Miracle-dz/UniFAR-FTUs huggingface dataset:
Rule-Based Control Algorithm for Active Tuned Mass Dampers

A rule-based control algorithm for active tuned mass dampers (TMDs) is proposed. First, perturbation solutions of the linear quadratic regulator (LQR) feedback gains for the active TMD system are derived. Using these solutions, interaction of the TMD and the actuator force is discussed in detail. Then, an intelligent rule-based control scheme is constructed based on this discussion along with a previously developed semiactive control method. Fuzzy logic is incorporated with the rule bases to implement analytical and/or intuitive knowledge about the TMD into control rules. The algorithm consists of two parts: (1) a variable gain displacement feedback control; and (2) a variable TMD damping control. The first one is applied when the TMD amplitude is small to make the TMD more effective, and the second one is applied when the TMD amplitude is large to dissipate the energy. Treatment of constraints on control force capacity and TMD displacement is emphasized in construction of the controller. Performance of the proposed method is demonstrated by numerical studies of continuous structures with widely spaced natural frequencies subject to seismic excitation.
Metabolic Disruption of Gold Nanospheres, Nanostars and Nanorods in Human Metastatic Prostate Cancer Cells

Nanomaterials offer a broad spectrum of applications in biomedicine. The shapes of gold nanoparticles could modulate tumor cell behavior. Spherical (AuNPsp), stars (AuNPst) and rods (AuNPr) shapes of polyethylene glycol coated-gold nanoparticles (AuNPs-PEG) were synthesized. Metabolic activity, cellular proliferation, and reactive oxygen species (ROS) were measured and the impact of AuNPs-PEG in metabolic enzymes function was evaluated by RT-qPCR in PC3, DU145, and LNCaP prostate cancer cells. All AuNPs were internalized, and the different morphologies of AuNPs showed to be an essential modulator of metabolic activity. For PC3 and DU145, the metabolic activity of AuNPs was found to rank in the following order from lowest to highest: AuNPsp-PEG, AuNPst-PEG, and AuNPr-PEG. Regarding LNCaP cells, the AuNPst-PEG were less toxic, followed by AuNPsp-PEG and AuNPr-PEG, but it seems not to be dose-dependent. The proliferation was lower in AuNPr-PEG in PC3 and DU145 cells but was stimulated around 10% in most conditions (0.001–0.1 mM) in LNCaP cells (not statistically significant). For 1 mM, LNCaP cells showed a significant decrease in proliferation only for AuNPr-PEG. The outcomes of the current study demonstrated that different AuNPs conformations influence cell behavior, and the correct size and shape must be chosen considering its final application in the field of nanomedicine.
Assessing risk for rare events

This paper develops a method for assessing the risk for rare events based on the following scenario. There exists a large population with an unknown percentage p of defects. A sample of size N is drawn from the population and, in the sample, 0 defects are drawn. Given these data, we want to determine the probability that no more than n defects will be found in another random sample of N drawn from the population. Estimates on the range of p and n are calculated from a derived joint distribution which depends on p, n and N. Asymptotic risk results based on an infinite sample are then developed. It is shown that these results are applicable even with relatively small sample spaces.
A Scientific Paper Recommendation Framework Based on Multi-Topic Communities and Modified PageRank

Personalized PageRank is a variant of PageRank, widely developed for citation recommendation. However, the personalized PageRank that works with a vast amount and rich scholarly data still results in information overload. Sometimes, junior scholars still need help to arrange queries quickly because of limited domain knowledge. Senior researchers need reference papers regarding a similar topic they intend to search for and related topics as a new insight. In this research, scientific citation recommendation aims to find the most influential papers with similar and related topics. Related topic papers in serendipitous perspectives are reference papers that are novel, diversified and unexpected to a user. The unexpectedness of recommended papers can be papers with different topics to queries but still relevant. To accomplish these challenges, we propose a framework of scientific citation recommendation with serendipitous perspectives. The framework includes feature extraction of an academic citation network, selection of multi-topic communities, and ranking papers in the selected multi-topic communities by modified PageRank. Papers in the chosen communities tend to link to similar and related papers. Modified PageRank is an extension of personalized PageRank, which works on multi-topic communities and manuscript queries. The experiments reveal that the proposed models outperform some models of personalized PageRank and some models of Content-Based Filtering. The multi-topic communities-based models work more effectively than the baselines if they run in a large dataset since the topic communities become more cohesive.
A model for GCR‐particle fluxes in stony meteorites and production rates of cosmogenic nuclides

A model is presented for the differential fluxes of galactic cosmic ray (GCR) particles with energies above 1 MeV inside any spherical stony meteorite as a function of the meteorite's preatmospheric radius and the samples's depth. This model is based on the Reedy-Arnold equations for the energy-dependent fluxes of GCR particles in the moon and is an extension of flux parameters that were derived for several meteorites of various sizes. This flux model is used to calculate the production rates of many cosmogenic nuclides as a function of radius and depth. The peak production rates for most nuclides that are created by the reactions of energetic GCR particles occur near the centers of meteorites with radii of 40 to 70 g cm/sup -2/. The calculated production rates usually agree well with measurements of cosmogenic nuclides in most meteorites. Although the model has some limitations, it reproduces well the basic trends observed for the depth-dependent production of cosmogenic nuclides in stony meteorites of various radii. Some of these production profiles are different from those of other calculations. The chemical dependence of the production rates for several nuclides varies with size and depth.
An electron conductivity model for dense plasmas

An electron conductivity model for dense plasmas is described which gives a consistent and complete set of transport coefficients including not only electrical conductivity and thermal conductivity, but also thermoelectric power, and Hall, Nernst, Ettinghausen, and Leduc–Righi coefficients. The model is useful for simulating plasma experiments with strong magnetic fields. The coefficients apply over a wide range of plasma temperature and density and are expressed in a computationally simple form. Different formulas are used for the electron relaxation time in plasma, liquid, and solid phases. Comparisons with recent calculations and available experimental measurement show the model gives results which are sufficiently accurate for many practical applications.
Enhanced Multi-Task Learning and Knowledge Graph-Based Recommender System

In recent years, the multi-task learning for knowledge graph-based recommender system, termed MKR, has shown its promising performance and has attracted increasing interest, because a recommendation task and a knowledge graph embedding (KGE) task can help each other to improve the recommendation. However, MKR still has two difficult issues. The first is how fully to capture users’ historical behavior pattern in the recommendation task and how fully to utilize deep multi-relation semantic information in the KGE task. The second is how to deal with datasets with different sparsity. Tackling these challenging issues, this paper proposes an enhanced MKR (EMKR) approach with two novelties. First, we propose to utilize the attention mechanism to aggregate users’ historical behavior for more accurately mining preferences in the recommendation task, and utilize the relation-aware graph convolutional neural network to fully capture the deep multi-relation neighborhood features in the KGE task, so as to address the first issue. Second, a two-part modeling strategy is proposed for a better representation of users in the recommendation task to expand the expressive ability of the model for adapting to datasets with different sparsity, so as to address the second issue. Extensive experiments are conducted on widely-used datasets and 11 approaches are used for comparison. The results show that the proposed EMKR can achieve substantial gains over the compared state-of-the-art approaches, especially in the situation where user-item interactions are sparse.
Hydrodynamic stability and breakdown of the viscous regime over riblets

The interaction of the overlying turbulent flow with a riblet surface and its impact on drag reduction are analysed. The ‘viscous regime’ of vanishing riblet spacing, in which the drag reduction produced by the riblets is proportional to their size, is reasonably well understood, but this paper focuses on the behaviour for spacings s+ ≃ 10–20, expressed in wall units, where the viscous regime breaks down and the reduction eventually becomes an increase. Experimental evidence suggests that the two regimes are largely independent, and, based on a re-evaluation of existing data, it is shown that the optimal rib size is collapsed best by the square root of the groove cross-section, ℓg+=Ag+1/2. The mechanism of the breakdown is investigated by systematic DNSs with increasing riblet sizes. It is found that the breakdown is caused by the appearance of long spanwise rollers below y+ ≈ 20, with typical streamwise wavelengths λx+ ≈ 150, that develop from a two-dimensional Kelvin–Helmholtz-like instability of the mean streamwise flow, similar to those over plant canopies and porous surfaces. They account for the drag breakdown, both qualitatively and quantitatively. It is shown that a simplified linear instability model explains the scaling of the breakdown spacing with ℓg+.
Sensitivity of a multi‐layer quasi‐geostrophic β‐channel to the vertical structure of the equilibrium meridional temperature gradient

A multi-layer, quasi-geostrophic, Boussinesq model on a β-channel is used to study the sensitivity of the mid-latitude storm-track to changes in the vertical structure of the zonal mean equilibrium meridional temperature gradient. Motivation for this study is taken from the observation that the doubled CO2 climatology of general-circulation models presents a weaker gradient in the lower troposphere and a stronger gradient in the upper troposphere than in a ‘control’ climatology. It is observed that the dynamics of the quasi-geostrophic model are generally more sensitive to the lower than to the upper tropospheric temperature gradient as a consequence of the importance of shallow eddies. In order to give a good representation of the eddy dynamics it is necessary to use a sufficiently high resolution, which, in the case chosen, proved to be between 10 and 20 tropospheric layers. If only three layers are used the general features of the response are still reproduced, but the model does not represent the sensitivities of the eddy meridional momentum-flux convergence adequately. It is argued that this is connected with the particular choice taken when parametrizing the surface Ekman friction. When a higher-resolution model is used, the eddy momentum-flux convergence proves to be more sensitive to changes in the upper than in the lower meridional equilibrium temperature gradient but is generally much less responsive to these changes than other quantities like the surface eddy potential-vorticity flux or eddy kinetic energy. For each variable the two sensitivities become close to each other as the baroclinicity of the equilibrium flow is increased because of the prevalence of deep eddies in the dynamics, sensitive only to the total radiative equilibrium wind vertical gradient. For very high values of the baroclinicity of the equilibrium flow the sensitivities approach a constant value in accordance with a simple scaling argument.
Classification of Atrial Tachycardia Types Using Dimensional Transforms of ECG Signals and Machine Learning

Accurate non-invasive diagnoses in the context of cardiac diseases are problems that hitherto remain unresolved. We propose an unsupervised classification of atrial flutter (AFL) using dimensional transforms of ECG signals in high dimensional vector spaces. A mathematical model is used to generate synthetic signals based on clinical AFL signals, and hierarchical clustering analysis and novel machine learning (ML) methods are designed for the un-supervised classification. Metrics and accuracy parameters are created to assess the performance of the model, proving the power of this novel approach for the diagnosis of AFL from ECG using innovative AI algorithms.
Sub-cellular quantitative optical diffraction tomography with digital holographic microscopy

Digital holographic microscopy (DHM) is an interferometric technique, providing quantitative mapping of the phase shift induced by semi-transparent microscopic specimens, such as cells, with sub-wavelength resolution along the optical axis. Thanks to actual PCs and CCDs, DHM provides nowadays cost-effective instruments for real-time measurements at very high acquisition rates, with sub-micron transverse resolution. However, DHM phase images do not reveal the threedimensional (3D) internal distribution of refractive index, but a phase shift resulting from a mean refractive index (RI) integrated over the cellular thickness. Standard optical diffraction tomography (ODT) techniques can be efficiently applied to reveal internal structures and to measure 3D RI spatial distributions, by recording 2D DHM phase data for different sample orientations or illumination beam direction, in order to fill up entirely the Ewald sphere in the Fourier space. The 3D refractive index can then be reconstructed, even in the direct space with backpropagation algorithms or from the Fourier space with inverse Fourier transform. The presented technique opens wide perspectives in 3D cell imaging: the DHM-based micro-tomography furnishes invaluable data on the cell components optical properties, potentially leading to information about organelles intracellular distribution. Results obtained on biological specimens will be presented. Morphometric measurements can be extracted from the tomographic data, by detection based on the refractive index contrast within the 3D reconstructions. Results and perspectives about sub-cellular organelles identification inside the cell will also be exposed.
X-ray pulsar profile analysis.

Un modele de profil des pulsations des pulsars RX est developpe. L'emission de la calotte polaire pour les rayons X emis par l'etoile a neutron en phase d'accretion est construite a partir de la distribution d'intensite RX locale pour le rayonnement de plasmas magnetises. Une application a 15 pulsars RX est realisee
Chemical and microbiological characterization of two traditional Mongolian high‐fat dairy products produced in Xilin Gol, China

Abstract Mongolian butter and Tude are traditional high‐fat dairy products produced in Xilin Gol, China, which have unique chemical and microbiological characteristics. Mongolian Tude is made from Mongolian butter, dreg, and flour. In this study, the traditional manufacturing process of Mongolian butter and Tude was investigated for the first time. Mongolian butter was characterized by high‐fat content (99.38 ± 0.63%) and high acidity (77.09 ± 52.91°T), whereas Mongolian Tude was considered a high‐fat (21.45 ± 1.23%) and high‐protein (8.28 ± 0.65%) dairy product obtained by butter, dreg, and flour. Mongolian butter and Tude were proven to be safe for human consumption in terms of benzopyrene content. In addition, Listeria monocytogenes, Staphylococcus aureus, Salmonella, coliforms, and aflatoxin M1 were not detected in the samples. Bacteria and molds were not isolated from Mongolian butter; in contrast, the total count of bacteria and molds in Mongolian Tude was within the range of 4.5 × 102 to 9.5 × 104 and 0 to 2.2 × 105, respectively. Moreover, Lactococcus (41.55%), Lactobacillus (11.05%), Zygosaccharomyces (40.20%), and Pichia (12.90%) were the predominant bacterial and fungal genera, and Lactobacillus helveticus (15.6%), Lactococcus raffinolactis (9.6%), Streptococcus salivarius (8.5%), Pantoea vagans (6.1%), Bacillus subtilis (4.2%), Kocuria rhizophila (3.5%), Acinetobacter johnsonii (3.5%), Zygosaccharomyces rouxii (46.2%), Pichia fermentans (14.7%), and Dipodascus geotrichum (11.7%) were the predominant species in the microbiota of Mongolian Tude. Thus, it can be stated that the microbiota of food products produced by different small families varied significantly. Collectively, the findings presented herein are the first report of chemical and microbiological characterization of products of geographical origin and highlight the need for standardization of manufacturing procedures of Mongolian butter and Tude in the future.
The realm of metaverse: A survey

The next step of digital development is the metaverse, which has the potential to drastically alter how people use technology and expand the range of services available beyond conventional systems that can be accessed online. As the efficiency, performance, and quality of service access reach their peak levels, the focus has shifted to the user experience. Due to this, there is an increasing demand for more involved and thorough customer service, and service providers are willing to increase their present standards. Consumers are genuinely asking for tactile and immersive elements in their digital interfaces, but these features can only be made possible by the metaverse's potentially futuristic subfields of virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR). However, the metaverse may not be widely used due to significant security and privacy issues either from underlying technology or produced by the new digital environment. A variety of fundamental problems, such as scalability and interoperability, can arise in terms of ensuring security for the metaverse because of the metaverse's inherent properties, such as immersive realism, sustainability, and heterogeneity. In this survey, we propose a hypothetical meta‐stack framework to understand the various components in the realm of metaverse and then provide wide‐ranging insights on the most recent development in metaverse realm in the context of cutting‐edge technologies, security vulnerabilities and preventive measures specific to the metaverse and the research challenges pertaining to metaverse.
Stirring the Frequentist Pot with a Dash of Bayes

Rethinking Social Inquiry (RSI) is a key turning point in a long arc of development and contestation within and between qualitative and quantitative research methods in the social sciences. It builds on and further advances three important trends in these research methods: a renaissance in qualitative methods in the last decade, the continuing refinement of statistical and formal methods, and a nascent convergence of methodologists of all kinds behind a more pluralistic vision of methodology that includes growing interest in multimethod work. RSI achieves these contributions not just substantively but symbolically, bringing together leading methodologists in the quantitative and qualitative traditions, most notably the editors themselves, to address the tough issue of what would constitute shared standards for good research regardless of method. Although much of the initial commentary on RSI will no doubt focus on its critiques of Designing Social Inquiry, I suspect that in the long run the subtitle of RSI (“Diverse Tools, Shared Standards”) better captures what will be its lasting contribution to the social sciences.
BETTER ASTROMETRIC DE-BLENDING OF GRAVITATIONAL MICROLENSING EVENTS BY USING THE DEFERENCE IMAGE ANALYSIS METHOD

As an efficient method to detect blending of general gravitational microlensing events, it is proposed to measure the shift of source star image centroid caused by microlensing. The conventional method to detect blending by this method is measuring the difference between the positions of the source star image point spread function measured on the images taken before and during the event (the PSF centroid shift, c,PSF). In this paper, we investigate the difference between the centroid positions measured on the reference and the subtracted images obtained by using the difference image analysis method (DIA centroid shift, c.DIA), and evaluate its relative usefulness in detecting blending over the conventional method based on c,PSF measurements. From this investigation, we find that the DIA centroid shift of an event is always larger than the PSF centroid shift. We also find that while c,PSF becomes smaller as the event amplification decreases, c.DIA remains constant regardless of the amplification. In addition, while c,DIA linearly increases with the increasing value of the blended light fraction, c,PSF peaks at a certain value of the blended light fraction and then eventually decreases as the fraction further increases. Therefore, measurements of c,DIA instead of c,PSF will be an even more efficient method to detect the blending effect of especially of highly blended events, for which the uncertainties in the determined time scales are high, as well as of low amplification events, for which the current method is highly inefficient.
Robust human motion detection via fuzzy-set-based image understanding

This paper presents an image understanding approach to monitor human movement and identify the abnormal circumstance by robust motion detection for the care of the elderly in a home-based environment. In contrast to the conventional approaches which apply either a single feature extraction scheme or a fixed object model for motion detection and tracking, we introduce a multiple feature extraction scheme for robust motion detection. The proposed algorithms include 1) multiple image feature extraction including the fuzzy compactness based detection of interesting points and fuzzy blobs, 2) adaptive image segmentation via multiple features, 3) Hierarchical motion detection, 4) a flexible model of human motion adapted in both rigid and non-rigid conditions, and 5) Fuzzy decision making via multiple features.
Transition rates and wavelengths in the X-ray spectra of nickel-like ions (Ag XX-Pb LV)

The n = 3-4 transitions observed in the X-ray spectra of nickel-like ions (Ag XX-Pb LV) have been investigated theoretically by the multiconfigurational Dirac-Fock approach. The effect of the level crossings on the transitions rates of low excitation depopulation transitions is considered both for forbidden and for allowed transitions. A comparison of the ad initio wavelengths with the available observations for the resonance transitions shows that some experimental data must be refined.