Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical simulation of an idealised Richtmyer–Meshkov instability shock tube experiment

Abstract The effects of initial conditions on the evolution of the Richtmyer–Meshkov instability (RMI) at early to intermediate times are analysed, using numerical simulations of an idealised version of recent shock tube experiments performed at the University of Arizona (Sewell et al., J. Fluid Mech., vol. 917, 2021, A41). The experimental results are bracketed by performing both implicit large-eddy simulations of the high-Reynolds-number limit as well as direct numerical simulations (DNS) at Reynolds numbers lower than those observed in the experiments. Various measures of the mixing layer width $h$, known to scale as ${\sim }t^\theta$ at late time, based on both the plane-averaged turbulent kinetic energy and volume fraction profiles are used to explore the effects of initial conditions on $\theta$ and are compared with the experimental results. The decay rate $n$ of the total fluctuating kinetic energy is also used to estimate $\theta$ based on a relationship that assumes self-similar growth of the mixing layer. The estimates for $\theta$ range between 0.44 and 0.52 for each of the broadband perturbations considered and are in good agreement with the experimental results. Decomposing the mixing layer width into separate bubble and spike heights $h_b$ and $h_s$ shows that, while the bubbles and spikes initially grow at different rates, their growth rates $\theta _b$ and $\theta _s$ have equalised by the end of the simulations. Overall, the results demonstrate important differences between broadband and narrowband surface perturbations, as well as persistent effects of finite bandwidth on the growth rate of mixing layers evolving from broadband perturbations. Good agreement is obtained with the experiments for the different quantities considered; however, the results also show that care must be taken when using measurements based on the velocity field to infer properties of the concentration field.
Head injury: assessment and early management—summary of updated NICE guidance

• Consider an intravenous tranexamic acid bolus within 2 hours of injury in people with suspected moderate or severe traumatic brain injury, even when no extracranial bleeding is evident • Shared decision making can inform a decision not to conduct a computed tomography head scan in people taking anticoagulant or antiplatelet medication if there are no signs or symptoms of traumatic brain injury • Any severity of head injury can cause symptoms of pituitary dysfunction. Consider investigations or referral for hypopituitarism in those with persistent symptoms consistent with hypopituitarism • Consider referring people who have persisting symptoms following a head injury to appropriate clinicians or a multidisciplinary team
Self-assembly of nanoscale lateral segregation profiles

The surface segregation profile of an intermetallic compound becomes vertically and laterally modulated upon epitaxial growth of a single-layer hexagonal boron nitride $(h$-BN) nanomesh. $h$-BN on PtRh(111) forms an 11-on-10 superhoneycomb, such as that on Rh(111) [Corso et al., Science 303, 217 (2004)], though with a smaller lattice constant of 2.73 nm. X-ray photoelectron diffraction shows that the $h$-BN layer reduces the Pt enrichment of the first layer by promoting site swapping of about 10 Pt-Rh pairs within the $10\ifmmode\times\else\texttimes\fi{}10$ unit cell between the first and second layers. This segregation profile is confirmed by density-functional-theory-based cluster-expansion calculations. Generally, a strong modulation of the $h$-BN bonding strength and a higher affinity to one of the constituents leads to self-assembly of top layer patches underneath the nanomesh pores.
Research on Identification Method of Gap Nonlinear Vibration

The research of linear vibration has been very mature, but the gap nonlinear element widely exists in the actual structure and vibration system. The general linear vibration theory can not meet the needs of solving the nonlinear dynamic problems of clearance, and the research on the nonlinear vibration of clearance is essential. Based on the description function method, the forced response calculation of linear and nonlinear systems and the nonlinear detection method of clearance systems are studied in this paper. The forced response calculation of linear and clearance nonlinear systems mainly depends on the digital filtering method to realize the corresponding simulation of the system with clearance nonlinear. Based on the definition of linear system, a concrete scheme for judging whether the system contains the nonlinear characteristics of clearance is given. The position identification of the clearance nonlinear system based on the description function and the polynomial fitting inversion of the description function is studied and verified by an example.
An Analysis Software for Visual Position and Attitude Measurement Algorithm

Visual position and attitude measurement (VPAM) system has been widely used in obtaining space target information. In order to better obtain different target information and meet the requirements, it is particularly important to select a correct and effective measurement algorithm. In this paper, a performance evaluation software of VPAM algorithm is designed, which can compare and analyze the accuracy and complexity of algorithms used by different VPAM models, and help users select appropriate position models to obtain more accurate target information. Finally, the software is verified by using the dual photogrammetric model in the shipborne helicopter landing system, and the validity of the analysis software is verified by comparing the calculation results with the theoretical value of the algorithm accuracy analysis. The main contribution of this paper is that, as far as we know, it is the first time to try to evaluate the complexity and accuracy of the algorithm by building analysis software instead of theoretical analysis.
WIM-Drone: Combining a World-In-Miniature and Micro-Gestures for Teleoperation in Augmented Reality

Teleoperation in industrial contexts require operators to understand and visualize in real-time the remote workspace. It is nonetheless challenging to provide independent and mobile viewpoints to remote operators. This paper explores a new approach to tackle this challenge: Augmented Reality (AR) to control a drone used as aerial camera. More precisely, WIM-Drone combines a World-In-Miniature (WIM) in AR with (micro)gestures to control a drone. We present the conception and experimental evaluation of four interaction techniques based on this approach. Results from studies 1 and 2 suggest that WIM-Drone can be a promising approach for teleoperation scenario. Besides, Study 2 suggest that participants avoided collisions between the drone and humans or moving objects, but often ignored collisions with static objects.
Data-Aware Declarative Process Mining with SAT

Process Mining is a family of techniques for analyzing business process execution data recorded in event logs. Process models can be obtained as output of automated process discovery techniques or can be used as input of techniques for conformance checking or model enhancement. In Declarative Process Mining, process models are represented as sets of temporal constraints (instead of procedural descriptions where all control-flow details are explicitly modeled). An open research direction in Declarative Process Mining is whether multi-perspective specifications can be supported, i.e., specifications that not only describe the process behavior from the control-flow point of view, but also from other perspectives like data or time. In this article, we address this question by considering SAT (Propositional Satisfiability Problem) as a solving technology for a number of classical problems in Declarative Process Mining, namely, log generation, conformance checking, and temporal query checking. To do so, we first express each problem as a suitable FO (First-Order) theory whose bounded models represent solutions to the problem, and then find a bounded model of such theory by compilation into SAT.
FlowTexNet: Fast Texture Synthesis for Massive Flow Field Visualization

Flow field texture synthesis is a common and popular way to visualize flow fields. When massive flow fields are to be processed, existing algorithms based on line integral convolution (LIC) are not fast enough. In this paper, a new deep-learning-based method is proposed to synthesize flow textures for massive flow fields. Firstly, a deep neural network called FlowTexNet is built on the base of encoder-decoder architecture. Then the network is trained by flow textures generated by the original LIC algorithm. By this way, FlowTexNet can synthesize flow textures that have the same visualization effect as LIC textures. But FlowTexNet is much faster than the LIC algorithm. Test results show that the speedup of FlowTexNet is up to 450x when it is used to process massive flow fields and compared with the original LIC algorithm. Moreover, FlowTexNet can be applied to flow fields that are out of training, showing good generalization performance.
Do radioiodine-avid lymph nodes from differentiated thyroid cancer on the initial posttherapy scan need repeated 131I therapy?

Background Residual/recurrent lymph node metastase (LNM) is often found after differentiated thyroid cancer (DTC) surgery. This study aimed to investigate whether patients complicated with radioiodine-avid (131I+) lymph nodes from DTC on the initial posttherapy scan (PTS) need repeated 131I therapy. Methods From June 2013 to August 2022, DTC patients with 131I+ lymph nodes on the initial PTS who received at least two cycles of 131I therapy were retrospectively enrolled. They were divided into a complete response (CR) group and an incomplete response (IR) group according to their response to the initial 131I therapy based on the 2015 American Thyroid Association (ATA) guidelines. Results A total of 170 DTC patients with 131I+ lymph nodes on the initial PTS were included; 42/170 (24.7%) patients were classified into the CR group and 128/170 (75.9%) were classified into the IR group according to their response to the initial 131I therapy. None of the 42 CR patients had disease progression at the subsequent follow-up, and 37/170 (21.8%) IR patients improved after repeated therapy. Univariate analysis showed that N stage (P=0.002), stimulated thyroglobulin (sTg) level before initial 131I therapy (P<0.001), LNM size (P<0.001), number of total residual/recurrent LNM (P=0.021), radioiodine-nonavid (131I-) LNM (P=0.002) and ultrasound features (P<0.001) were related to the initial treatment response. On multivariate analysis, sTg level (OR=1.186, P<0.001) and LNM size (OR=1.533, P=0.004) were independent risk factors for IR after initial 131I therapy. The optimal sTg level and LNM size cutoff value for predicting the treatment response after initial 131I therapy were 18.2 µg/l and 5mm. Conclusion This study suggested that approximately one-quarter of patients with 131I+ lymph nodes on initial PTS, especially those with N0 or N1a stage, lower sTg level, smaller LNM size, ≤2 residual/recurrent LNMs, negative ultrasound features and no 131I- LNM, remain stable after one cycle of 131I therapy and do not need repeated therapy.
Editorial: Exploring GWAS data by biomolecular network analysis in revealing genetic disease mechanisms

Since the publication of the first draft of the human genome sequence in 2001 and especially within the past decade, genome-wide association studies (GWAS) have largely contributed in accelerating the identification of genetic variants across the genomes of millions of individuals, statistically associated with various specific traits and disease phenotypes (e.g.,: Uffelmann et al., 2021). For common diseases, e.g., type 2 diabetes mellitus (e.g., Mahajan et al., 2022), coronary artery disease (e.g., Musunuru and Kathiresan, 2019), schizophrenia (e.g., Kato et al., 2023), lung cancer (e.g., Long et al., 2022), thousands of susceptibility genetic loci have been determined, the analysis of which may reveal valid functional information about the disorders, accelerate drug development, and contribute to the design of predictive health diagnostics. However, the effect size of each individual variant associated with a multifactorial disease is relatively small, the overall disease risk corresponds to the cumulative effect size resulting from multiple genes and their interactions. The information content of GWAS data can be upgraded in the context of biomolecular interaction networks, selectively or in combination, e.g., protein interaction, metabolic, RNA and/or gene regulatory networks (e.g., Tomkins and Manzoni, 2021; Farrow et al., 2022). Such strategies have successfully been used for the evaluation of GWAS combined with relevant gene expression data (e.g.,: Wainberg et al., 2019) to a) prioritize groups of core susceptibility genes with respect to their position in the biomolecular network (Zhang et al., 2021), b) reveal disease modules, pathways, and disease subgroups (e.g., Wu et al., 2021), c) discover novel, and extent currently known disease-associated biological mechanisms and pathways (e.g., Wang et al., 2020), d) identify additional risk genes based on the network architecture of the disease (e.g., Meng et al., 2020), and e) suggest repurposing or development of new drugs to plausible OPEN ACCESS
Involvement, leadership and social practice to the development of postgraduate attributes: evidence from extracurricular education with Chinese characteristics

Limited by students’ time and energy, participation in extracurricular activities is not necessarily beneficial to the development of postgraduate attributes. Thus, it is necessary to explore the impact path of extracurricular activities and education outcomes on the development of postgraduate attributes. From a configuration perspective, this study identifies the asymmetric causal effects of engagement and extracurricular education on postgraduate attributes. First, this study proposes a theoretical framework for postgraduate attribute development in extracurricular education with Chinese characteristics based on the input-environment-output (IEO) theory. Second, 166 academic scholarship applications submitted by the whole third-grade postgraduates who are from a science and engineering school at a double first-class university in China are taken as the sample. Finally, utilizing data envelopment analysis (DEA) and fuzzy set qualitative comparative analysis (fsQCA), this study conducts the effect of the combination of causal conditions on the development of postgraduate attributes. Results are as follows: (1) the development efficiency of postgraduate attribute in extracurricular education with Chinese characteristics is practical but still insufficient; (2) four configurations consistently linked to high development efficiency of postgraduate attributes. Specifically, in context with outstanding academic research achievement and excellent moral character, participating in extracurricular education or not consistently linked to high development efficiency. In contrast, in a context characterized by academic achievement or moral award not outstanding enough, involvement in extracurricular activities or social practice is consistently linked to high development efficiency. In addition, no configuration links student leadership to high development efficiency, and non-scientific research ability is consistently linked to low development efficiency; (3) there is an asymmetric causal relationship between the high and low development efficiency paths, indicating that the conditions affecting the development of postgraduate attributes have multiple concurrencies. These findings provide a new practical path and perspective for promoting the development of postgraduate attributes through extracurricular education with Chinese characteristics.
Research on the influence and mechanism of human–vehicle moral matching on trust in autonomous vehicles

Introduction Autonomous vehicles can have social attributes and make ethical decisions during driving. In this study, we investigated the impact of human-vehicle moral matching on trust in autonomous vehicles and its mechanism. Methods A 2*2 experiment involving 200 participants was conducted. Results The results of the data analysis show that utilitarian moral individuals have greater trust than deontological moral individuals. Perceived value and perceived risk play a double-edged role in people’s trust in autonomous vehicles. People’s moral type has a positive impact on trust through perceived value and a negative impact through perceived risk. Vehicle moral type moderates the impact of human moral type on trust through perceived value and perceived risk. Discussion The conclusion shows that heterogeneous moral matching (people are utilitarian, vehicles are deontology) has a more positive effect on trust than homogenous moral matching (both people and vehicles are deontology or utilitarian), which is consistent with the assumption of selfish preferences of individuals. The results of this study provide theoretical expansion for the fields related to human-vehicle interaction and AI social attributes and provide exploratory suggestions for the functional design of autonomous vehicles.
Cation permeability and pore dynamics in TRPV1 ion channels

The Transient Receptor Vanilloid 1 (TRPV1) is a non-selective ion channel, which is activated by several chemical ligands and heat. We have previously shown that activation of TRPV1 by different ligands result in single-channel openings with different conductance, suggesting that the selectivity filter is highly dynamic. TRPV1 is weakly voltage-dependent, here we sought to explore whether the permeation of different monovalent ions could influence the voltage-dependence of this ion channel. By using single-channel recordings, we show that TRPV1 channels undergo rapid transitions to closed states that are directly connected to the open state, which may result from structural fluctuations of their selectivity filters. Moreover, we demonstrate that the rates of these transitions are strongly influenced by the permeant ion, suggesting that ion permeation regulates the voltage dependence of these channels.
In vivo long-term voltage imaging by genetically encoded voltage indicator reveals spatiotemporal dynamics of neuronal populations during development

A central question in brain development lies in how individual neurons emerge and organize communities to acquire various functions. Voltage imaging provides unique approaches to addressing this by enabling simultaneous, non-invasive, in vivo recording of voltage dynamics from a population of cells. Recently, genetically encoded voltage indicators (GEVIs) facilitate cell-type specific imaging of voltage dynamics. However, it has not been applied to brain development. Here, we applied ArcLight, a GEVI utilizing voltage-sensitive domain, to zebrafish and established experimental approaches for analyzing voltage and morphology of neuron populations during development, focusing on the spinal cord and cerebellum. We initially demonstrated that Arclight was widely distributed in the neural tissues. With voltage imaging, we successfully visualized the coordinated, spontaneous activity of spinal cord neurons in their early stage of development at a high spatiotemporal resolution, at subcellular and population levels. Hyperpolarization and subthreshold signals were also detected. Finally, long-term voltage imaging during development revealed the process of changes in voltage dynamics in neuron populations, accompanied by axonal outgrowth. Voltage imaging could greatly contribute to our understanding of the functional organization of the nervous system during development.
Distinct excitability of thalamocortical neurons correlates with the presence of cerebellar afferents

Thalamocortical (TC) neurons within the ventrolateral thalamus (VL) receive projections from the cerebellum and the basal ganglia (BG) to facilitate motor and non-motor functions. Tonic and rebound firing patterns in response to excitatory cerebellar and inhibitory BG inputs, respectively, are a canonical feature of TC neurons and plays a key role in signal processing. The intrinsic excitability of TC neurons has a strong influence on how they respond to synaptic inputs, however, it is unknown whether their afferents influence their firing properties. Understanding the input-specific firing patterns could shed light into movement disorders with cerebellar or BG involvement. Here, we used whole-cell electrophysiology in brain slices from C57BL/6 mice to investigate the firing of TC neurons with optogenetic confirmation of cerebellar or BG afferents. TC neurons with cerebellar afferents exhibited higher tonic and rebound firing rates than those with BG afferents. This increased firing was associated with faster action potential depolarization kinetics and a smaller afterhyperpolarization potential. We also found differences in the passive membrane properties and sag currents during hyperpolarization. Despite higher rebound firing in TC neurons with cerebellar afferents, there were no differences in T-type calcium channel function compared to those with BG inputs. These data suggest input-specific differences in sodium and SK, but not T-type calcium channels, impact firing properties in TC populations. Altogether, we showed that the pronounced divergence observed in TC neuron firing properties correlate with its heterogeneous anatomical connectivity, which could signify a distinct signal integration and processing by these neurons. Keypoints Thalamocortical neurons in the VL with cerebellar afferents have higher intrinsic tonic and rebound firing properties than those with basal ganglia afferents. Membrane resistance and action potential depolarization slope were different based on the presence of cerebellar afferents. Despite elevated rebound burst firing, T-type mediated currents did not correlate with increased firing in neurons with cerebellar afferents.
VARIATIONAL MODELS FOR SIMULTANEOUS IMAGE SEGMENTATION AND NOISE REMOVAL

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy VARIATIONAL MODELS FOR SIMULTANEOUS IMAGE SEGMENTATION AND NOISE REMOVAL By Iulia Magdalena Posirca December 2012 Chair: Yunmei Chen Major: Mathematics We present two projects for simultaneous image segmentation and noise removal. The first project concerns the images corrupted with Gaussian noise and the second one was developed for images contaminated with multiplicative noise. For both models we use soft segmentation, which allows each pixel to belong to each image pattern with some probability. Our work proposes also a functional with variable exponent, which provides a better noise removal with feature preserving. The diffusion resulting from the proposed models is a combination between the total variation (TV)-based and isotropic smoothing. To minimize the functional energy, we use the Euler-Lagrange equations on the (K-1)-simplex and the alternating minimization (AM) algorithm. The experimental and comparison results with some traditional models show the efficiency of our work, with improved denoising and segmentation of real and synthetic images.