Miracle-dz/UniFAR-FTUs huggingface dataset:
(In)commensurability, scaling, and multiplicity of friction in nanocrystals and application to gold nanocrystals on graphite

The scaling of friction with the contact size A and (in)commensurabilty of nanoscopic and mesoscopic crystals on a regular substrate are investigated analytically for triangular nanocrystals on hexagonal substrates. The crystals are assumed to be stiff, but not completely rigid. Commensurate and incommensurate configurations are identified systematically. It is shown that three distinct friction branches coexist, an incommensurate one that does not scale with the contact size (A(0)) and two commensurate ones which scale differently (with A(1/2) and A) and are associated with various combinations of commensurate and incommensurate lattice parameters and orientations. This coexistence is a direct consequence of the two-dimensional nature of the contact layer, and such multiplicity exists in all geometries consisting of regular lattices. To demonstrate this, the procedure is repeated for rectangular geometry. The scaling of irregularly shaped crystals is also considered, and again three branches are found (A(1/4), A(3/4), A). Based on the scaling properties, a quantity is defined which can be used to classify commensurability in infinite as well as finite contacts. Finally, the consequences for friction experiments on gold nanocrystals on graphite are discussed.
Cosmic ray origins in supernova blast waves

We extend the self-similar solution derived by Chevalier for a Sedov blast wave accelerating cosmic rays (CR) to show that the Galactic CR population can be divided into: (A) CR with energies above ~200GeV released upstream during CR acceleration by supernova remnants (SNR), (B) CR advected into the interior of the SNR during expansion and then released from the SNR at the end of its life to provide the Galactic CR component below ~200GeV. The intersection between the two populations may correspond to a measured change in the Galactic CR spectral index at this energy.
Manifestations of the rotation and gravity of the Earth in high-energy physics experiments

The inertial (due to rotation) and gravitational fields of the Earth affect the motion of an elementary particle and its spin dynamics. This influence is not negligible and should be taken into account in high-energy physics experiments. Earth's influence is manifest in perturbations in the particle motion, in an additional precession of the spin, and in a change of the constitutive tensor of the Maxwell electrodynamics. Bigger corrections are oscillatory, and their contributions average to zero. Other corrections due to the inhomogeneity of the inertial field are not oscillatory but they are very small and may be important only for the storage ring electric dipole moment experiments. Earth's gravity causes the Newton-like force, the reaction force provided by a focusing system, and additional torques acting on the spin. However, there are no observable indications of the electromagnetic effects due to Earth's gravity.
Search for high energy neutrino point sources in the northern hemisphere with the AMANDA-II neutrino telescope

In this paper we report the most recent survey of the northern sky to search for neutrino point sources using the AMANDA-II telescope. A search for astrophysical neutrinos of energies above a few tens of GeV was performed on the data collected between the years 2000 and 2003 for a total live-time of 807 days. Thanks to a higher reconstruction accuracy and background rejection power compared to past analyses, together with a longer exposure time, a noticeable improvement has been achieved in the sensitivity of the telescope. The sensitivity to individual point sources, assuming a signal energy spectrum proportional to d /dE with a spectral index of 2, is E d /dE GeVcm s , weakly dependent on declination. We have obtained a large sample of neutrino candidates with high reconstructed track quality, consisting of 3329 selected upgoing events. We searched this sample for a signal from point sources. Individual potential neutrino sources belonging to a catalogue of 33 preselected objects were scanned together with the complete northern sky. We report the outcomes of the individual observations and the significance map of the northern sky.
The Moist Quasi-Geostrophic Coupled Model: MQ-GCM 2.0

Abstract. This paper contains a description of recent changes to the formulation and numerical implementation of the Quasi-Geostrophic Coupled Model (Q-GCM), which constitute a major update of the previous version of the model (Hogg et al., 2014). The Q-GCM model has been designed to provide an efficient numerical tool to study the dynamics of multi-scale midlatitude air–sea interactions and their climatic impacts. The present additions/alterations were motivated by an inquiry into the dynamics of mesoscale ocean–atmosphere coupling and, in particular, by an apparent lack of the Q-GCM atmosphere's sensitivity to mesoscale sea-surface temperature (SST) anomalies, even at high (mesoscale) atmospheric resolutions, contrary to ample theoretical and observational evidence otherwise. Major modifications aimed at alleviating this problem include an improved radiative-convective scheme resulting in a more realistic model mean state and associated model parameters; a new formulation of entrainment in the atmosphere, which prompts more efficient communication between the atmospheric mixed layer and free troposphere; and an addition of a temperature-dependent wind component in the atmospheric mixed layer and the resulting mesoscale feedbacks. The most drastic change is, however, the inclusion of moist dynamics in the model, which may be key to midlatitude ocean–atmosphere coupling. Accordingly, this version of the model is to be referred to as the MQ-GCM model. Overall, the MQ-GCM model is shown to exhibit a rich spectrum of behaviors reminiscent of many of the observed properties of the Earth's climate system. It remains to be seen whether the added processes are able to affect in fundamental ways the simulated dynamics of the midlatitude ocean–atmosphere system's coupled decadal variability.
Land-cover classification using both hyperspectral and LiDAR data

The increased availability of data from different satellite and airborne sensors from a particular scene makes it desirable to jointly use data from multiple data sources for improved information extraction and classification. In particular, hyperspectral sensors provide valuable spectral information that can be used to discriminate different classes of interest, but they do not provide structural and elevation information. On the other hand, LiDAR data can extract useful information related to the size, structure and elevation of different objects, but cannot model the spectral characteristics of different materials. In this paper, a new classification framework is proposed by considering the integration of hyperspectral and LiDAR data. In this case, the recently introduced theoretically sound attribute profile (AP) is considered to model the spatial information of LiDAR and hyperspectral data. In parallel, in order to reduce the redundancy of the hyperspectral data and address the so-called curse of dimensionality, supervised feature extraction techniques are taken into account. Then, the new features obtained by the AP and the supervised feature extraction techniques are concatenated into a stacked vector. The final classification map is achieved by using either support vector machine or random forest classification techniques. The proposed method was applied on two data sets and the obtained results were compared in terms of classification accuracies and CPU processing time. From the results it can be concluded that the proposed method can classify the integration of hyperspectral and LiDAR data accurately in a very acceptable CPU processing time. It should be noted that the proposed method is fully automatic and there is no need to set any parameters to increase the favourability of the proposed method.
MASSIVE QUIESCENT CORES IN ORION: DYNAMICAL STATE REVEALED BY HIGH-RESOLUTION AMMONIA MAPS

We present combined Very Large Array and Green Bank Telescope images of NH3 inversion transitions (1, 1) and (2, 2) toward OMC2 and OMC3. We focus on the relatively quiescent Orion cores, which are away from the Trapezium cluster and have no sign of massive protostars or evolved star formation. The 5″ angular resolution and 0.6 km s−1 velocity resolution enable us to study the thermal and dynamic state of these cores at ∼0.02 pc scales, comparable to or smaller than those of the current dust continuum surveys. We measure temperatures for a total of 30 cores, with average masses of 11 M☉, radii of 0.039 pc, virial mass ratio , and critical mass ratio . Twelve sources contain Spitzer protostars. The thus defined starless and protostellar subsamples have similar temperature, line width, but different masses, with an average of 7.3 M☉ for the former and 16 M☉ for the latter. Compared to other Gould Belt dense cores, more Orion cores have a high gravitational-to-kinetic energy ratio and more cores have a larger than unity critical mass ratio. Orion dense cores have velocity dispersions similar to those of cores in low-mass star-forming regions but larger masses for given size. Some cores appear to have truly supercritical gravitational-to-kinetic energy ratios, even when considering significant observational uncertainties: thermal and non-thermal gas motions alone cannot prevent collapse.
Catalysis of Schwinger vacuum pair production

We propose a new catalysis mechanism for nonperturbative vacuum electron-positron pair production, by superimposing a plane-wave x-ray probe beam with a strongly focused optical laser pulse, such as is planned at the Extreme Light Infrastructure (ELI) facility. We compute the absorption coefficient arising from vacuum polarization effects for photons below threshold in a strong electric field. This setup should facilitate the (first) observation of this nonperturbative QED effect with planned light sources such as ELI yielding an envisioned intensity of order 10{sup 26} W/cm{sup 2}.
Continuum Kinetic and Multi-Fluid Simulations of Classical Sheaths

The kinetic study of plasma sheaths is critical, among other things, to understand the deposition of heat on walls, the effect of sputtering, and contamination of the plasma with detrimental impurities. The plasma sheath also provides a boundary condition and can often have a significant global impact on the bulk plasma. In this paper, kinetic studies of classical sheaths are performed with the continuum code, Gkeyll, that directly solves the Vlasov-Poisson/Maxwell equations. The code uses a novel version of the finite-element discontinuous Galerkin (DG) scheme that conserves energy in the continuous-time limit. The electrostatic field is computed using the Poisson equation. Ionization and scattering collisions are included, however, surface effects are neglected. The aim of this work is to introduce the continuum-kinetic method and compare its results to those obtained from an already established finite-volume multi-fluid model also implemented in Gkeyll. Novel boundary conditions on the fluids allow the sheath to form without specifying wall fluxes, so the fluids and fields adjust self-consistently at the wall. The work presented here demonstrates that the kinetic and fluid results are in agreement for the momentum flux, showing that in certain regimes, a multi-fluid model can be a useful approximation for simulating the plasma boundary. There are differences in the electrostatic potential between the fluid and kinetic results. Further, the direct solutions of the distribution function presented here highlight the non-Maxwellian distribution of electrons in the sheath, emphasizing the need for a kinetic model.
Inaudible Sounds From Appliances as Anchors: A New Signal of Opportunity for Indoor Localization

The indoor localization still has challenges when compromising accuracy, cost, scalability, and so on. In recent years, signals of opportunity have been widely used for low-cost and scalable indoor localization services, especially in complex and changeable environments. In this article, a new opportunistic signal for indoor localization is proposed using the inaudible sound pattern from the appliances, which has superiority in long-term existence, stability, high availability, and privacy protection. The short-time Fourier transformed feature is exploited with a random forest model to recognize the appliance, and a promised matching algorithm is proposed to avoid any false detection. The recognized appliances can be used as standalone location indicators or supplements, such as loop closure pairs and additional landmarks, to the existed indoor localization system. The experimental results show that the appliance recognition achieves a recall rate of 79.66% under 100% precision, and a 95.43% improvement of localization accuracy is obtained to the pedestrian dead reckoning system over a 621.58-m trajectory.
Blazar candidates beyond redshift 4 observed with GROND

The search for extremely massive high-redshift blazars is essential to put strong constraints on the supermassive black hole formation theories. Up to now, the few blazars known to have a redshift larger than 4 have been discovered serendipitously. We try a more systematic approach. Assuming radio-loudness as a proxy for the jet orientation, we select a sample of extremely radio-loud quasars. We measure their black hole masses with a method based on fitting the thermal emission from the accretion disc. We achieve a precision of a factor of 2 for our measures, thanks to the observations performed with the Gamma-Ray Burst Optical NearInfrared Detector (GROND). The infrared to optical GROND data allow us to observe directly the peak of the disc emission, thus constraining the overall disc luminosity. We obtain a small range of masses that peak at 10 9.3 M� . If some of our candidates will be confirmed as blazars, theseresultswouldintroduceinterestingconstraintsonthemassfunctionofextremelymassive black holes at very high redshift. Moreover, all our blazar candidates have high accretion rates. This result, along with the high masses, opens an interesting view on the need of a fast growth of the heaviest black holes at very high redshift.
Characterization and Modelling of Multiscale Natural Fractures in Shale Reservoirs: A Case Study from a Block in the Southern Sichuan Basin

Natural fractures are vital to the efficiencies of drilling and completion operation. The purpose of this paper is to characterize and model multiscale natural fractures in shale reservoirs. Based on the seismic and log responses, as well as outcrop and core observations, we divide natural fractures into Macro, Meso, and Micro three scales. Macroscale fractures are the faults picked directly in seismic profiles. Also, Mesoscale fractures are the natural fracture corridors analyzed by ant tracking technique in 3D seismic data. Furthermore, Microscale fractures are the fractures observed in imaging logs and cores. The fracture intensity is obtained by the correlation between ant tracking attributes and fracture density in borehole. The fracture aperture, dip, and azimuth are three main parameters, which are recognized by the loggings and cores. Stochastic modelling is applied to factures. We find that faults identified by the ant tracking result are excellent in line with Macroscale faults interpreted directly from seismic data. In addition, Mesoscale fractures are indicated from the ant tracking result, which are in accord with breakpoint in the well and in keeping with tectonic history of the area. Such high consistency indicates the ant tracking result is reliable. Moreover, image logs and cores reveal that it mainly develops high angle natural fractures and the fracture aperture is about 1 mm. The fracture strike includes three sets (NNW-SSE, NE-SW, and NNE-SSW). The distribution of the natural fractures in discrete fracture network (DFN) system is distributed controlled by the ant tracking result. Comparing the histograms of DFN results and fracture characterized by seismic and logging responses, as well as outcrop and core observation, it suggests that the major part of the observed natural fractures is retained into our DFN model.
Post‐earthquake damage recognition and condition assessment of bridges using UAV integrated with deep learning approach

Rapid and accurate assessment of the damage to bridge structures after an earthquake can provide a basis for decision‐making regarding post‐earthquake emergency work. However, the traditional structural damage inspection techniques are subjective, time‐consuming, and inefficient. This paper proposed a framework for rapid post‐earthquake structural damage inspection and condition assessment by integrating the technologies of satellite, unmanned aerial vehicle (UAV), and smartphone with the deep learning approach. The images of structural components of post‐earthquake bridges can be obtained by UAVs and smartphones. Furthermore, the multi‐task high‐resolution net (MT‐HRNet) model was adopted to recognize the structural components and damage conditions by weighting and combining the loss functions of a single‐task HRNet model. The performance of the proposed MT‐HRNet model and the single‐task HRNet model was verified based on the Tokaido dataset, which includes 2000 images of post‐earthquake bridges. The results showed that the MT‐HRNet model and the HRNet model exhibited equivalent recognition accuracy, while the number of floating‐point‐operations (FLOPs) and the parameters of the MT‐HRNet model were reduced by 46.48% and 49.58% compared with the HRNet model. In addition, a method for the determination of the safety risk level of the post‐earthquake bridge structures was developed, and the evaluation indices were established by considering the damage type, the spalling area, and the width of cracks as well as the recognition statistics of all images in Tokaido dataset. This study will provide a valuable reference for the rapid determination of structural safety level and the corresponding treatment measures of post‐earthquake bridges.
Effects of Cooperative Learning Structures in Physical Education: A Systematic Review

One possibility of successfully dealing with the increasing heterogeneity of students is cooperative learning. However, this learning structure is still rarely used in physical education. To substantiate positive effects of cooperative learning in physical education and to support the commitment of pedagogical staff, the aim of this research was to evaluate physical, social, behavioral and psychological effects of interventions implementing cooperative learning structures in physical education lessons. The current systematic review was designed based on the PRISMA guidelines. Five databases were used to select articles that evaluated effects of cooperative learning in physical education of children and adolescents with an average age within the range of five to 19 years in August 2022. Studies had to report quantitative measured outcomes of cooperative learning. Two authors independently screened studies for eligibility and assessed risk of bias. Descriptive analyses were conducted to evaluate intervention effects. In total, 23 articles reporting 36 outcomes were included in the analysis with an overall sample size of 3699 children and adolescents aged between 6.5 and 17.5 years. Summarized 71% of all reported outcomes resulted in significant positive effects on cooperative learning compared to traditional teaching styles with most positive effects on social outcomes (92%). It is recommended to integrate different teaching styles, e.g. cooperative learning, to improve children’s development and to engage them in lifelong physical activity. Furthermore, interdisciplinary cooperation (e.g. between researchers and pedagogical school staff) should be sought in order to develop pedagogical approaches that consider the needs and prerequisites of students.
Models of Analysis of Credible Deviation from Speed Limits on Two-Lane Roads of Bosnia and Herzegovina

Any deviation of speed in a traffic flow from a speed limit represents a potential risk of traffic accidents, so speed management appears as an imperative. However, an inadequately set speed limit often causes drivers’ noncompliance to it in the conditions of real traffic flow. By determining the value of exceeding the speed limit according to vehicle classes, it is possible to recommend a credible speeding value that can be considered credible up to a value above the speed limit. In this paper, deterministic multistep mathematical models of speed deviation from the speed limit as a function of longitudinal gradient for the proposed vehicle classes were developed. A total of 11 measuring sections with different traffic flow types were analyzed. Based on a detailed analysis of speeding, models for the deviation of the 15th, 50th, and 85th percentiles were obtained, with the aim of adjusting the credible deviation to control measures. The results obtained in this study were compared with a survey of traffic flow speeding on two-lane roads conducted in Serbia.
Nielsen Identity and the Renormalization Group Functions in an Abelian Supersymmetric Chern-Simons Model in the Superfield Formalism

In this paper we study the Nielsen identity for the supersymmetric Chern-Simons-matter model in the superfield formalism, in three spacetime dimensions. The Nielsen identity is essential to understand the gauge invariance of the symmetry breaking mechanism, and it is calculated by using the BRST invariance of the model. We discuss the technical difficulties in applying this identity to the complete effective superpotential, but we show how we can study in detail the gauge independence of one part of the effective superpotential, $K_{eff}$. We calculate the renormalization group functions of the model for arbitrary gauge-fixing parameter, finding them to be independent of the gauge choice. This result can be used to argue that $K_{eff}$ also does not depend on the gauge parameter. We discuss the possibility of the extension of these results to the complete effective superpotential.
Prediction of Deleterious Non-Synonymous Single Nucleotide Polymorphisms (Nssnps) of Human TLR7 Gene

Toll-like receptors (TLRs) play a key role in innate immune response activation against viruses. TLR7, one of the TLRs family, is potentially important in controlling viral infection and the production of vaccines against the virus. The wide spectrum of discrepancy in response to antiviral drugs among different populations which is emerged by some pandemics like COVID-19 might be due to their different TLR7 single nucleotide polymorphisms (SNPs). The present study aimed to investigate the consequences of 401 non-synonymous missense SNPs (nsSNPs) within TLR7 on its protein structure, stability, and function by using specific bioinformatics tools. Seven bioinformatics tools were used to investigate 401 TLR7 nsSNPs from the dbSNP database. The results showed that the six variations, rs1171508003 (R262H), rs35160120 (F580S), rs968155471 (H587Q), rs202028806 (Y871D), rs1331496205 (W933S), and rs181600414 (R1004W), were found to be extremely deleterious by all of the employed bioinformatics tools. All six variations showed an impact on the protein’s structure, function, and stability. Among them, Y871D (rs202028806) and R1004W (rs181600414) were revealed as the most damaging nsSNPs. This study suggested that the predicted six damaging variants of TLR7 could indirectly or directly destabilize the structure of protein and deviate its function to some extent.