Miracle-dz/UniFAR-FTUs huggingface dataset:
Calibration free method for measurement of the AC magnetization loss

A calibration free measurement method for determination of the magnetization loss of superconducting samples exposed to the external AC magnetic field is presented. The idea is based on the measurement of the part of the power which is supplied by the AC source to the AC magnet generating the magnetic field, in which the sample is located. It uses a coil wound in parallel to the AC field magnet as the measurement coil. To achieve the necessary sensitivity, two identical systems are used, each consisting of an AC magnet and a measurement coil, one of them containing the sample and the other left empty. No measurement and/or calculation of the calibration constant is required. To confirm the suitability of this method, the loss of a Cu sample with known dissipation was measured. The applicability to the AC magnetization loss measurements of superconducting tapes is presented.
Gossip Algorithms for Average Energy Estimation in Wireless Sensor Networks

Gossip algorithms which belong to a kind of distributed algorithms can be used to compute the (possibly weighted) average of the initial measurements of the nodes at every node in the network. This paper proposes an improved broadcast gossip algorithm to estimate average energy with better accuracy. In this proposed algorithm, we use some companion variables to save the sum, and convergence error is decreasing with the number of companion variables increasing. Convergence of the proposed algorithm is studied theoretically and verified by simulations. Although the estimated value is random, we show that the novel algorithm can converge closer to average than broadcast gossip algorithm in probability.
Neutrino propagation in the nuclear 'pasta phase' of neutron stars

The mean free path of the neutrino in charged and neutral current reactions is calculated for inhomogeneous nuclear matter which is expected to appear in the inner crust of neutron stars. The relevant cross section depends on Fermi and Gamow–Teller strength distributions, which are derived from the self-consistent Skyrme–Hartree–Fock and a relativistic mean-field calculations. The inhomogeneous nuclear matter, which appears at the boundary between the inner crust and outer core, is described in terms of cubic Wigner–Seitz cells. It allows for a microscopic description of the structures in the so-called pasta phase of nuclear configurations and provides a smooth transition to the limit of homogeneous matter. The influence of the pasta phase, its microscopical structure and geometrical shapes on neutrino propagation are considered.
Adh Promotes Actinobacillus pleuropneumoniae Survival in Porcine Alveolar Macrophages by Inhibiting CHAC2-Mediated Respiratory Burst and Inflammatory Cytokine Expression

Actinobacillus pleuropneumoniae (A. pleuropneumoniae) causes porcine pleuropneumonia that seriously endangers pig’s health. Adh, located in the head region of trimeric autotransporter adhesion of A. pleuropneumoniae, affects bacterial adhesion and pathogenicity. However, how Adh mediates A. pleuropneumoniae immune invasion is still unclear. Here, we established the A. pleuropneumoniae strain L20 or L20 ΔAdh-infected porcine alveolar macrophages (PAM) model, and applied protein overexpression, RNA interference, qRT-PCR, Western blot and immunoflourescence techniques to dissect the effects of Adh on PAM during A. pleuropneumoniae infection. We found that Adh could increase the A. pleuropneumoniae adhesion and intracellular survival in PAM. Gene chip analysis of piglet lungs further showed that Adh significantly induced cation transport regulatory-like protein 2 (CHAC2) expression, whose overexpression suppressed the phagocytic capacity of PAM. Furthermore, CHAC2 overexpression dramatically increased glutathione (GSH) expression, decreased reactive oxygen species (ROS), and promoted A. pleuropneumoniae survival in PAM, while the knockdown of CHAC2 reversed these phenomena. Meanwhile, CHAC2 silence activated the NOD1/NF-κB pathway, resulting in an increase in IL-1β, IL-6, and TNF-α expression, whereas this effect was weakened by CHAC2 overexpression and addition of NOD1/NF-κB inhibitor ML130. Moreover, Adh enhanced the secretion of LPS of A. pleuropneumoniae, which regulated the expression of CHAC2 via TLR4. In conclusion, through a LPS-TLR4-CHAC2 pathway, Adh inhibits respiratory burst and inflammatory cytokines expression to promote A. pleuropneumoniae survival in PAM. This finding may provide a novel target for the prevention and treatment of A. pleuropneumoniae.
Polarization modulation in Young's interference experiment

Polarization properties at the observation screen in Young's interference experiment are examined. Several recent results on the modulation of Stokes parameters, including the minimum number of modulated parameters, are reviewed. The theory is then applied to find out the relation between the Stokes parameters at the pinholes and the Pancharatnam-Berry phase at the screen.
Polarimetry of the binary PDS 144

Aims. Our goal is to study the circumstellar environment associated with each component of the wide intermediate-mass pre-main sequence binary system PDS 144 using broadband polarimetry. Methods. We present near-infrared (NIR) linear polarimetric observations of PDS 144 gathered with the IAGPOL imaging polarimeter along with the CamIV infrared camera at the Observatorio do Pico dos Dias (OPD). In addition, we re-analyzed OPD archive optical polarization to separate the binary and estimate the interstellar polarization using foreground stars. Results. After discounting the interstellar component, we found that both stars of the binary system are intrinsically polarized. The polarization vectors at optical and NIR bands of both components are aligned with the local magnetic field and the jet axis. These findings indicate an interplay between the interstellar magnetic field and the formation of the binary system. We also found that the PDS 144N is less polarized than its southern companion in the optical. However, in the NIR PDS 144N is more polarized. Our polarization data can only be explained by high inclinations (i 80 ◦ ) for the disks of both members. In particular, comparisons of our NIR data with young stellar objects disk models suggest predominantly small grains in the circumstellar environment of PDS 144N. In spite of the different grain types in each component, the infrared spectral indexes indicate a coeval system. We also found evidence of coplanarity between the disks.
On Gibbs sampling for state space models

SUMMARY We show how to use the Gibbs sampler to carry out Bayesian inference on a linear state space model with errors that are a mixture of normals and coefficients that can switch over time. Our approach simultaneously generates the whole of the state vector given the mixture and coefficient indicator variables and simultaneously generates all the indicator variables conditional on the state vectors. The states are generated efficiently using the Kalman filter. We illustrate our approach by several examples and empirically compare its performance to another Gibbs sampler where the states are generated one at a time. The empirical results suggest that our approach is both practical to implement and dominates the Gibbs sampler that generates the states one at a time.
Refined filtering of interferometric phase from INSAR data

Radar interferometry has been widely applied in measuring the surface height. The information about surface can be derived from phase interferograms. However, phase noise reduces the accuracy and reliability of that information. Hence, the minimization of phase noise is essential to the retrieval of surface information. This work presents a refined filter that is based on the Lee adaptive INSAR filter and the sigma filter. The basic idea is to filter adaptively the interferometric phase according to the local noise level to minimize the loss of signal for a particular shape of fringes, including in such extreme cases as involve broken fringes, following the elimination of unreliable pixels of phase noise. The goal is to reduce the phase deviation and the number of residues, and minimize the phase error. The proposed filter was inspected herein using both simulated data and real interferometer data. Results reveal that the filtering performance is better than that of commonly used filters.
Multi-View Scholar Clustering With Dynamic Interest Tracking

Scholar clustering has garnered increasing attention due to the explosive growth of scholar data. Although researchers have proposed many algorithms to cluster scholars, they typically focus on clustering scholars from the intrinsic view (scholars’ contents). These algorithms may lead to inaccurate and biased clustering results because they ignore the extrinsic view (scholar's specialty) and the changeability of scholars’ interest in each view. In this paper, we propose a multi-view scholar clustering topic model (MSCT), which integrates complementary information from both intrinsic and extrinsic views while considering dynamic scholar interests. Specifically, MSCT involves two novel schemes. The first one is multi-view integration, where MSCT collaboratively tracks scholars’ time-varying topic distribution from two views: intrinsic view and extrinsic view. The former exploits the details of different academic degrees in the title and information in the abstract; the latter leverages the specialty of different categories in the corresponding research field and research discipline. The second one is dynamic interest tracking, which dynamically models each scholar's interest distribution in terms of the current scholar texts and previously estimated distribution through a newly designed collapsed Gibbs sampling algorithm. Experimental results demonstrate that MSCT can significantly outperform state-of-the-art algorithms.
Evaluation of a compartmental model for estimating tumor hypoxia via FMISO dynamic PET imaging

This paper systematically evaluates a pharmacokinetic compartmental model for identifying tumor hypoxia using dynamic positron emission tomography (PET) imaging with 18F-fluoromisonidazole (FMISO). A generic irreversible one-plasma two-tissue compartmental model was used. A dynamic PET image dataset was simulated with three tumor regions—normoxic, hypoxic and necrotic—embedded in a normal-tissue background, and with an image-based arterial input function. Each voxelized tissue's time activity curve (TAC) was simulated with typical values of kinetic parameters, as deduced from FMISO-PET data from nine head-and-neck cancer patients. The dynamic dataset was first produced without any statistical noise to ensure that correct kinetic parameters were reproducible. Next, to investigate the stability of kinetic parameter estimation in the presence of noise, 1000 noisy samples of the dynamic dataset were generated, from which 1000 noisy estimates of kinetic parameters were calculated and used to estimate the sample mean and covariance matrix. It is found that a more peaked input function gave less variation in various kinetic parameters, and the variation of kinetic parameters could also be reduced by two region-of-interest averaging techniques. To further investigate how bias in the arterial input function affected the kinetic parameter estimation, a shift error was introduced in the peak amplitude and peak location of the input TAC, and the bias of various kinetic parameters calculated. In summary, mathematical phantom studies have been used to determine the statistical accuracy and precision of model-based kinetic analysis, which helps to validate this analysis and provides guidance in planning clinical dynamic FMISO-PET studies.
Differentiable Learning of Scalable Multi-Agent Navigation Policies

We present an end-to-end differentiable learning algorithm for multi-agent navigation policies. Compared with prior model-free learning algorithms, our method leads to a significant speedup via the gradient information. Our key innovation lies in a novel differentiability analysis of the optimization-based crowd simulation algorithm via the implicit function theorem. Inspired by continuum multi-agent modeling techniques, we further propose a kernel-based policy parameterization, allowing our learned policy to scale up to an arbitrary number of agents without re-training. We evaluate our algorithm on two tasks in obstacle-rich environments, partially labeled navigation and evacuation, for which loss functions can be defined making the entire task learnable in an end-to-end manner. The results show that our method can achieve more than one order of magnitude speedup over model-free baselines and readily scale to unseen target configurations and agent sizes.
Dopant extraction from scanning capacitance microscopy measurements of p-n junctions using combined inverse modeling and forward simulation

This article proposes a more accurate approach to dopant extraction using combined inverse modeling and forward simulation of scanning capacitance microscopy (SCM) measurements on p-n junctions. The approach takes into account the essential physics of minority carrier response to the SCM probe tip in the presence of lateral electric fields due to a p-n junction. The effects of oxide fixed charge and interface state densities in the grown oxide layer on the p-n junction samples were considered in the proposed method. The extracted metallurgical and electrical junctions were compared to the apparent electrical junction obtained from SCM measurements.
Oxidative Stress and Cardiovascular Risk Factors: The Coronary Artery Risk Development in Young Adults (CARDIA) Study

Introduction—Oxidative stress is linked to cardiovascular diseases (CVD) and is suggested to vary by sex. However, few population-level studies have explored these associations and the majority comprise populations with advanced CVD. We assessed urinary isoprostane concentrations, a standard measure of oxidative stress, in a relatively young and healthy cohort, hypothesizing that higher oxidative stress is associated with an adverse cardiometabolic profile and female sex. Methods—Oxidative stress was measured in 475 women and 266 men, aged 48–55 years, from the Coronary Artery Risk Development in Young Adults (CARDIA) study using urinary 8-isoprostane (IsoP) and 2,3-dinor-8-isoprostane (IsoP-M). Multivariable-adjusted regression was used to evaluate cross-sectional associations. As secondary analysis, previously measured plasma F2-isoprostanes (plasma IsoP) from another CARDIA subset was similarly analyzed. Results—Mean (SD) ages for men and women were 52.1(2.3) and 52.2(2.2) years, respectively (p = 0.46), and 39% of the participants self-identified as Black (vs. White). Before adjustments, female sex was associated with higher median urinary IsoP (880 vs. 704 ng/g creatinine in men; p < 0.01) and IsoP m (1675 vs. 1284 ng/g creatinine in men; p < 0.01). Higher body mass index (BMI), high-density cholesterol (HDL-C), and triglycerides, current smoking, and less physical activity were associated with higher oxidative stress. Diabetes was not associated with urinary IsoP but was associated with lower IsoP m and plasma IsoP. Higher serum creatinine showed diverging associations with higher plasma and lower urinary isoprostane concentrations. Conclusions—Different isoprostane entities exhibit varying association patterns with CVD risk factors, and therefore are complementary, rather than interchangeable, in assessment of oxidative stress. Still, consistently higher isoprostanes among women, smokers, less active persons, and those with higher BMI and plasma triglycerides could reflect higher oxidative stress among these groups. While urinary isoprostanes are indexed to urinary creatinine due to variations in concentration, caution should be exercised when comparing groups with differing serum creatinine.
Slovenian Version of the Deese–Roediger–McDermott Paradigm: A Normative and Exploratory Study

The research with the Deese–Roediger–McDermott (DRM) paradigm has accumulated substantial evidence for the reliable cross-cultural false memory effect. However, only a limited set of languages has had the benefit of receiving their own version of the DRM paradigm, let alone the corresponding normative data. In this study, we used 728 participants (594 women) to create the first Slovenian version of the DRM paradigm, and another 90 participants (66 women) to test its effectiveness in inducing false memories and conduct exploratory analyses. We are the first one to conduct a normative DRM study entirely online and among the few that measured reaction times (RTs) besides commonly employed accuracy measures. Overall, the participants recalled 69% of the list items and 14% of the critical lures, and additionally recognised 79% of the list items and 45% of the critical lures. The participants recalled and recognised list items as fast as critical lures and significantly faster than unrelated words. The study shows that false memories can be effectively explored by using the online format of the DRM paradigm. Future studies should investigate the relationship between RTs and accuracy measures, the relationship between recall and recognition, as well as the effects of the experimental environment on the results of the DRM studies in more detail. We hope that the provided material and its scrutiny will contribute to the progress in false memory research, especially in the Slovenian language environment.
4-Methylesculetin ameliorates LPS-induced depression-like behavior through the inhibition of NLRP3 inflammasome

The pathophysiology of depression is heavily dependent on inflammation. Evidence suggests that the etiology of depression is linked with NLRP3 inflammasome-induced inflammation. Therefore, blocking the activated NLRP3 inflammasome may be beneficial for treating depression. Due to the limitations of currently available antidepressants, it is necessary to develop novel, safe, and affordable drugs for the treatment of depression. A natural coumarin derivative named 4-methylesculetin (4-MESC) possesses anti-inflammatory properties. However, the role of 4-MESC as an antidepressant has not been elucidated. Therefore, in this study, we explored the antidepressant-like effects of 4-MESC and its underlying molecular mechanism through the modulation of the NLRP3 inflammasome. The docking and molecular dynamic simulation studies revealed that 4-MESC has a higher affinity for the NLRP3 PYD. Blood–brain barrier permeability was confirmed using the SwissADME pharmacokinetic tool. High doses (50 mg/kg) of 4-MESC significantly reduced the immobility duration in the tail-suspension test (TST) and forced swim test (FST) without changing the overall locomotor activity in the female Swiss albino mice that were subjected to lipopolysaccharide (LPS). LPS-induced pro-inflammatory cytokines such as IL-6 and TNF-α were reduced in serum and brain tissues using 4-MESC. 4-MESC’s neuroprotective effects are mediated by increased brain-derived neurotrophic factor (BDNF) and decreased cortisol levels. 4-MESC markedly reduced LPS-induced elevated levels of ROS and lipid peroxidation (malondialdehyde levels) and enhanced the superoxide dismutase (SOD) activity and glutathione levels, which revealed its anti-oxidant potential against oxidative stress. 4-MESC diminished the expression levels of NF-κBp65, IL-6, NLRP3, caspase-1, gasdermin D, and IL-1β in the hippocampus. These findings demonstrated that 4-MESC exhibited antidepressant-like effects by inhibiting the NLRP3 inflammasome. However, other antidepressant mechanisms might also be involved which require further studies.
Severity and Longitudinal Course of Depression, Anxiety and Post-Traumatic Stress in Paediatric and Young Adult Cancer Patients: A Systematic Review and Meta-Analysis

Background: A diagnosis of cancer and treatment may constitute a highly traumatic period for paediatric cancer patients (PYACPs). However, no review has comprehensively analysed how the mental health of PYACPs is acutely affected and the longitudinal course. Methods: This systematic review followed PRISMA guidelines. Comprehensive searches of databases were conducted to identify studies of depression, anxiety and post-traumatic stress symptoms in PYACPs. Random effects meta-analyses were used for the primary analysis. Results: From 4898 records, 13 studies were included. Acutely after diagnosis, depressive and anxiety symptoms were significantly elevated in PYACPs. Depressive symptoms only significantly decreased after 12 months (standardised mean difference, SMD = −0.88; 95% CI: −0.92, −0.84). This downward trajectory persisted to 18 months (SMD = −1.862; 95% CI: −1.29, −1.09). Anxiety symptoms similarly only decreased after 12 (SMD = −0.34; 95% CI: −0.42, −0.27) up to 18 months (SMD = −0.49; 95% CI: −0.60, −0.39) after the cancer diagnosis. Post-traumatic stress symptoms showed protracted elevations throughout follow-up. Overall, significant predictors of poorer psychological outcomes included unhealthy family functioning, concomitant depression or anxiety, poor cancer prognosis or experiencing cancer and treatment-related side effects. Conclusions: While depression and anxiety may improve over time with a favourable environment, post-traumatic stress may have a protracted course. Timely identification and psycho-oncological intervention are critical.
Power Estimation for Two‐Sample Tests Using Importance and Antithetic Resampling

COLLINGS and HAMILTON (1988), described a uniform bootstrap method that is applied on observed or pilot data in order to approximate the power of the two-sample Wilcoxon test for location shift alternatives. In this paper we demonstrate how importance and antithetic resampling can be used to substantially reduce the amount of computation needed to approximate the power of the two-sample tests for location shift and scale alternatives. Importance and antithetic bootstrap resampling methods are applied to simulated data of different sample sizes from a variety of distributions as well as to data from the Iowa 65+ Rural Health Study. Also, a suggestion is given for using a combination of importance and antithetic resampling for approximating the power of two-sample tests.
Segmentation of urban high resolution remote sensing images based on mean shift with texture feature extraction by Gabor filtering

Owing to the extremely rich and sensitive information they can provide, urban high-resolution (HR) remote sensing images have a wide range of applications in smart city planning. However, conventional spectral information-based image processing methods are no longer appropriate for producing such images. Remote sensing image segmentation processes commonly apply the mean shift method, which generally uses spatial and spectral features, to maintain edge information and reduce the impact of noise. This paper proposes a novel mean shift segmentation method based on Gabor texture analysis, in which the number of dimensions in the feature space is increased by applying textural elements in addition to spatial and spectral elements to fully utilize detailed information in urban high-resolution remote images. To obtain texture information, multi-scale, multi-orientation Gabor filters are used to extract pixel-to-pixel features from an image, with local variance (LV) used to estimate the optimal texture bandwidth. QuickBird and Mass. Buildings images are used to experimentally validate the proposed method based on analyses using the global segmentation quality index (GS) and error rate (ER) as indicators. The results obtained for two different remote sensing datasets show that the proposed method is superior and more precise than the conventional mean shift method.