Miracle-dz/UniFAR-FTUs huggingface dataset:
Canonical Extra Mixing in Low-Mass Red Giants

We have used the latest observational data on the evolutionary variations of the surface chemical composition in low-mass metal-poor stars, both in the field and in globular clusters, to constrain the basic properties of extra mixing in upper red giant branch (RGB) stars. Two different models of extra mixing have been incorporated into a stellar evolution code: a parametrical diffusion model and a model with rotation-induced turbulent diffusion. Application of the first model to the interpretation of the observed variations of the surface abundances of Li, C, and N and of the isotopic ratio 12C/13C in field stars has revealed that, for the majority of upper RGB stars, the depth and rate of extra mixing do not appear to vary appreciably from star to star. Furthermore, comparisons of our calculations with the results obtained by other authors show that at least the extra mixing depth does not seem to depend strongly on metallicity. Therefore, we propose to call this universal nonconvective mixing process "canonical extra mixing. " We also put forward the hypothesis that some of the upper RGB stars may be experiencing "enhanced extra mixing," which is much faster (by a factor of ~100) and somewhat deeper than canonical extra mixing. This could explain the phenomenon of Li-rich giants. Enhanced extra mixing could also contribute to the O-Na and Mg-Al anticorrelations that are seen in some globular cluster red giants. A possible mechanism of extra mixing in upper RGB stars may be turbulent diffusion or/and meridional circulation induced by rotation. In this case, enhanced extra mixing requires rotational velocities that are ≈10 times as fast as those that are sufficient for the occurrence of canonical extra mixing. Observations do not exclude this possibility because (1) the dispersion in the surface rotational velocities of field Li-rich giants span a range of a factor of ~10 and (2) the extremely fast rotation of blue horizontal branch stars in globular clusters may require that their RGB precursors had been spun up appreciably by an external source. Star-to-star abundance variations in globular clusters may well have been produced as the result of both evolutionary and primordial processes. In the primordial scenario, the nuclearly processed material that is accreted by low-mass main-sequence stars may have originated primarily in earlier generations of massive asymptotic giant branch stars that had undergone hot bottom burning of their envelopes and partly in mass-losing upper RGB stars that had been just a bit more massive than the present-day main-sequence turnoff stars and had experienced extra mixing in the past.
Modeling and scaling of aeration bubble plumes: A two-phase flow analysis

In this paper, we focus on bubble plumes aimed at providing oxygen to, and at mixing unstratified water bodies. First, we discuss scaling relations obtained from a three-dimensional (3D) theoretical model for multi-component fluids.An expression for a length scale that represents a balance between inertia and buoyancy is derived, and it is used to scale vertical as well as horizontal (radial) distances. This length scale is then analyzed in terms of previous bubble-plume non-dimensional numbers. For the practical case in which the bubble diameter remains almost constant throughout the bubble plume, a one-dimensional theoretical model—which can be derived by integrating the 3D model in a horizontal plane—is used to show that the length scale (D) is well suited for defining zones in bubble plumes. These zones include a region near the diffuser where the size of the diffuser is important, an intermediate zone where the diffuser size and D determine the flow features, and an asymptotic region where plume variables approach a universal behavior. Procedures for scaling entire reservoirs and portions of reservoirs are developed. These procedures are evaluated with results from previous experiments and new observations in a large tank and a scaled physical model. Scaling of turbulence statistics in bubble plumes is also investigated.
Total Utilization-Upcycling of Mushroom Protein By-Product: Characterization and Assessment as an Alternative Batter Ingredient for Fried Shrimp

Mushroom by-products are economical and eco-friendly raw materials with bioactive and functional characteristics that allow for potential uses as food ingredients. However, mushroom upcycling has yet to be fully exploited, despite the many opportunities that mushrooms may offer. The mushroom protein by-product (MPBP) resulting from mushroom protein production was characterized (chemical composition, physicochemical attributes, and functional properties) and incorporated into plant-based batter formulations to prepare four experimental groups with different ratios (w/w, %) of wheat flour (W) to MPBP (100 W, 75 W/25 MPBP, 25 W/75 MPBP, and 100 MPBP). Subsequently, the batter was used for frying batter-coated shrimp, which was evaluated for cooking loss, coating pick-up, oil absorption, and color parameters (L*, a*, and b*). MPBP showed high content of dietary fiber, mainly insoluble fiber (49%), and it is potentially suited for the formulation of high-fiber food products. The MPBP physicochemical attributes pH (11.69), water activity (0.34), L* (58.56), a* (5.61), b* (18.03), and particle size distribution (250–500 µm (22.12%), 125–250 µm (41.18%), 63–125 µm (37.53%), and < 63 µm (0.82%) were noted. Concerning the MPBP functional characteristics, solubility (12.7%), emulsifying activity index (7.6 m2/gr), emulsion stability index (52.4 min), water holding capacity (4.9%), and oil holding capacity (4.8%) were reported. Adding MPBP into batter formulations for batter-coated shrimp resulted in higher values of cooking loss, oil absorption, coating pick-up, and a* color, while lowering L* and b* values. The best experimental results were reported for group 75 W/25 MPBP, which indicates that MPBP can potentially be accepted as a novel batter ingredient for partial substitution of wheat flour.
Flow Boiling Heat Transfer Coefficients of R-134a in a Microfin Tube

This paper presents results of an experimental study for forced convection evaporative heat transfer coefficients of R-134a (a replacement for R-12), inside a horizontal microfin tube. Data are reported as a function of parametric values of mass flux, quality, and heat flux in the test section. Results include heat transfer coefficients for the low mass flux levels typically encountered in household refrigerators and certain small-scale commercial evaporators
Nature-Derived Compounds as Potential Bioactive Leads against CDK9-Induced Cancer: Computational and Network Pharmacology Approaches

Given the importance of cyclin-dependent kinases (CDKs) in the maintenance of cell development, gene transcription, and other essential biological operations, CDK blockers have been generated to manage a variety of disorders resulting from CDK irregularities. Furthermore, CDK9 has a crucial role in transcription by regulating short-lived anti-apoptotic genes necessary for cancer cell persistence. Addressing CDK9 with blockers has consequently emerged as a promising treatment for cancer. This study scrutinizes the effectiveness of nature-derived compounds (geniposidic acid, quercetin, geniposide, curcumin, and withanolide C) against CDK9 through computational approaches. A molecular docking study was performed after preparing the protein and the ligands. The selected blockers of the CDK9 exerted reliable binding affinities (−8.114 kcal/mol to −13.908 kcal/mol) against the selected protein, resulting in promising candidates compared to the co-crystallized ligand (LCI). The binding affinity of geniposidic acid (−13.908 kcal/mol) to CDK9 is higher than quercetin (−10.775 kcal/mol), geniposide (−9.969 kcal/mol), curcumin (−9.898 kcal/mol), withanolide C (−8.114 kcal/mol), and the co-crystallized ligand LCI (−11.425 kcal/mol). Therefore, geniposidic acid is a promising inhibitor of CDK9. Moreover, the molecular dynamics studies assessed the structure–function relationships and protein–ligand interactions. The network pharmacology study for the selected ligands demonstrated the auspicious compound–target–pathway signaling pathways vital in developing tumor, tumor cell growth, differentiation, and promoting tumor cell progression. Moreover, this study concluded by analyzing the computational approaches the natural-derived compounds that have potential interacting activities against CDK9 and, therefore, can be considered promising candidates for CKD9-induced cancer. To substantiate this study’s outcomes, in vivo research is recommended.
Chromoelectric-flux-tube model of particle production

Quark confinement is assumed to be implemented by the generation of chromoelectric flux tubes with uniform energy density. Approximate formulas for the production of quark pairs in these tubes by tunneling are derived and various observable implications are studied. The non-Abelian nature of the SU(3) color group is taken partially into account thus yielding a quantitative estimate for the baryons-to-mesons ratio. Reasonable agreement with experiment is obtained.
Convolution-Based Trajectory Generation Methods Using Physical System Limits

This paper proposes two novel convolution-based trajectory generation methods using physical system limits such as maximum velocity, maximum acceleration, and maximum jerk. Convolution is a mathematical operation on two functions of an input function and a convoluted function, producing an output function that is typically viewed as a modified version of input function. Time duration parameters of the convoluted functions with a unit area are determined from the given physical system limits. The convolution-based trajectory generation methods to be proposed in this paper have three advantages; first, a continuously differentiable trajectory is simply obtained by applying successive convolution operations; second, a resultant trajectory is always generated satisfying the given physical system limits; third, the suggested methods have low computational burden thanks to recursive form of convolution operation. The suggested methods consider both zero and nonzero initial/terminal conditions. Finally, the effectiveness of the suggested methods is shown through numerical simulations.
Stability of Product Form G-Networks

We prove necessary and sufficient conditions for the existence and uniqueness of the stationary solution of the queueing networks (G-networks) with negative and positive customers introduced in Gelenbe [3], which have been shown to have product form. First, the existence of the solution of the nonlinear customer flow equations is established using Brouwer's fixed-point theorem; this result is valid for stable and unstable systems, as well as for certain networks that may not have product form. Then, the result is used to establish general stability related to the usual “load factor less than 1” criterion of queueing systems for G-networks with product form.
Generalized cellular neural network for novelty detection

A cellular neural network (CNN) for novelty detection is proposed. Each cell is connected to its neighboring inputs via an adaptive control operator, and interacts with neighboring cells via nonlinear feedback. In the learning mode, the control operator is modified in correspondence to a given set of patterns applied at the input. In the application mode, the CNN behaves like a memoryless system, which evidences those components of the input pattern that cannot be explained as a linear combination of the learned patterns. >
Hubble Space Telescope STIS Observations of the Kinematics of Emission-Line Nebulae in Three Compact Steep-Spectrum Radio Sources

We have obtained Hubble Space Telescope Space Telescope Imaging Spectrograph long-slit spectroscopy of the aligned emission-line nebulae in three compact steep-spectrum radio sources: 3C 67, 277.1, and 303.1. We find systematic offsets (∼300–500 km s-1) of the emission-line velocities on one or both sides of the radio sources. We also see evidence for broad lines (FWHM ∼ 500 km s-1) and complex emission-line profiles. In 3C 303.1 the data are consistent with multiple components and possibly split lines. The amplitude of the velocity variations is not so large as to exclude gravitationally induced motions. However, the complex kinematics, the lack of a signature of Keplerian rotation, and the association of the velocity variations with the radio lobes are consistent with the motions being driven by the expansion of the radio source. These kinematic signatures are consistent with an interaction between the expanding radio lobe and the emission-line clouds in which the clouds have been accelerated to velocities ∼300–500 km s-1. Acceleration of the clouds by the bow shock is plausible given the estimated densities in the clouds and the velocities observed in the much smaller compact symmetric objects and with expansion velocities estimated from spectral aging.
Magnetic domain structure and magnetic reversal in elliptical dot arrays

We present a joint magnetic force microscopy analysis and magneto-optical Kerr measurements of dot arrays that contain elliptical elements with different shape. In particular we have investigated the influence of the element shape on the domain structure in the remanent state. The single- or multiple domain nature of the magnetization at remanence is shown to depend strongly on how rounded or pointed each element is.
Film Condensation of Refrigerant-113 and Ethanediol on a Horizontal Tube—Effect of Vapor Velocity

Mesure des transferts de chaleur au cours de la condensation pelliculaire du freon 113 et de l'ethanodiol-1,2 sur un tube horizontal en ecoulement vertical descendant. Influence de la vitesse d'ecoulement des vapeurs. Comparaison des resultats obtenus avec des resultats publies anterieurement
Concrete Constructed with Recycled Water to Experimental Analysis of the Physical Behavior of Polypropylene Aggregate (PPA)

In concrete industry, the use of concrete solid wastes is becoming further common since it can minimize associated costs and environmental implications. The current research looks at how concrete performs when polypropylene (PP) plastic, produced from pollution garbage items, is used as a partially replacing for coarse aggregate (CA). The proportion of total PP (PPA) (0, 5, 10, 15, and 20%) as well as the water-cement ratio 0.45 of recycled household waste water, are the most important variables. Flow ability, hard porosity, maximum compressive strengths are all factors to consider, and economic feasibility are all discussed. The slump value grew when the proportion of PPA was raised, according to the findings. Compressive and cracking tensile strengths were increased in concrete containing 15% PPA. As a result, suggested to utilize more than 10% PPA in structural concrete, with either crushed stone or PP aggregate. The results showed that 15% is the best alternative to coarse gravel during terms of strength and durability, and hence PPA can be utilized as a partial replacement for coarse aggregate in concrete. To conduct studies to enhance the impact resistance of concrete by utilizing recycled water. Reducing the consumption of nonrenewable resources reducing greenhouse gas emissions and improving the strength and load capacity of industrial materials used in building research. Finally, employing non-biodegradable waste plastic resources to make green concrete will offer up new possibilities.
MCMC Estimation of Restricted Covariance Matrices

This article is motivated by the difficulty of applying standard simulation techniques when identification constraints or theoretical considerations induce covariance restrictions in multivariate models. To deal with this difficulty, we build upon a decomposition of positive definite matrices and show that it leads to straightforward Markov chain Monte Carlo samplers for restricted covariance matrices. We introduce the approach by reviewing results for multivariate Gaussian models without restrictions, where standard conjugate priors on the elements of the decomposition induce the usual Wishart distribution on the precision matrix and vice versa. The unrestricted case provides guidance for constructing efficient Metropolis–Hastings and accept-reject Metropolis–Hastings samplers in more complex settings, and we describe in detail how simulation can be performed under several important constraints. The proposed approach is illustrated in a simulation study and two applications in economics. Supplemental materials for this article (appendixes, data, and computer code) are available online.
A shock-fitting algorithm for the Lighthill–Whitham–Richards model on inhomogeneous highways

The analytical shock-fitting algorithm outperforms traditional numerical methods in solving the Lighthill–Whitham–Richards (LWR) traffic flow model for homogeneous highways. In this study, we extend the algorithm to an inhomogeneous highway in which two homogeneous sections with different fundamental diagrams are connected by a junction (interface). According to the entropy condition of the Riemann problem, the flow conditions at the interface can be categorized into four groups, and the density on both sides of the interface can be uniquely determined. Based on the physical conditions, we construct a fictitious element as an appropriate boundary condition for each homogeneous section. Consequently, the Riemann problem of an inhomogeneous highway can be transformed into a problem of equivalent homogeneous sections to which the shock-fitting algorithm can be applied. We apply this algorithm to some representative traffic flow cases and compare the results with numerical solutions obtained using the Weighted Essentially Non-Oscillatory (WENO) scheme.
Predicting the accuracy of nanofluid heat transfer coefficient's computational fluid dynamics simulations using neural networks

This research presents a neural network algorithm to identify the best modeling and simulation methods and assumptions for the most widespread nanofluid combinations. The neural network algorithm is trained using data from earlier nanofluid experiments. A multilayer perceptron with one hidden layer was employed in the investigation. The neural network algorithm and data set were created using the Python Keras module to forecast the average percentage error in the heat transfer coefficient of nanofluid models. Integer encoding was used to encode category variables. A total of 200 trials of different neural networks were taken into consideration. The worst‐case error bound for the chosen architecture was then calculated after 100 runs. Among the eight models examined were the single‐phase, discrete‐phase, Eulerian, mixture, the mixed model of discrete and mixture phases, fluid volume, dispersion, and Buongiorno's model. We discover that a broad range of nanofluid configurations is accurately covered by the dispersion, Buongiorno, and discrete‐phase models. They were accurate for particle sizes (10–100 nm), Reynolds numbers (100–15,000), and volume fractions (2%–3.5%). The accuracy of the algorithm was evaluated using the root mean square error (RMSE), mean absolute error (MAE), and R2 performance metrics. The algorithm's R2 value was 0.80, the MAE was 0.77, and the RMSE was 2.6.
Reflection of Acoustic Pulses from Stable and Instable Interfaces between Moving Fluids

Cagniard's method is used to find the transient solution for the reflection of line‐source generated waves from an interface between two fluid half‐spaces in relative motion, but with the same density and sound speed. The resulting solution for the reflected wave in the (normally motionless) lower half‐space may be conveniently considered as the sum of a specularly reflected wave, a refracted arrival wave, a neutral stability wave comprising one or more resonance waves, and an instability wave. Critical angles limiting the appropriate domains of reception are derived for each wave type as a function of the Mach number M of the fluid motion in the upper half‐space. The instability wave appears in the solution when M < 2 2. This feature may possibly represent the prediction of the spatial extent of a turbulent region arising from an inherent instability of the ambient medium which has been triggered by the arival of the incident pulse at the interface.
Enhancement in sensitivity and detection of luminescent quenching based oxygen sensing by gold nanoparticles

The field of plasmonics has shown a great promise in the enhancement of luminescence detection. Here, a simple method to enhance oxygen detection by quenching of Ru[(4,7-diphenyl-1,10-anthroline)3]2+ (or Ru[dpp]2+) in a sol-gel matrix by localized surface plasmon resonance (LSPR) of gold nanoparticles (AuNP) is presented. In the experiments, AuNP (10 ± 1.5 nm diameter) were added to a sol that was prepared by hydrolysis of trimethoxysilane, octyltrimethoxysilane and ethanol in the presence of Ru[dpp]2+ luminophore. The resulting sol of the mixture was spincoated on glass and allowed to age in the dark for one week to form the sol-gel film. A control sample was also prepared using the procedure, except that AuNP was not added to the sol. The resulting AuNP embedded sol-gel shows 8.3 times improvement in the baseline (0% O2) intensity (I0) over the control. Moreover, there is a dramatic improvement in the sensitivity from 0.0011 per % O2 in the control to 0.059 per % O2 with AuNP, for O2 level below 15%. Signal to noise ratio also improved, thus leading to a 100-fold improvement in the detection limit. Using phaseluminometry, it was determined that there is a reduction in the luminescence lifetime when AuNP is added to the sol-gel matrix. This reduction in the lifetime can be explained by the near-field interaction between the luminophores and the AuNP.
Lack of Association between YEASTONE Antifungal Susceptibility Tests and Clinical Outcomes of Cryptococcus Meningitis

The relation between antifungal susceptibility and treatment outcomes is not well-characterized. There is paucity of surveillance data for cerebrospinal fluid (CSF) isolates of cryptococcus investigated with YEASTONE colorimetric broth microdilution susceptibility testing. A retrospective study of laboratory-confirmed cryptococcus meningitis (CM) patients was conducted. The antifungal susceptibility of CSF isolates was determined using YEASTONE colorimetric broth microdilution. Clinical parameters, CSF laboratory indices, and antifungal susceptibility results were analyzed to identify risk factors for mortality. High rates of resistance to fluconazole and flucytosine were observed in this cohort. Voriconazole had the lowest MIC (0.06 µg/mL) and lowest rate of resistance (3.8%). In a univariate analysis, hematological malignancy, concurrent cryptococcemia, high Sequential Organ Failure Assessment (SOFA) score, low Glasgow coma scale (GCS) score, low CSF glucose level, high CSF cryptococcal antigen titer, and high serum cryptococcal antigen burden were associated with mortality. In a multivariate analysis, meningitis with concurrent cryptococcemia, GCS score, and high CSF cryptococcus burden, were independent predictors of poor prognosis. Both early and late mortality rates were not significantly different between CM wild type and non-wild type species.