Miracle-dz/UniFAR-FTUs huggingface dataset:
Peristaltic particle transport using the Lattice Boltzmann method

Peristaltic transport refers to a class of internal fluid flows where the periodic deformation of flexible containing walls elicits a non-negligible fluid motion. It is a mechanism used to transport fluid and immersed solid particles in a tube or channel when it is ineffective or impossible to impose a favorable pressure gradient or desirous to avoid contact between the transported mixture and mechanical moving parts. Peristaltic transport occurs in many physiological situations and has myriad industrial applications. We focus our study on the peristaltic transport of a macroscopic particle in a two-dimensional channel using the lattice Boltzmann method. We systematically investigate the effect of variation of the relevant dimensionless parameters of the system on the particle transport. We find, among other results, a case where an increase in Reynolds number can actually lead to a slight increase in particle transport, and a case where, as the wall deformation increases, the motion of the particle becomes non-negative only. We examine the particle behavior when the system exhibits the peculiar phenomenon of fluid trapping. Under these circumstances, the particle may itself become trapped where it is subsequently transported at the wave speed, which is the maximum possible transport in the absence of a favorable pressure gradient. Finally, we analyze how the particle presence affects stress, pressure, and dissipation in the fluid in hopes of determining preferred working conditions for peristaltic transport of shear-sensitive particles. We find that the levels of shear stress are most hazardous near the throat of the channel. We advise that shear-sensitive particles should be transported under conditions where trapping occurs as the particle is typically situated in a region of innocuous shear stress levels.
Chemical pseudopotential approach to covalent bonding. II. Bond lengths and bond energies in diamond, silicon and graphite

For pt.I see ibid., vol.8, p.2695 (1975). Bond energy curves for carbon and silicon are derived using a bonding orbital basis and a 'nearest-atom' approximation for the potential. When interactions between neighbouring atomic cells are included, good agreement is obtained with observed bond lengths, bulk moduli and bond energies.
Size Refinement of Empirical Likelihood Tests in Time Series Models using Sieve Bootstraps

We employ sieve bootstraps for empirical likelihood tests in time series models because their null distribu- tions are often vulnerable to the presence of serial dependence. We found a significant size refinement of the bootstrapped versions of a Lagrangian Multiplier type test statistic regardless of the bandwidth choice required by long-run variance estimations. Empirical likelihood(EL) methods (known as nonparametric likelihoods) are commonly used for ro- bust hypothesis testing under weak identification. In time series models with dependent processes, valid testing procedures have been developed that include a generalized method of mo-ments(GMM)- based test statistic by Kleibergen (2005), Lagrangian Multiplier(LM) tests by Guggenberger and Smith(GS) (2008) and by Otsu (2006). Tests based on quadratic forms of first-order conditions in EL estimation can achieve pivotal properties because the asymptotic null distributions do not depend on the strength of identifications. They have standard Chi-squared limit distributions with degrees of freedom equal to the number of parameters instead of the number of moments; subsequently, the EL tests prove practically useful in many aspects. EL tests typically generate size distortions when underlying processes carry serial dependence. Instrumental variables or innovation processes tend to be often serially correlated in time series models that consist of (for example) macroeconomic series. Subsequently, empirical null distributions of EL tests become sensitive to the choice of bandwidth, which is required for the estimation of long-run variance. Empirical null rejection probabilities of the tests significantly depart from the nominal level, which negatively affects accurate statistical inferences. This issue of size distortion is closely related with the well-known size problem of heteroskedasticity and autocorrelation consistent(HAC) covariance matrix estimation in time series literature (cf: Andrews, 1991; Newey and West, 1994). Therefore, we try to find a way to improve size performance of the EL tests. In this work, we make use of the sieve bootstraps by B¨ (1997) as an appropriate bootstrap method. In particular, the sieve method is a natural way to handle serial dependence in a time series. It approximates a dependent time series process through autoregressive models under the null hypoth- esis and generates bootstrapped data based on re-sampled residuals and fitted autoregressive models. It is expected that the bootstrapped EL tests can achieve a more accurate size performance than the
Role of negative properties in knowledge modeling

An appropriate and eective knowledge domain model is the key element of any intelligent systems. The dominating knowledge models in information technology, like ontology or formal concept analysis [2] are strongly related with the current SE modeling tools from the viewpoint of property modeling. These models use positive and mainly binary valued properties. On the field of philosophical ontology, this can be a valid assumption, but IT ontologies are used sometimes for modeling epistemology. The epistemology model is the result of a cognitive process using deductive and inductive methods. In this model, the lack of some property can be an important characteristic of the objects. The paper presents a survey on interpretation of negative property [1] in philosophy and IT ontology. On both fields, very dierent approaches compete with each other [3]. In the analysis, we argue for the acceptance of negative properties in knowledge modeling. The main benefit of the dual representation of positive and negative properties is a finer description of the support values for facts during an inductive learning process. This extended structure provides more information compared even with the multi-valued logic or fuzzy representation models. The paper demonstrates the construction of knowledge graph using the dual property model.
Oscillator strengths of the transitions in a spherically confined hydrogen atom

Dipole and quadrupole oscillator strengths have been calculated for the series of transitions nl → n′l′ (n ⩽ 5) in a hydrogen atom confined in the centre of a sphere with impenetrable walls. The non-monotonic dependence of the oscillator strengths on the radius of confinement has been discussed through their general forms obtained with a properly chosen radial wavefunction. It was qualitatively ascribed to variation of the radial electron density distributions of the states involved in the transitions. The effect of the confinement on the use of the dipole approximation has been studied, introducing a correction through the octupole term as well as performing the calculation with the full form of the interaction term.
Espaces fibrés algébriques

© Association des collaborateurs de Nicolas Bourbaki, 1954, tous droits réservés. L’accès aux archives du séminaire Bourbaki (http://www.bourbaki. ens.fr/) implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
Engagement of Online Biobehavioral Health Students in Ethics Education Through Virtual Immersive Experiences

In public health, ethics is a core competency. Accordingly, ethical decision-making should be part of the curriculum for all students seeking to enter the profession. Evidence suggests simulation may positively impact student learning of ethics. Yet, engaging in a traditional simulative or immersive experience is inaccessible to many students. Instead, could participation in virtual immersive experiences serve as a proxy for promoting student outcomes for ethical decision-making in public health? This quasi-experimental mixed methods study examined whether the integration of 360° videos in an undergraduate online health ethics course enhanced intended ethical decision-making. Undergraduate students (n = 26) provided access to two assignments or artifacts. These artifacts were analyzed in accordance with a tailored rubric to explore differences in five constructs of ethical reasoning when writing assignments were completed with (n = 13), or without (n = 13), an integrated 360° video component. The results show the 360° videos enhanced the students’ ability to engage in ethical reasoning, with significant differences in rater scores being noted across multiple constructs. The data were also analyzed qualitatively. Students in the intervention group engaged in a process of “compassionate calculus,” integrating various elements of empathy, compassion, and self-awareness toward a clinical outcome. Together these findings demonstrate the capacity of virtual immersive experience to support students in the enhancement of intended ethical decision-making through the development of greater compassion or empathic concern. This study provides evidence to guide the continued integration of virtual immersive experiences into undergraduate public health education.
Evolution of Antibiotic Resistance in Escherichia coli and Klebsiella pneumoniae Clinical Isolates in a Multi-Profile Hospital over 5 Years (2017–2021)

In recent years, we have witnessed a growing drug resistance among bacteria, which is associated with the use and availability of an increasing number of broad-spectrum antimicrobial agents, as well as with their irrational and excessive use. The present study aims to analyze changes in the drug resistance of Gram-negative Enterobacterales: Escherichia coli and Klebsiella pneumoniae, isolated from infections in a multi-profile hospital over five years (from 2017 to 2021). Among the practical outcomes of the evaluation of these data will be the possibility of determining changes in susceptibility to the antibiotics used in the hospital. In turn, this will help propose new therapeutic options, especially for empirical therapy that is necessary in severe infections. The analysis of the use of individual groups of antibiotics allowed for identification of the causes of the increasing resistance of Gram-negative bacilli. The highest number of infections whose etiological agent was K. pneumoniae ESBL(+) and E. coli ESBL(+) was observed in 2018. In the analyzed five-year period, the number of multi-resistant (MDR) K. pneumoniae strains increased successively, which seems to be related to the growing use, especially in the pandemic period, of broad-spectrum antibiotics, mainly penicillins with inhibitors, third-generation cephalosporins, and carbapenems.
Ion abundance ratios in the Jovian magnetosphere

[1] Galileo, as the first orbiting spacecraft around Jupiter, provides the opportunity to study globally the composition of the energetic ion population in the equatorial plane of the Jovian magnetosphere. This enables us to derive the relative importance of the various sources and sinks of plasma and energetic particles in the largest magnetosphere of our solar system. In this paper we use data from the Energetic Particles Detector (EPD) on board Galileo and derive relative ion abundance ratio maps of S/O, S/He, O/He, and p/He. We extend the previous work in terms of global coverage, species, and energy range. In addition we compare them with previous results and especially with those derived on board Voyager 2. We found that the S/O abundance ratio is relatively constant throughout the magnetosphere decreasing slightly with radial distance. Within the error bars the S/O ratios could be reproduced. Only a minor energy dependence is observed for this ratio pointing to a common source for both ions. The S/He-, O/He-, and p/He-ratios decrease with increasing radial distance, furthering the notion that sulfur, oxygen, and protons originate mainly from a source within the Jovian system, in contrast with helium, which originates from the solar wind. A spectral kink observed at energies of several 100 keV/nuc for all species and most pronounced for helium explains the observed energy dependence of the ratios relative to helium. Differences in the abundance ratios up to more than one order of magnitude between sequential orbits are evidence of large temporal variations taking place in the Jovian magnetosphere. These variations and the strong energy dependence can explain the differences between the results derived from Galileo EPD measurements and those from Voyager data.
Potential Antifibrotic Effect of Blueberry and Grape Polyphenol Extracts in Acute and Subacute Bleomycin-Induced Lung Tissue Injury Models in Mice

Potential antifibrotic effect of blueberry and grape polyphenol extracts in acute and subacute bleomycin-induced lung tissue injury models in mice. Int. J. Morphol. , 41(1) :51-58, 2023. SUMMARY: An experimental morphological and morphometric study of the antifibrotic function of blueberry and grape extracts was carried out on a model of lung injury in mice induced by intraperitoneal administration of bleomycin. During intraperitoneal administration of bleomycin to mice, acute and subacute damage to the pulmonary system was noted. Both patterns had the same prevalence and severity. The administration of polyphenolic extracts of blueberry and grape to mice showed a significant reduction in the severity of the acute and subacute pattern of lung injury. Blueberry and grape extracts reduce the acute phase of damage to the microvasculature, enhance phagocytic function, have an anti-inflammatory effect, reducing the degree of lymphohistiocytic infiltration and locoregional foci of residual inflammatory effects.
Very-Low-Voltage Charge Pump Topologies for IoT Applications

This paper presents and discusses two Dickson charge pumps that are capable of working with a supply voltage lower than the MOS threshold voltage and are particularly suited for energy-constrained applications. Specifically, the paper includes a theoretical analysis of a previous topology introduced by the authors, and then it discusses a novel topology which solves drawbacks of the previous one. The paper also includes a comparison with other conventional topologies, namely the latched and the four-phase charge pumps, that shows the inherent advantage of the proposed solutions also in the range of input values higher than the MOS threshold voltage. 2-, 4-, and 6- stage versions of the conventional and proposed CPs have been fabricated in a 65-nm standard CMOS technology having a MOS threshold voltage equal to about 440 mV. Experimental results confirm the analytical predictions since the proposed topologies work at input voltage equal to 300 mV with a current load equal to $20~\mu \text{A}$ and also confirm the advantages of the two proposed solutions in terms of settling time, output current drivability and output power density for voltages slightly higher (450 mV) than the MOS threshold voltage.
Effect of pressure of vanadium nitride using XRD and DFT

ABSTRACT In this study, synchrotron radiation X-ray diffraction is used to study the equation of state of vanadium nitride in a diamond anvil cell at room temperature. Experimental results reveal the high stability of the cubic structure up to 76 GPa pressure. In addition, the bulk modulus of vanadium nitride derived from XRD data is K 0 = 328.4(2.8) GPa with K 0′ = 5.86(0.14). Finally, the high pressure compression behavior is investigated to 80 GPa using density functional theory of first-principle calculations, and the obtained bulk elastic modulus is 316 GPa. Vanadium nitride is not magnetic in the whole pressure range and is metallic. Highlights • We have investigated the equation of state of VN under high pressure with silicone oil as the pressure transmitting medium to 76 GPa. • It was found that the face-centred cubic structure of VN maintains the maximum pressure of 76 GPa. • We have carried out first-principles calculations and obtained the equation of state, band structure, and electronic density of state of VN to 80 GPa.
Wall turbulence without walls

Abstract We perform direct numerical simulations of turbulent channels whose inner layer is replaced by an off-wall boundary condition synthesized from a rescaled interior flow plane. The boundary condition is applied within the logarithmic layer, and mimics the linear dependence of the length scales of the velocity fluctuations with respect to the distance to the wall. The logarithmic profile of the mean streamwise velocity is recovered, but only if the virtual wall is shifted to a position different from the location assumed by the boundary condition. In those shifted coordinates, most flow properties are within 5–10 % of full simulations, including the Kármán constant, the fluctuation intensities, the energy budgets and the velocity spectra and correlations. On the other hand, buffer-layer structures do not form, including the near-wall energy maximum, and the velocity fluctuation profiles are logarithmic, strongly suggesting that the logarithmic layer is essentially independent of the near-wall dynamics. The same agreement holds when the technique is applied to large-eddy simulations. The different errors are analysed, especially the reasons for the shifted origin, and remedies are proposed. It is also shown that the length rescaling is required for a stationary logarithmic-like layer. Otherwise, the flow evolves into a state resembling uniformly sheared turbulence.
DTiGNN: Learning drug-target embedding from a heterogeneous biological network based on a two-level attention-based graph neural network.

MOTIVATION In vitro experiment-based drug-target interaction (DTI) exploration demands more human, financial and data resources. In silico approaches have been recommended for predicting DTIs to reduce time and cost. During the drug development process, one can analyze the therapeutic effect of the drug for a particular disease by identifying how the drug binds to the target for treating that disease. Hence, DTI plays a major role in drug discovery. Many computational methods have been developed for DTI prediction. However, the existing methods have limitations in terms of capturing the interactions via multiple semantics between drug and target nodes in a heterogeneous biological network (HBN). METHODS In this paper, we propose a DTiGNN framework for identifying unknown drug-target pairs. The DTiGNN first calculates the similarity between the drug and target from multiple perspectives. Then, the features of drugs and targets from each perspective are learned separately by using a novel method termed an information entropy-based random walk. Next, all of the learned features from different perspectives are integrated into a single drug and target similarity network by using a multi-view convolutional neural network. Using the integrated similarity networks, drug interactions, drug-disease associations, protein interactions and protein-disease association, the HBN is constructed. Next, a novel embedding algorithm called a meta-graph guided graph neural network is used to learn the embedding of drugs and targets. Then, a convolutional neural network is employed to infer new DTIs after balancing the sample using oversampling techniques. RESULTS The DTiGNN is applied to various datasets, and the result shows better performance in terms of the area under receiver operating characteristic curve (AUC) and area under precision-recall curve (AUPR), with scores of 0.98 and 0.99, respectively. There are 23,739 newly predicted DTI pairs in total.
Classical and modern heuristics for the vehicle routing problem

This article is a survey of heuristics for the Vehicle Routing Problem. It is divided into two parts: classical and modern heuristics. The first part contains well-known schemes such as, the savings method, the sweep algorithm and various two-phase approaches. The second part is devoted to tabu search heuristics which have proved to be the most successful metaheuristic approach. Comparative computational results are presented.
In Vitro Evaluation of Bioavailability of Se from Daily Food Rations and Dietary Supplements

Bioavailability refers to a fraction of a substance that is potentially absorbed from the gastrointestinal tract and enters the systemic circulation (blood). This term is related to various substances, including minerals, that are present in a complex matrix of food which is consumed every day as natural products and pharmaceutical preparations, e.g., dietary supplements. The purpose of the study was to assess the bioavailability of Se from selected dietary supplements, with the simultaneous assessment of the effect the diet type (standard, basic and high-residue diets) has on relative bioavailability. The research included a two-stage in vitro model of digestion using cellulose dialysis tubes of the food rations with the addition of dietary supplements. Se was determined using the ICP-OES method. The bioavailability of Se from dietary supplements, in the presence of food matrix, was determined to be within the range of 19.31–66.10%. Sodium selenate was characterized by the highest value of this parameter, followed by organic forms and sodium selenite. The basic diet, characterized by moderate protein and high carbohydrate and fiber contents, positively influenced the bioavailability of Se. The bioavailability of Se was also influenced by the pharmaceutical form of the product—the highest was for tablets, followed by capsules and coated tablets.
Vascular Progenitor Cells: From Cancer to Tissue Repair

Vascular progenitor cells are activated to repair and form a neointima following vascular damage such as hypertension, atherosclerosis, diabetes, trauma, hypoxia, primary cancerous lesions and metastases as well as catheter interventions. They play a key role not only in the resolution of the vascular lesion but also in the adult neovascularization and angiogenesis sprouting (i.e., the growth of new capillaries from pre-existing ones), often associated with carcinogenesis, favoring the formation of metastases, survival and progression of tumors. In this review, we discuss the biology, cellular plasticity and pathophysiology of different vascular progenitor cells, including their origins (sources), stimuli and activated pathways that induce differentiation, isolation and characterization. We focus on their role in tumor-induced vascular injury and discuss their implications in promoting tumor angiogenesis during cancer proliferation and migration.
Bioprocessing of broiler feathers to produce biomethane

SUMMARY Feathers are among the main by-products of the poultry meat industry and are a serious environmental and health liability, with millions of tons being burned or buried annually. However, given their protein nature, they can be used as an energy matrix based on methane-rich biogas, the so-called biomethane. In theory, energy conversion is very cost-effective and would be employable to generate heat or electricity for the producer or the industry. This review presents biotechnological aspects to increase the production of methane-rich biogas (MRB). Topics such as feather quality, keratin hydrolysis (by keratinolytic microorganisms, by alkali and by reducing agents), keratin extraction by rapid vapour explosion, methane production, inoculants for anaerobic digestion and ammonia stripping are addressed.