Miracle-dz/UniFAR-FTUs huggingface dataset:
Pinpointing the Mechanism of Magnetic Enhancement in Modern Soils Using High‐Resolution Magnetic Field Imaging

In well‐buffered modern soils, higher annual rainfall is associated with enhanced soil ferrimagnetic mineral content, especially of ultrafine particles that result in distinctive rock magnetic properties. Hence, paleosol magnetism has been widely used as a paleoprecipitation proxy. Identifying the dominant mechanism(s) of magnetic enhancement in a given sample is critical for reliable inference of paleoprecipitation. Here, we use high‐resolution magnetic field and electron microscopy to identify the grain‐scale setting and formation pathway of magnetic enhancement in two modern soils developed in higher (∼580 mm/y) and lower (∼190 mm/y) precipitation settings from the Qilianshan Range, China. We found that both soils contain 1–30 μm aeolian Fe‐oxide grains with indistinguishable rock magnetic properties, while the higher‐precipitation soil contains an additional population of ultrafine (<150 nm) magnetically distinct magnetite grains. We show that the in situ precipitation of these ultrafine particles, likely during wet‐dry cycling, is the only significant magnetic enhancement mechanism in this soil. These results demonstrate the potential of quantum diamond microscope magnetic microscopy to extract magnetic information from distinct, even intimately mixed, grain populations. This information can be used to evaluate the contribution of distinct enhancement mechanisms to the total magnetization.
Monaural Loudness Function at 1000 cps and Interaural Summation

A monaural loudness function is determined for 1000 cps and compared to a previously obtained binaural loudness function. Although the monaural function appears to have a somewhat smaller exponent than the binaural one, the empirical evidence does not permit rejection of the hypothesis of a simple interaural loudness summation. The psychophysical methods of magnitude estimation with and without a designated standard and of magnitude production without standards are used. A combination of magnitude estimation and production without standards is accepted as a method with minimum bias and classified under a more general methodological category called “psychological magnitude balance. ”
Epigenetics in LMNA-Related Cardiomyopathy

Mutations in the gene for lamin A/C (LMNA) cause a diverse range of diseases known as laminopathies. LMNA-related cardiomyopathy is a common inherited heart disease and is highly penetrant with a poor prognosis. In the past years, numerous investigations using mouse models, stem cell technologies, and patient samples have characterized the phenotypic diversity caused by specific LMNA variants and contributed to understanding the molecular mechanisms underlying the pathogenesis of heart disease. As a component of the nuclear envelope, LMNA regulates nuclear mechanostability and function, chromatin organization, and gene transcription. This review will focus on the different cardiomyopathies caused by LMNA mutations, address the role of LMNA in chromatin organization and gene regulation, and discuss how these processes go awry in heart disease.
Gas‐assisted fluid displacement in a circular tube and a rectangular channel

In this paper the amount of liquid left inside of a circular tube and a rectangular channel when displaced by another immiscible fluid are determined by solving the full creeping‐motion equations. The exact continuity of stress on the free surface is employed with a finite difference method. In order to solve the equations, the steady‐state shape of the interface is guessed and the normal stress boundary condition is dropped. The equations based on a stream function‐vorticity formulation are solved with the aid of elliptic grid generation. The computed results are compared with the experimental results of Taylor (J. Fluid Mech. 1961; 10:161), the theoretical results of Reinelt and Saffman (SIAM J. Sci. Stat. Comput. 1985; 6:542) and our experimental data. The computed results are in close agreement with our experimental data and those of previous investigators. Copyright © 2002 John Wiley & Sons, Ltd.
Simulation of single-phase mixing in fuel rod bundles, using an immersed boundary method

Over recent decades, the study of turbulent flow structures and the mixing behavior in fuel rod bundles has been an active research topic. In this paper, the (isothermal) turbulent mixing in rod bundles is simulated for two different cases using a finite-volume/immersed boundary method with a staggered velocity discretization. The solution procedure is based on an efficient multigrid algorithm, and simulations are performed on state-of-the-art massively parallel computer architectures. As the first case, the cross-flow through a staggered tube bundle under fully turbulent conditions is computed using large eddy simulation. The computed velocities were compared with the experimental data from the literature, and the agreement between the experimental and the computational results was found to be rather good. As a second case, we considered an experiment performed in the SUBFLOW test facility. The SUBFLOW facility models a 4 × 4 vertical rod bundle and uses water in atmospheric conditions as the working fluid. Experiments are based on the wire-mesh sensor technique, which measures the conductivity of the working fluid, and report the distribution of the tracer inserted at various heights of the bundle. This case is even more challenging for large eddy simulations, since it does not feature large structures present in the flow crossing the bundles. However, large eddy simulations of the SUBFLOW facility reproduced the concentration of the tracer liquid and its fluctuations well. The good agreement obtained for the two cases of flows in rod bundles serves as a motivation to focus future research on the modeling of bubbly flows in the same arrangements.
Incidence, Risk Factors, and Prognosis of Hidradenitis Suppurativa Across the Globe: Insights from the Literature

Abstract Hidradenitis suppurativa, a chronic inflammatory disease of the skin, affects a patient’s quality of life to a greater extent. While the disease burden, including its incidence and prevalence, has been extensively studied in the western population, there is a paucity of data from developing countries on the epidemiology of Hidradenitis suppurativa. Therefore, a general literature review was conducted to shed light on the epidemiology of Hidradenitis suppurativa across the globe. We reviewed the most recently available information on epidemiology, including incidence, prevalence, risk factors, prognosis and quality of life, complications, and associated comorbid among patients with Hidradenitis suppurativa. The estimated global prevalence of Hidradenitis suppurativa is found to be 0.00033–4.1%, with a relatively higher prevalence of 0.7–1.2% in European and US populations. Both genetic and environmental factors are associated with Hidradenitis suppurativa. Patients with Hidradenitis suppurativa have associated comorbid such as cardiovascular disease, type II diabetes mellitus, mental health issues, and sleep and sexual dysfunctions. These patients spend poor quality of life and tend to be less productive. Future studies are needed to assess the burden of Hidradenitis suppurativa in developing countries. Since the disease tends to be underdiagnosed, future studies should rely on clinical diagnosis rather than self-reporting to avoid the potential of recall bias. Attention needs to be diverted to developing countries with less amount of data on Hidradenitis suppurativa.
A comparison of switching energy of resonant and nonresonant electro-optic switches

Optical space switching is an important functionality in dense wavelength division multiplexing (DWDM) optical communication systems, particularly within reconfigurable optical add-drop multiplexers (ROADMs) [1]. Current commercially available ROADMs are based on micro-electromechanical systems (MEMS) or liquid crystal switches but these do not have sufficient switching speed for future network requirements. Power consumption (i.e. energy per switching operation multiplied by switching rate) is a very important parameter in the selection of a switching technology. Space switches based on current injection in silicon have been reported with nanosecond switching speeds and average power consumption on the order of mW [2], which becomes significant if many switches are required in a fabric. Electro-optic (EO) switches, which utilize the Pockels effect in which the refractive index changes when an external voltage is applied [3], only dissipate power when the switch state is changed. Electro-optic switches can be implemented either as non-resonant designs (for example the Mach-Zehnder interferometer (MZI)) or as resonant designs (for example the Fabry Perot interferometer (FPI)). In this study we compare the switching energies of electro optic MZI and FPI switches by considering the capacitance of the switch, which is determined by the length of the active region of the switch. We show that for a non-resonant switch, switching energy increases linearly with device length, regardless of applied voltage, and so is simply determined by the strength of the electro-optic coefficient. We assume that the resonant switch is implemented as a switchable comb filter [4], with a free-spectral range equal to twice the wavelength spacing. This then fixes the interferometer length. As a result the resonant switch has requires significantly less switching energy for the same material parameters and is thus of interest for future ROADM implementations.
A recursive integration method for approximate solution of stochastic differential equations

A deterministic approach for solving stochastic differential equations is described and numerically tested. In this approach, probability distributions of the sample paths at successive time steps in a numerical procedure are shown to satisfy a recursive integral equation. These probability distributions are approximated by solving the integral equation numerically. The advantage of this approach is the elimination of the need for computing sample paths of the solutions. This approach is useful, for example, in approximating the solution of first-passage time problems.
An unstructured grid Reynolds stress model for separated turbulent flow simulations

Most of the turbulence models in the literature contain simplified assumptions which make them computationally inexpensive but of limited accuracy for the solution of separated turbulent flows. Dramatic improvements in computer processing speed and parallel processing make it possible to use more complete models, such as Reynolds Stress Models, for separated turbulent flow simulations, which is the focus of this work. The Reynolds Stress Model consists of coupling the Reynolds transport equations with the Favre–Reynolds averaged Navier–Stokes equations, which results in a system of 12 coupled non-linear partial differential equations. The solutions are obtained by running the PUMA_RSM computational fluid dynamics code on unstructured meshes. The equations are solved all the way to the wall without using any wall functions. Results for high Reynolds number flow around a 6:1 prolate spheroid and a Bell 214ST fuselage are presented. For the prolate spheroid basic flow features such as cross-flow separation are simulated. Predictions of circumferential locations of cross flow separation points are in good agreement with the experiment. A grid refinement study is performed to improve the computations. The fine mesh solution predicted locations of primary and secondary separation points with errors of roughly 2° and 0°, respectively. Flow simulations around an isolated Bell 214ST helicopter fuselage were also performed. Predicted pressure and drag force correlate well with the wind tunnel data, with a less than 10% deviation from the experiment. Drag predictions also show relative speed of Reynolds Stress Model compared to Large Eddy Simulation to compute time averaged quantities. For numerical solutions parallel processing is applied with the MPI communication standard. The code used in this study is run on Beowulf clusters. The parallel performance of the code PUMA_RSM is analysed and presented.
Case report: Left gaze and facial nerve palsies after ventral intermediate thalamic nucleus deep brain stimulation implantation

Deep brain stimulation (DBS) to the ventral intermediate nucleus (VIM) of the thalamus has become a common procedure for some refractory, medication-resistant movement disorders like essential tremors. The most common adverse effects from this surgery include dysarthria and gait disturbances. This case report details a left gaze and ipsilateral facial nerve palsy following overshot cannula insertion into the pons during a VIM DBS procedure. Initial patient presentation after surgery revealed significant impairment of horizontal gaze to the left. This improved during follow-up visits and after the recession of the bilateral medial recti. When considering complications of the VIM DBS procedure, surgeons should be aware of the risks of cannula overshot given the anatomic proximity between the thalamus and brainstem. Furthermore, patients should be aware of this risk when making their surgical decision. All patients who undergo VIM DBS should be assessed for cranial nerve deficits after placement.
Improved bandwidth of microstrip antennas using parasitic elements

A method is described of doubling the bandwidth of rectangular microstrip patch antennas by locating capacitively excited λ m /4 short circuit parasitic elements at their radiating edges. The antenna characteristics are explained in terms of an antiphase mode of a pair of coupled resonators, and it is shown that the bandwidth improvement is independent of the coupling capacitance. Experimental results are presented for both linearly and circularly polarised antennas which support the theoretical predictions.
Multimodal Analysis of Physiological Signals for Wearable-Based Emotion Recognition Using Machine Learning

Recent advancements in wearable technology and machine learning have led to an increased research interest in the use of peripheral physiological signals to recognize emotion granularity. In healthcare, the ability to create an algorithm that classifies emotion content can aid in the development of treatment protocols for psychopathology and chronic disease. The non-invasive nature of peripheral physiological signals however is usually of low quality due to low sampling rates. As a result, single-mode physiological signal-based emotion recognition shows low performance. In this research, we explore the use of multi-modal wearable-based emotion recognition using the K-EmoCon dataset. Physiological signals in addition to self-reported arousal and valence records were analyzed with a battery of datamining algorithms including decision trees, support vector machines, k-nearest neighbors, and ensembles. Performance was evaluated using accuracy, true positive rate, and area under the receiver operating characteristic curve. Results support the assumption with 83% average accuracy when using an ensemble bagged tree algorithm compared to single heart rate-based emotion accuracy of 56.1%. Emotion granularity can be identified by wearables with multi-modal signal recording capabilities that improve diagnostics and possibly treatment efficacy.
DensePPMUNet-a: A Robust Deep Learning Network for Segmenting Water Bodies From Aerial Images

The identification of water bodies from aerial images using semantic segmentation networks can provide accurate information for ecological monitoring, flood prevention, and disaster reduction. Outliers on aerial images might reduce interclass separability and thus cause discontinuous prediction of water bodies. The fusion of global context information is helpful to solve this problem. However, the existing global prior representation does not provide sufficient information for identifying a large number of multiscale objects and outliers. In this study, a dense pyramid pooling module (DensePPM) was introduced to extract global prior knowledge with a dense scale distribution. The ablation experiments showed that the models using the DensePPM had higher values of IoU, $F1$ -score, and recall than that using pyramid pooling module (PPM), showing that the proposed module could capture more global context information of outliers under multiscale scenarios. A robust deep learning network named DensePPMUNet-a based on the DensePPM was then proposed for segmenting water bodies from aerial images. The comparative experiments with different datasets demonstrated that the DensePPMUNet-a outperformed other state-of-the-art deep learning models.
Ablative Techniques for Sarcoma Metastatic Disease: Current Role and Clinical Applications

Sarcomas are heterogenous mesenchymal neoplasms with more than 80 different histologic subtypes. Lung followed by liver and bone are the most common sites of sarcoma metastatic disease. Ablative techniques have been recently added as an additional alternative curative or palliative therapeutic tool in sarcoma metastatic disease. When compared to surgery, ablative techniques are less invasive therapies which can be performed even in non-surgical candidates and are related to decreased recovery time as well as preservation of the treated organ’s long-term function. Literature data upon ablative techniques for sarcoma metastatic disease are quite heterogeneous and variable regarding the size and the number of the treated lesions and the different histologic subtypes of the original soft tissue or bone sarcoma. The present study focuses upon the current role of minimal invasive thermal ablative techniques for the management of metastatic sarcoma disease. The purpose of this review is to present the current minimally invasive ablative techniques in the treatment of metastatic soft tissue and bone sarcoma, including local control and survival rates.
Nonlinear dynamics and breakup of free-surface flows

Surface-tension-driven flows and, in particular, their tendency to decay spontaneously into drops have long fascinated naturalists, the earliest systematic experiments dating back to the beginning of the 19th century. Linear stability theory governs the onset of breakup and was developed by Rayleigh, Plateau, and Maxwell. However, only recently has attention turned to the nonlinear behavior in the vicinity of the singular point where a drop separates. The increased attention is due to a number of recent and increasingly refined experiments, as well as to a host of technological applications, ranging from printing to mixing and fiber spinning. The description of drop separation becomes possible because jet motion turns out to be effectively governed by one-dimensional equations, which still contain most of the richness of the original dynamics. In addition, an attraction for physicists lies in the fact that the separation singularity is governed by universal scaling laws, which constitute an asymptotic solution of the Navier-Stokes equation before and after breakup. The Navier-Stokes equation is thus continued uniquely through the singularity. At high viscosities, a series of noise-driven instabilities has been observed, which are a nested superposition of singularities of the same universal form. At low viscosities, there is rich scalingmore » behavior in addition to aesthetically pleasing breakup patterns driven by capillary waves. The author reviews the theoretical development of this field alongside recent experimental work, and outlines unsolved problems. {copyright} {ital 1997} {ital The American Physical Society}« less
Pressure-induced metal-insulator transition in the filled skutterudite PrFe4P12

We have studied the electrical resistivity of the filled skutterudite compound $\mathrm{Pr}{\mathrm{Fe}}_{4}{\mathrm{P}}_{12}$ under high pressure. The antiferroquadrupolar ordering temperature ${T}_{Q}$ decreases monotonically with increasing pressure up to $2.4\phantom{\rule{0.3em}{0ex}}\mathrm{GPa}$. Above $2.4\phantom{\rule{0.3em}{0ex}}\mathrm{GPa}$, we have found that a metal-insulator $(M\text{\ensuremath{-}}I)$ transition appears. The insulating state is easily suppressed by a magnetic field. The observed Kondo effect and a field-induced heavy-fermion state at high pressure suggest that the quadrupolar interactions survive in the insulating region. The quadrupolar interactions might play an essential role in the $M\text{\ensuremath{-}}I$ transition. $\mathrm{Pr}{\mathrm{Fe}}_{4}{\mathrm{P}}_{12}$ is an unusual compound in that it shows a pressure-induced transition from a metal to an insulator.
Psychological issues in breast cancer survivors confronted with motherhood: Literature review and a call to action

Breast cancer is currently the most common cancer among women worldwide; in 15–25% of cases, patients are premenopausal at the time of diagnosis, and 50% of women desire pregnancy after cancer diagnosis. Motherhood after breast cancer involves complex psychological challenges with long-term consequences, though it is safely pursuable with adequate support. The purpose of this mini-review is to analyze the psychological implications surrounding pregnancy and motherhood after breast cancer and promote action in addressing the challenges that might affect women facing these life events.
Clinically Relevant Myocardium Segmentation in Cardiac Magnetic Resonance Images

Deep learning approaches have shown great success in myocardium region segmentation in Cardiac MR (CMR) images. However, most of these often ignore irregularities such as protrusions, breaks in contour, etc. As a result, the common practice by clinicians is to manually correct the obtained outputs for the evaluation of myocardium condition. This paper aims to make the deep learning systems capable of handling the aforementioned irregularities and satisfy desired clinical constraints, necessary for various downstream clinical analysis. We propose a refinement model which imposes structural constraints on the outputs of the existing deep learning-based myocardium segmentation methods. The complete system is a pipeline of deep neural networks where an initial network performs myocardium segmentation as accurate as possible and the refinement network removes defects from the initial output to make it suitable for clinical decision support systems. We experiment with datasets collected from four different sources and observe consistent final segmentation outputs with improvement up to 8% in Dice Coefficient and up to 18 pixels in Hausdorff Distance due to the proposed refinement model. The proposed refinement strategy leads to qualitative and quantitative improvements in the performances of all the considered segmentation networks. Our work is an important step towards the development of a fully automatic myocardium segmentation system. It can also be generalized for other tasks where the object of interest has regular structure and the defects can be modelled statistically.
SIMPLE MODEL OF MATING PREFERENCE AND EXTINCTION RISK

We present a simple model of a population of individuals characterized by their genetic structure in the form of a double string of bits and the phenotype following from it. The population is living in an unchanging habitat preferring a certain type of phenotype (optimum). Individuals are unisex, however a pair is necessary for breeding. An individual rejects a mate if the latter's phenotype contains too many bad, i.e. different from the optimum, genes in the same places as the individual's. We show that such strategy, analogous to disassortative mating based on the major histocompatibility complex, avoiding inbreeding and incest, could be beneficial for the population and could reduce considerably the extinction risk, especially in small populations.