Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the subgrid-scale Prandtl number for large-eddy simulation

The subgrid-scale (SGS) Prandtl number ( $Pr$ ) is an important parameter in large-eddy simulation. Prior models often assume that the ‘ $-5/3$ ’ inertial subrange scaling applies to the wavenumber range from 0 to $k_{\unicode[STIX]{x1D6E5}}$ (the wavenumber corresponding to the filter scale $\unicode[STIX]{x1D6E5}$ ) and yield a $Pr$ that is stability-independent and scale-invariant, which is inconsistent with experimental data and the results of dynamic models. In this study, the SGS Prandtl number is revisited by solving the co-spectral budgets of momentum and heat fluxes in an idealized but thermally stratified atmospheric surface layer. The SGS Prandtl number from the co-spectral budget model shows a strong dependence on the atmospheric stability and increases (decreases) as the atmosphere becomes stable (unstable), which is in good agreement with recent field experimental data. The dependence of $Pr$ on the filter scale is also captured by the co-spectral budget model: as the filter scale becomes smaller, the SGS Prandtl number decreases. Finally, the value of SGS Prandtl number under neutral conditions is shown to be caused by the dissimilarity between momentum and heat in the pressure decorrelation term and the flux transfer term. When the dissimilarity exists only in the flux transfer term, the fact that under neutral conditions the SGS Prandtl number is usually smaller than the turbulent Prandtl number for Reynolds-averaged Navier–Stokes simulations is an indication of a stronger spectral transfer coefficient for heat than for momentum. The model proposed in this study can be readily implemented.
Stability of orthogonality preserving mappings and the ort hogonality equation

To study stability of orthogonality preserving mappings,the(δ,e)-approximately orthogonality preserving mapping is introduced,which is proved to be bounded.It is proved that linear orthogonality preserving mapping is stable in the sense of linear(δ,e)-approximately orthogonality preserving.A sufficient condition for a linear mapping to be a(δ,e)-approximately orthogonality preserving mapping is given.Stability of the orthogonality equation is established.
No Firewalls in HST or Matrix Theory

We use the formalisms of Holographic Space-time (HST) and Matrix Theory[11] to investigate the claim of [1] that old black holes contain a firewall, i.e. an in-falling detector encounters highly excited states at a time much shorter than the light crossing time of the Schwarzschild radius. In both formalisms there is no dramatic change in particle physics inside the horizon until a time of order the Schwarzschild radius. The Matrix Theory formalism has been shown to give rise to an S-matrix, which coincides with effective supergravity for an infinite number of low energy amplitudes. We conclude that the firewall results from an inappropriate use of quantum effective field theory to describe fine details of localized events near a black hole horizon. In both HST and Matrix Theory, the real quantum gravity Hilbert space in a localized region contains many low energy degrees of freedom that are not captured in QU(antum) E(ffective) F(ield) T(heory) and omits many of the high energy DOF in QUEFT.
A nonlocal energy‐informed neural network for isotropic elastic solids with cracks under thermomechanical loads

In this paper, a nonlocal energy‐informed neural network is proposed to characterize the deformation behaviors of elastic solids with cracks subjected to thermomechanical loads in the framework of ordinary state‐based peridynamics. Based on principle of virtual work, a nonlocal energy approach is developed to recast the solution to peridynamic equilibrium equation as a problem of minimizing the potential energy of the system, which automatically satisfies the traction‐free boundary conditions. Meanwhile, the energy representation of physical system can be treated as the loss function for machine learning methods. Therefore, a nonlocal energy‐informed neural network is constructed to approximate the solution of the system. A distinct advantage of the proposed neural network is that the strain energy is expressed in terms of spatial integration instead of spatial derivatives, which avoids the invalidation of automatic differentiation at the crack surfaces in original physics‐informed neural networks. To demonstrate the convergence and accuracy of the proposed neural network, a series of problems in solid and fracture mechanics are conducted, and compared with results from analytical solutions or classical numerical methods. Additionally, for elastic material containing initial cracks, displacement extrapolation method is encoded into the proposed neural network to evaluate the static stress intensity factor.
Identification of Immunoglobulin Gene Rearrangement Biomarkers in Multiple Myeloma through cfDNA-Based Liquid Biopsy Using tchDNA-Seq

Simple Summary Multiple myeloma has remained largely incurable despite improvements in patient outcomes in the era of targeted anti-myeloma agents. Targeted therapies used in oncology in recent years have significantly changed the way myeloma is treated and thus improved the prognosis for patients. We sought to identify new biomarkers for patient stratification and the prediction of treatment outcomes by applying targeted capture hybridization DNA sequencing (tchDNA-Seq) technology. We have evaluated plasma and bone marrow samples from a homogenous population of 23 patients, which IG rearrangements have the potential to provide important diagnostic, prognostic, and predictive information. We will likely be able to offer a more targeted and risk-adapted therapeutic approach to MM patients at different stages of their disease guided by these potential biomarkers. Abstract Multiple myeloma (MM) is a hematological malignancy characterized by the clonal proliferation of pathogenic CD138+ plasma cells (PPCs) in bone marrow (BM). Recent years have seen a significant increase in the treatment options for MM; however, most patients who achieve complete the response ultimately relapse. The earlier detection of tumor-related clonal DNA would thus be very beneficial for patients with MM and would enable timely therapeutic interventions to improve outcomes. Liquid biopsy of “cell-free DNA” (cfDNA) as a minimally invasive approach might be more effective than BM aspiration not only for the diagnosis but also for the detection of early recurrence. Most studies thus far have addressed the comparative quantification of patient-specific biomarkers in cfDNA with PPCs and BM samples, which have shown good correlations. However, there are limitations to this approach, such as the difficulty in obtaining enough circulating free tumor DNA to achieve sufficient sensitivity for the assessment of minimal residual disease. Herein, we summarize current data on methodologies to characterize MM, and we present evidence that targeted capture hybridization DNA sequencing (tchDNA-Seq) can provide robust biomarkers in cfDNA, including immunoglobulin (IG) rearrangements. We also show that detection can be improved by prior purification of the cfDNA. Overall, liquid biopsies of cfDNA to monitor IG rearrangements have the potential to provide important diagnostic, prognostic, and predictive information in patients with MM.
Ultracompact electro-optical logic gates based on graphene–silica metamaterial

Abstract. We propose and simulate numerically a permittivity-tunable metamaterial channel, which is composed of alternating layers of graphene and silica. The real part of the permittivity of the proposed metamaterial can be tuned from a positive value to a negative one for a broadband width. Furthermore, optical waves can pass through the metamaterial channel only when its permittivity is tuned to zero. Inspired by this intriguing property of the graphene–silica metamaterial, three basic electro-optical logic gates, including NOT, NOR, and NAND gates, were proposed and numerically investigated by using the finite element method. Taking advantage of the permittivity-tunable property of graphene, the working wavelength of the proposed electro-optical logic gates can be actively controlled by tuning the external voltage applied on the graphene–silica metamaterial. These tunable and ultracompact electro-optical logic gates could benefit the development of nanoscale optical devices for highly integrated photonic circuits.
Preparation of cellulose aerogels as a nano-biomaterial from lignocellulosic biomass

Cellulose aerogels were prepared from various delignified wood fibers by using a peracetic acid pretreatment. The effect of the treatment conditions was evaluated to find the optimum condition for the production of a functional cellulose aerogel for use as a nano-biomaterial. The morphologies and the physical properties of the delignified wood fibers were analyzed and compared according to the various conditions of pretreatment. The density and the microstructure were studied for cellulose aerogels produced from delignified wood fibers. The results showed that peracetic acid pretreatment did not have a considerable effect on the average fiber length and the surface; however, acid hydrolysis caused a decrease in the viscosity of the wood fibers and an increase in their density and destroyed the microstructure. According to these results minimizing cellulose degradation while wood pulping is an important consideration in the preparation of cellulose aerogels.
High-Aspect-Ratio Thin-Film Stiffening Structures Made of Atomic-Layer-Deposited Alumina and Its Application to a Scanning Micromirror

This work presents the first high-aspect-ratio alumina stiffening structures, fabricated by combining atomic layer deposition (ALD) of alumina with silicon deep reactive ion etching (Si-DRIE), designed to improve MEMS micromirror devices. By selective etching of an alumina-coated silicon mold, we produced vertical alumina fins with a depth up to <inline-formula> <tex-math notation="LaTeX">$500 \mu \text{m}$ </tex-math></inline-formula> and a width down to <inline-formula> <tex-math notation="LaTeX">$0.13 \mu \text{m}$ </tex-math></inline-formula>, corresponding film-aspect-ratios up to 3800. We applied this stiffening structure to a lightweight MEMS micromirror with <inline-formula> <tex-math notation="LaTeX">$215 \mu \text{m}$ </tex-math></inline-formula> thickness and 2 mm diameter. We characterized the optical flatness and dynamic deformation of this device, which are quantities of interest for compact image projection systems.
Algebraic expression and characteristics of static wrench-closure workspace boundary for planar cable-driven parallel robots

This article proposes a deduction method of algebraic expression of static wrench-closure workspace boundary for planar 3-degree-of-freedom cable-driven parallel robots based on the antipodal theorem. Different from interval method and discretization approach, a more precise algebraic expression of workspace boundary can be obtained without undetermined region or costly computation burden to make the design process and optimization easy and feasible. In order to illustrate and study the conic characteristics of workspace boundary, a case study is conducted on a four-cable-driven 3-degree-of-freedom parallel robot. Simulation results show that the type of conic of boundary depends on the determinant of the specific deduced matrix and corresponds to which cable is collinear with the intersection line. In addition, the results also demonstrate that the boundary of workspace consists of the same type of two conics (i.e. ellipse, parabola, and hyperbola), and the region surrounded by these two quadratic curves is the smallest when the type of the border equation is parabola.
RoaDSaVe: A Robust Lane Detection Method Based on Validity Borrowing From Reliable Lines

Lane detection is a critical but challenging task in autonomous driving, especially in complex scenes. In most of the complicated scenes, only a part of the scene is challenged, and the whole road surface is not involved. We believe that perception of the scene’s structure and using information of reliable areas to detect lanes in challenging areas is the key to robust lane detection. This article proposes a novel lane detection method that can provide a reliable system even in complex situations and meet real-time requirements, called RoaDSaVe. The RoaDSaVe method consists of three major modules: scene awareness, physical inference, and validity effectiveness. The scene awareness module takes advantage of an effective combination of shallow and deep features in a neural network, resulting in a more accurate acquisition of spatial and semantic feature maps. Following that, an optimization method is used to obtain the physical parameters of the lane lines based on road and lane semantic labels. The physical inference module creates an adjusted histogram and considers locations with high scores and reliability to be valid lines. In the validity effectiveness module, the potential points are stimulated by the valid lines. If a potential line did not achieve sufficient validity in the previous module, it can still be considered valid if it meets the required score. The experiments on several public datasets show that the RoaDSaVe lane detection method has significant ability in scenes with local challenges and achieves excellent performance compared to the state-of-the-art methods.
Effect of Leading-Edge Thickness on High-Speed Airfoil-Turbulence Interaction Noise

Airfoil-turbulence interaction noise is studied to understand how thickness changes in the neighborhood of an airfoil leading edge can modify noise generation. The broadband interaction noise is generated by placing an airfoil in the turbulent boundary layer of a wind tunnel’s rectangular duct test section. Four airfoil leading edge configurations are tested at four different airfoil positions, relative to the duct exit plane. Local changes in mean flow properties are produced by both modifying leading edge thickness, and translating the airfoil axially to positions internal and external to the duct. In each configuration, the maximum airfoil thickness, camber distribution, and aerodynamic suction surface are unchanged. Leading edge thickness changes are confined to nearly the first ten percent of the airfoil chord, along the airfoil pressure surface only. Numerical results for the mean flow show that when the airfoil is internal to the duct, changes in thickness result in small changes in incidence between the flow and airfoil leading edge. However, with changes in the airfoil axial position, more significant changes in incidence occur, with large flow angles exhibited at the furthest external position. Measurements show that noise is reduced by several decibels as leading edge thickness increases, over a frequency range of 2-4 kHz when the airfoil lies within the duct, and 1-4 kHz when the airfoil lies external to the duct. The maximum noise reduction occurs at frequencies where the reduced frequency, based on leading edge thickness, is order one. The difference in the spectral noise reduction when the airfoil is translated internal versus external to the duct, suggests that changes in leading edge flow incidence may contribute to the noise reduction observed, and that the mechanism for noise reduction is not purely controlled by the relative turbulence scale to leading edge thickness relationship. To examine this hypothesis a simple model for turbulence-airfoil interaction noise that includes thickness effects is compared with data. Results show that agreement between the theory and the measurements are quite good when the airfoil lies within the duct and the incidence is small. When the airfoil lies external to the duct, significant variation between the theoretical model, which does not account for incidence, and measurements exists.
Financial Statement Restatements and Conditional Conservatism in Companies Listed on B3

This study investigated the association between financial statement restatements and conditional conservatism in Brazilian publicly traded companies listed on B3. A sample of 192 non-financial companies was used from 2012 to 2020. The conditional conservatism was measured using Ball and Shivakumar's (2005) model. The study employed five dimensions of restatement measures, comprising both voluntary and compulsory restatement modalities, and six-panel data regression models was conducted. The results revealed that there is no relationship between voluntary restatement and conservatism. However, there is generally a positive association between compulsory restatement and conservatism. Thus, companies mandated to restatement their reports by the regulator tend to be more conservative, indicating the lower quality of financial reports. These findings provide valuable insights for various external users of accounting information, as restatements can assist companies in understanding negative signals in the market, help investors in better resource allocation, and assist regulators in devising appropriate measures for prevention or punishment.
Methods for the Evaluation of Image Quality: A Review

In medical imaging, information about the patient and possible abnormalities is transferred to the radiologist in two major steps: (i) data acquisition and image formation, and (ii) processing and display. Step one is mainly dependent on technical and physical characteristics of the equipment. Step two includes the vital importance of the performance of the radiologist; i.e. how he or she detects and interprets the structures in the image. The quality of a radiographical procedure must therefore be described with regard to both these steps. The spectrum of possible evaluation methods of importance will be described. The principles, benefits and drawbacks of some of these methods will be given together with examples of their use.
Comparative calibration of IP scanning equipment

Imaging Plates (IP) are diagnostic devices which contain a photostimulable phosphor layer that stores the incident radiation dose as a latent image. The image is read with a scanner which stimulates the decay of electrons, previously excited by the incident radiation, by exposition to a laser beam. This results in emitted light, which is detected by photomultiplier tubes; so the latent image is reconstructed. IPs have the interesting feature that can be reused many times, after erasing stored information. Algorithms to convert signals stored in the detector to Photostimulated luminescence (PSL) counts depend on the scanner and are not available on every model. A comparative cross-calibration of the IP scanner Dürr CR35 BIO, used in ABC laboratory, was performed, using the Fujifilm FLA 7000 scanner as a reference, to find the equivalence between grey-scale values given by the Dürr scanner to PSL counts. Using an IP and a 55Fe β-source, we produced pairs of samples with the same exposition times, which were analysed by both scanners, placing particular attention to fading times of the image stored on IPs. Data analysis led us to the determine a conversion formula which can be used to compare data of experiments obtained in different laboratories and to use IP calibrations available, till now, only for Fujifilm scanners.
Prediction of Non-Home Discharge Following Total Hip Arthroplasty in Geriatric Patients

Introduction The majority of total hip arthroplasty (THA) patients are discharged home postoperatively, however, many still require continued medical care. We aimed to identify important characteristics that predict nonhome discharge in geriatric patients undergoing THA using machine learning. We hypothesize that our analyses will identify variables associated with decreased functional status and overall health to be predictive of non-home discharge. Materials and Methods Elective, unilateral, THA patients above 65 years of age were isolated in the NSQIP database from 2018-2020. Demographic, pre-operative, and intraoperative variables were analyzed. After splitting the data into training (75%) and validation (25%) data sets, various machine learning models were used to predict non-home discharge. The model with the best area under the curve (AUC) was further assessed to identify the most important variables. Results In total, 19,840 geriatric patients undergoing THA were included in the final analyses, of which 5194 (26.2%) were discharged to a non-home setting. The RF model performed the best and identified age above 78 years (OR: 1.08 [1.07, 1.09], P < .0001), as the most important variable when predicting non-home discharge in geriatric patients with THA, followed by severe American Society of Anesthesiologists grade (OR: 1.94 [1.80, 2.10], P < .0001), operation time (OR: 1.01 [1.00, 1.02], P < .0001), anemia (OR: 2.20 [1.87, 2.58], P < .0001), and general anesthesia (OR: 1.64 [1.52, 1.79], P < .0001). Each of these variables was also significant in MLR analysis. The RF model displayed good discrimination with AUC = . 831. Discussion The RF model revealed clinically important variables for assessing discharge disposition in geriatric patients undergoing THA, with the five most important factors being older age, severe ASA grade, longer operation time, anemia, and general anesthesia. Conclusions With the rising emphasis on patient-centered care, incorporating models such as these may allow for preoperative risk factor mitigation and reductions in healthcare expenditure.
Exchange coupling and spin structure in cobalt-on-chromia thin films

Cobalt-chromia interface exchange interactions in the Co-on-Cr2O3(0001) system are investigated. Density-functional theory predicts the exchange coupling at the interface to be antiferromagnetic, in agreement with earlier experimental results. The spin-polarized photoemission spectra reveal both perpendicular and in-plane magnetization components, in the cobalt adlayer on chromia. A magnetization canted with respect to the surface normal, inferred from the presence of remnant spin polarization both in the plane of the cobalt film and along the surface normal may be understood as a micromagnetic canting effect involving magnetostatic self-interaction and exchange coupling between Co and Cr2O3.
A Novel Ensemble Fault Diagnosis Model for Main Circulation Pumps of Converter Valves in VSC-HVDC Transmission Systems

The intelligent fault diagnosis of main circulation pumps is crucial for ensuring their safe and stable operation. However, limited research has been conducted on this topic, and applying existing fault diagnosis methods designed for other equipment may not yield optimal results when directly used for main circulation pump fault diagnosis. To address this issue, we propose a novel ensemble fault diagnosis model for the main circulation pumps of converter valves in voltage source converter-based high voltage direct current transmission (VSG-HVDC) systems. The proposed model employs a set of base learners already able to achieve satisfying fault diagnosis performance and a weighting model based on deep reinforcement learning that synthesizes the outputs of these base learners and assigns different weights to obtain the final fault diagnosis results. The experimental results demonstrate that the proposed model outperforms alternative approaches, achieving an accuracy of 95.00% and an F1 score of 90.48%. Compared to the widely used long and short-term memory artificial neural network (LSTM), the proposed model exhibits improvements of 4.06% in accuracy and 7.85% in F1 score. Furthermore, it surpasses the latest existing ensemble model based on the improved sparrow algorithm, with enhancements of 1.56% in accuracy and 2.91% in F1 score. This work presents a data-driven tool with high accuracy for the fault diagnosis of main circulation pumps, which plays a critical role in maintaining the operational stability of VSG-HVDC systems and satisfying the unmanned requirements of offshore flexible platform cooling systems.