all the energy on the first mode or the first few modes. We observe that the approach to equipartition occurs on two different time scales: in a short time the energy spreads up by forming a packet involving all low--frequency modes up to a cutoff frequency $\omega_c$, while a much longer time is required in order to reach equipartition, if any. In this sense one has an energy localization with respect to frequency. The crucial point is that our numerical computations suggest that this phenomenon of a fast formation of a natural packet survives in the thermodynamic limit. More precisely we conjecture that the cutoff frequency $\omega_c$ is a function of the specific energy $\epsilon = E/N$, where $E$ and $N$ are the total energy and the number of particles, respectively. Equivalently, there should exist a function $\epsilon_c(\omega)$, representing the minimal specific energy at which the natural packet extends up to frequency $\omega$. The time required for the fast formation of the natural packet is also investigated.
Using Halton Sequences in Random Parameters Logit Models

Quasi-random numbers that are evenly spread over the integration domain have become used as alternatives to pseudo-random numbers in maximum simulated likelihood problems to reduce computational time. In this paper, we carry out Monte Carlo experiments to explore the properties of quasi-random numbers, which are generated by the Halton sequence, in estimating the random parameters logit model. We vary the number of Halton draws, the sample size and the number of random coefficients. We show that increases in the number of Halton draws influence the efficiency of the random parameters logit model estimators only slightly. The maximum simulated likelihood estimator is consistent. We find that it is not necessary to increase the number of Halton draws when the sample size increases for this result to be evident.
Cellular Uptake of Modified Mesoporous Bioactive Glass Nanoparticles for Effective Intracellular Delivery of Therapeutic Agents

Introduction There has recently been a surge of interest in mesoporous bioactive glass nanoparticles (MBGNs) as multi-functional nanocarriers for application in bone-reconstructive and -regenerative surgery. Their excellent control over their structural and physicochemical properties renders these nanoparticles suitable for the intracellular delivery of therapeutic agents to combat degenerative bone diseases, such as bone infection, or bone cancer. Generally, the therapeutic efficacy of nanocarriers strongly depends on the efficacy of their cellular uptake, which is determined by numerous factors including cellular features and the physicochemical characteristics of nanocarriers, particularly surface charge. In this study, we have systematically investigated the effect of the surface charge of MBGNs doped with copper as a model therapeutic agent on cellular uptake by both macrophages and pre-osteoblast cells involved in bone healing and bone infections to guide the future design of MBGN-based nanocarriers. Methods Cu-MBGNs with negative, neutral, and positive surface charges were synthesized and their cellular uptake efficiency was assessed. Additionally, the intracellular fate of internalized nanoparticles along with their ability to deliver therapeutic cargo was studied in detail. Results The results showed that both cell types internalized Cu-MBGNs regardless of their surface charge, indicating that cellular uptake of nanoparticles is a complex process influenced by multiple factors. This similarity in cellular uptake was attributed to the formation of a protein corona surrounding the nanoparticles when exposed to protein-rich biological media, which masks the original nanoparticle surface. Once internalized, the nanoparticles were found to mainly colocalize with lysosomes, exposing them to a more compartmentalized and acidic environment. Furthermore, we verified that Cu-MBGNs released their ionic components (Si, Ca, and Cu ions) in both acidic and neutral environments, leading to the delivery of these therapeutic cargos intracellularly. Conclusion The effective internalization of Cu-MBGNs and their ability to deliver cargos intracellularly highlight their potential as intracellular delivery nanocarriers for bone-regenerative and -healing applications.
Baroclinic vorticity generation near the turbulent/non-turbulent interface in a compressible shear layer

Direct numerical simulations of temporally evolving compressible mixing layers are performed to study the baroclinic vorticity generation due to the interaction between the intense vorticity structures (IVSs) and the turbulent/non-turbulent interface (TNTI). In order to examine this interaction, the vorticity transport across the TNTI and the IVSs in compressible turbulence are studied. The conditional mean budget of different terms in the transport equation of vorticity with respect to the distance from the TNTI is analyzed in the interface coordinate system. In highly compressible mixing layers, it is shown that in proximity of the TNTI, contribution of the baroclinic torque to the total change of vorticity, compared with the other terms in the transport equation of the vorticity, cannot be ignored. The conditional average of the baroclinic torque in the vorticity transport equation reaches a maximum inside the interface layer, with the thickness of approximately one Taylor length scale, at a distance approximately equal to the radius of the IVS from the TNTI. Flow visualization results show that the intense vorticity structures generate a baroclinic torque as they become close to the turbulent/non-turbulent interface. In order to statistically examine the organized interaction between the TNTI and the IVSs, an algorithm described in the Appendix is developed to detect and to study the intense vorticity structures. It is shown that the IVSs generate a pressure gradient from the core of the vortex, low pressure, towards the region outside of the vortex. As the IVSs interact with the TNTI, the pressure gradient vectors become misaligned with the density gradient vectors, which are aligned with the direction normal to the TNTI, and generate a baroclinic torque. It is also observed that compressibility has a small effect on the structural features of the IVSs in the shear layer.
Iteratively Reweighted Generalized Least Squares for Estimation and Testing With Correlated Data: An Inference Function Framework

The focus of this article is on fitting regression models and testing of general linear hypotheses for correlated data using quasi-likelihood based techniques. The class of generalized method of moments or GMMs provides an elegant approach for estimating a vector of regression parameters from a set of score functions. Extending the principle of the GMMs, in the generalized estimating equation framework, leads to a quadratic inference function or QIF approach for the analysis of correlated data. We derive an iteratively reweighted generalized least squares or IRGLS algorithm for finding the QIF estimator and establish its convergence properties. A software library implementing the techniques is demonstrated through several datasets.
The Eikonal ray equations in optical fibers

The traditional methods for the characterization of ray trajectories in nonhomogeneous media are based on the Eikonal equation valid in the region of geometrical optics. To apply Eikonal theory to fiber optics requires a fairly extensive use of vector calculus and geometry. Alternatively, through a simple correspondence rule, and a few intuitive guidelines for relating rays to waves, an equivalent set of trajectory equations can be obtained. This approach results in the Eikonal equations for the space evolution of optical fiber rays which are expressed in terms of the mode analysis invariants, azimuthal mode number, and optical waveguide propagation phase constant.
Sparse recovery via convex optimization

This thesis considers the problem of estimating a sparse signal from a few (possibly noisy) linear measurements. In other words, we have y = Ax + z where A is a measurement matrix with more columns than rows, x is a sparse signal to be estimated, z is a noise vector, and y is a vector of measurements. This setup arises frequently in many problems ranging from MRI imaging to genomics to compressed sensing. We begin by relating our setup to an error correction problem over the reals, where a received encoded message is corrupted by a few arbitrary errors, as well as smaller dense errors. We show that under suitable conditions on the encoding matrix and on the number of arbitrary errors, one is able to accurately recover the message. We next show that we are able to achieve oracle optimality for x, up to a log factor and a factor of sqrt{s}, when we require the matrix A to obey an incoherence property. The incoherence property is novel in that it allows the coherence of A to be as large as O(1/ log n) and still allows sparsities as large as O(m/log n). This is in contrast to other existing results involving coherence where the coherence can only be as large as O(1/sqrt{m}) to allow sparsities as large as O(sqrt{m}). We also do not make the common assumption that the matrix A obeys a restricted eigenvalue condition. We then show that we can recover a (non-sparse) signal from a few linear measurements when the signal has an exactly sparse representation in an overcomplete dictionary. We again only require that the dictionary obey an incoherence property. Finally, we introduce the method of l_1 analysis and show that it is guaranteed to give good recovery of a signal from a few measurements, when the signal can be well represented in a dictionary. We require that the combined measurement/dictionary matrix satisfies a uniform uncertainty principle and we compare our results with the more standard l_1 synthesis approach. All our methods involve solving an l_1 minimization program which can be written as either a linear program or a second-order cone program, and the well-established machinery of convex optimization used to solve it rapidly.
Exploring natural language processing in mechanical engineering education: Implications for academic integrity

In this paper, the authors review extant natural language processing models in the context of undergraduate mechanical engineering education. These models have advanced to a stage where it has become increasingly more difficult to discern computer vs. human-produced material, and as a result, have understandably raised questions about their impact on academic integrity. As part of our review, we perform two sets of tests with OpenAI's natural language processing model (1) using GPT-3 to generate text for a mechanical engineering laboratory report and (2) using Codex to generate code for an automation and control systems laboratory. Our results show that natural language processing is a potentially powerful assistive technology for engineering students. However, it is a technology that must be used with care, given its potential to enable cheating and plagiarism behaviours given how the technology challenges traditional assessment practices and traditional notions of authorship.
Features of Afterbody Radiative Heating for Earth Entry

Radiative heating is identified as a major contributor to afterbody heating for Earth entry capsules at velocities above 10 km/s. Because of rate-limited electron–ion recombination processes, many of the electronically excited N and O atoms produced in the high-temperature/pressure forebody remain as they expand into the afterbody region, which results in significant afterbody radiation. Large radiative heating sensitivities to electron-impact ionization rates and escape factors are identified. Ablation products from a forebody ablator are shown to increase the afterbody radiation by nearly 40%, due to the influence of CO on the vibrational-electronic temperature. The tangent-slab radiation transport approach is shown to overpredict the radiative flux by as much as 50% in the afterbody, therefore making the more computationally expensive ray-tracing approach necessary for accurate radiative flux predictions. For the Stardust entry, the afterbody radiation is predicted to be nearly twice as large as the c.
RUL Prediction of Lithium-Ion Battery in Early-Cycle Stage Based on Similar Sample Fusion Under Lebesgue Sampling Framework

Remaining useful life (RUL) prediction of lithium-ion battery in early-cycle stage is of great significance to improve battery performance and reduce losses caused by failure. Because of complex degradation mechanism and insufficient data in early-cycle stage, current RUL prediction schemes for lithium-ion battery have trouble obtaining degradation characteristics to achieve satisfactory prediction accuracy. Aiming at this problem, this article proposes an RUL prediction method of lithium-ion batteries in early-cycle stage based on similar sample fusion under the Lebesgue sampling framework. First, a novel similarity measurement index based on the fusion of Pearson correlation coefficient (PCC) and Euclidean distance (EuD) is proposed, and the fusion parameter is optimized by the jumping spider optimization algorithm (JSOA). Similar samples are selected as references for the prediction model. Then, the Lebesgue sampling theory is introduced to complete data structure transformation for similar samples so as to ensure that the fused points of different similar samples are under the same degradation state. Finally, a similar sample fusion result is transformed into Riemann sampling framework, and a linear fitting is performed. Fitting results are used to construct a particle filter (PF) model for the capacity degradation process and RUL prediction. Experimental results and comparative studies on the APR18650M1A battery dataset demonstrate the effectiveness of the proposed method.
Fonction spectrale et valeurs propres d'une classe d'operateurs non elliptiques

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Synergetic Effect of Electrical and Topographical Cues in Aniline Trimer-Based Polyurethane Fibrous Scaffolds on Tissue Regeneration

Processibility and biodegradability of conductive polymers are major concerns when they are applied to tissue regeneration. This study synthesizes dissolvable and conductive aniline trimer-based polyurethane copolymers (DCPU) and processes them into scaffolds by using electrospinning with different patterns (random, oriented, and latticed). The effects of topographic cue changes on electrical signal transmission and further regulation of cell behaviors concerning bone tissue are researched. Results show that DCPU fibrous scaffolds possessed good hydrophilicity, swelling capacity, elasticity, and fast biodegradability in enzymatic liquid. In addition, the conductivity and efficiency of electrical signal transmission can be tuned by changing the surface’s topological structure. Among them, oriented DCPU scaffolds (DCPU-O) showed the best conductivity with the lowest ionic resistance value. Furthermore, the viability and proliferation results of bone mesenchymal stem cells (BMSCs) demonstrate a significant increase on three DCPU scaffolds compared to AT-free scaffolds (DPU-R). Especially, DCPU-O scaffolds exhibit superior abilities to promote cell proliferation because of their unique surface topography and excellent electroactivity. Concurrently, the DCPU-O scaffolds can synergistically promote osteogenic differentiation in terms of osteogenic differentiation and gene expression levels when combined with electrical stimulation. Together, these results suggest a promising use of DCPU-O fibrous scaffolds in the application of tissue regeneration.
Multi‐Model Subseasonal Prediction Skill Assessment of Water Vapor Transport Associated With Atmospheric Rivers Over the Western U.S.

Subseasonal‐to‐seasonal (S2S) forecasts of atmospheric rivers (ARs) are in high demand in the water supply management and flood control communities. This study focuses on a new metric, the accumulated water vapor transport associated with ARs, which is closely related to the winter precipitation over the western U.S., and provides a multi‐model S2S prediction skill assessment. The prediction skill is evaluated at lead time 1–4 weeks in four dynamical model hindcast data sets from National Centers for Environmental Prediction (NCEP), European Center for Medium‐Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), and Global Modeling and Assimilation Office (GMAO) at National Aeronautics and Space Administration. Three reanalysis data sets are used to evaluate the uncertainty of prediction skills related to the choice of references. The AR‐related water vapor transport is underestimated in ECMWF and ECCC over most of the investigated region, while its maximum has a southeastward shift in NCEP and GMAO at lead time 3–4 weeks. The root mean square error, anomaly correlation coefficient, and Brier skill score are calculated to quantify the prediction skill in both deterministic and probabilistic sense. At week‐3 lead time, the models have significant skill near the lower latitudes (<40°N) of the eastern North Pacific, extending northeastward to the California coastal area. Models have higher skills in forecasting no and strong AR cases than weak cases. The Madden–Julian Oscillation can modulate the prediction skill at week‐3 lead over central and Southern California, but with large uncertainties across models.
GIBBS MEASURES ON CAYLEY TREES: RESULTS AND OPEN PROBLEMS

The purpose of this review paper is to present systematically all known results on Gibbs measures on Cayley trees (Bethe lattices). There are about 150 papers which contain mathematically rigorous results about Gibbs measures on Cayley trees. This review is mainly based on the recently published mathematical papers. The method used for the description of Gibbs measures on Cayley trees is the method of Markov random field theory and recurrent equations of this theory, but the modern theory of Gibbs measures on trees uses new tools such as group theory, information flows on trees, node-weighted random walks, contour methods on trees and nonlinear analysis. We discuss all the mentioned methods which were developed recently. Thus, the paper informs the reader about what is (mathematically) done in the theory of Gibbs measures on trees and about where the corresponding results were published. We only give proofs which were not published in literature. Moreover, we give several open problems.
Study on neutral wind contribution to the electrodynamics in the polar ionosphere using EISCAT CP‐1 data

Energy coupling between the thermosphere, ionosphere and magnetosphere is studied quantitatively through an analysis using the European Incoherent Scatter (EISCAT) Common Program (CP) −1 version H data obtained on May 3, 1988. A negative excursion of the H component in the Tromso magnetogram occurred during the experiment period, which involved the following two features: (1) the electric potential across the polar cap was expected to be reduced abruptly in association with a sudden change of the interplanetary magnetic field (IMF) Bz polarity from southward to around null and (2) the negative excursion had a relatively long duration of development (about 4 hours), which may drive neutrals to move significantly through ion drag. In order to investigate the energy coupling between the thermosphere and ionosphere, we evaluate quantitatively the electromagnetic energy flux J·E, the Joule heating rate J·E′ (E′ = E + U × B), and the mechanical energy transfer rate U·(J × B), where U is the neutral wind velocity. The CP-I-H experiment provides directly or indirectly all quantities above at altitudes of 101 km, 109 km, 119 km, and 132 km. The results are summarized as follows. (1) The amplitude of the neutral wind related electric field U × B varied greatly with altitude, i.e., at altitudes above 119 km it often became larger than 50% of the amplitude of the observed electric field; (2) during the late recovery phase of the negative excursion of the H component of the Tromso magnetic field, the neutral wind related electric field tended to be canceled with the observed electric field; (3) in the E region the neutral wind mechanical energy transfer rate U·(J × B) is not negligible but is comparable to the Joule heating rate J·E′; and (4) in particular, at higher altitudes (132 km high) the conversion from the neutral wind mechanical energy to the electromagnetic energy occasionally may occur.
Evolution of the subtropical marine boundary layer: Comparison of soundings over the eastern Pacific from FIRE and HaRP

Abstract The mean time rates of change of temperature, total water mixing ratio and ozone along airflow trajectories in the lower troposphere over the eastern Pacific are inferred by comparing aircraft soundings from the First ISCCP Regional Experiment (FIRE) and the Hawaiian Rainband Project (HaRP). Through the use of the estimated mean fluxes of temperature and total water mixing ratio, it is found that the tendency for stratus layers to grow or dissipate is very sensitive to the assumed turbulence structure below the capping inversion. A mixed-layer model that assumes a well-mixed boundary layer up to the capping inversion predicts a solid cloud layer extending all the way to Hawaii, whereas a model that allows decoupling predicts rapid dissipation of the stratus layer. It is concluded that stratus dissipation here is due to the slowdown of turbulent mixing throughout the layer below the capping inversion, not the drying out of a well-mixed layer; hence, the mixed-layer model cannot be expected to pred.