Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability and onset of two-dimensional viscous fingering in immiscible fluids

Viscous flows in a quasi-two-dimensional Hele-Shaw geometry can lead to an interfacial instability when one fluid, of viscosity $\eta_{in}$ displaces another of higher viscosity, $\eta_{out}$. Recent studies have shown that there is a delay in the onset of fingering in miscible fluids as the viscosity ratio, $\eta_{in}/\eta_{out}$, increases and approaches unity; the interface can remain stable even though the displacing liquid is less viscous. This paper shows that a delayed onset and stable pattern can occur in immiscible fluids as well. However, there are two significant differences between the two cases. First, in miscible fluids, stable patterns are obtained whenever $\eta_{in}/\eta_{out} > 0.33$ while in immiscible fluids, the radius at which the onset of fingering starts, $R_{onset}$, increases steadily until $\eta_{in}/\eta_{out}=1$. A stable pattern is obtained only when the total size of the plate used is smaller than $R_{onset}$. Second, once the delayed fingering starts in immiscible fluids, the fingers grow faster than the central circular region. In miscible fluids, there is a regime in which the fingers and the central region grow in proportion to each other. These differences between miscible and immiscible fingering are maintained even when we compare immiscible fluids that have very low interfacial tension with miscible fluids that have negligible diffusion.
Convolutional Neural Network Model Based on 2D Fingerprint for Bioactivity Prediction

Determining and modeling the possible behaviour and actions of molecules requires investigating the basic structural features and physicochemical properties that determine their behaviour during chemical, physical, biological, and environmental processes. Computational approaches such as machine learning methods are alternatives to predicting the physiochemical properties of molecules based on their structures. However, the limited accuracy and high error rates of such predictions restrict their use. In this paper, a novel technique based on a deep learning convolutional neural network (CNN) for the prediction of chemical compounds’ bioactivity is proposed and developed. The molecules are represented in the new matrix format Mol2mat, a molecular matrix representation adapted from the well-known 2D-fingerprint descriptors. To evaluate the performance of the proposed methods, a series of experiments were conducted using two standard datasets, namely the MDL Drug Data Report (MDDR) and Sutherland, datasets comprising 10 homogeneous and 14 heterogeneous activity classes. After analysing the eight fingerprints, all the probable combinations were investigated using the five best descriptors. The results showed that a combination of three fingerprints, ECFP4, EPFP4, and ECFC4, along with a CNN activity prediction process, achieved the highest performance of 98% AUC when compared to the state-of-the-art ML algorithms NaiveB, LSVM, and RBFN.
Sexual Harassment and Mental Health of Female Healthcare Workers in a Tertiary Care Hospital in Riyadh, Saudi Arabia

Background In this study, we aimed to estimate the prevalence and identify the correlates of sexual harassment among female healthcare workers in a tertiary care hospital in Riyadh, Saudi Arabia. Methodology A cross-sectional study was conducted among 432 female healthcare workers in both Arabic and English languages. The questionnaire consisted of five sections which included the sociodemographic characteristics, a sexual harassment survey, the Kessler distress scale, social support, and the Rosenberg self-esteem scale. Unadjusted odds ratios with 95% confidence intervals were calculated to identify the significant factors associated with harassment. Results Sexual harassment was reported by 15.5% of the participants, with verbal harassment being the most commonly reported (66%), followed by physical harassment (34%). Around three-fourths of harassment acts took place during the daytime, and 18% of the participants preferred to keep quiet about it. Moderate distress [2.38 (1.17, 4.84)] and severe distress [2.31 (1.09, 4.90)], feeling hopeless [2.86 (1.47, 5.57)] and feeling depressed [3.70 (1.62, 8.48)] were significantly associated with sexual harassment. Low self-esteem items, such as “I don’t have good qualities” [4.78 (2.0, 11.43)], “don’t have much to be proud of” [2.10 (1.22, 3.63)], “wish that I have more respect for myself” [2.30 (1.36, 3.90)], and “inclined to feel that I am a failure” [2.27 (1.24, 4.16)] were significantly associated with sexual harassment. Conclusions It is important for all employees to know about their rights and report all types of harassment acts. Counseling sexually harassed victims should focus on improving the self-esteem and mental distress of these women. Prevention of harassment against female healthcare workers can improve their mental health as well as their quality of work.
Restoration of Topological Surface State by Vacuum Annealing in Magnetically Doped Topological Insulator

The introduction of magnetic order on the surface of topological insulators in general breaks the two-dimensional character of topological surface state (TSS). Once the TSS disappears, it is improbable to restore the topological surface properties. In this report, we demonstrate that it is possible to restore the inherent TSS by ultra-high vacuum annealing. Starting from an antiferromagnetic Gd-doped Bi2Te3, that has surface state gap without TSS properties, after annealing we observed the gap closing as well as typical TSS features in physical properties. The microscopic mechanism of atomic migration and TSS restoration by annealing process is unraveled by the combination of scanning tunneling microscopy measurements and density functional theory calculations. This approach to control the surface of topological insulators and stabilize the TSS simply by vacuum annealing provides a new platform towards the exploitation of their topological properties.
English and Romanian Brain-to-Text Brain-Computer Interface Word Prediction System

—Brain-Computer Interface (BCI) can recognise the thoughts of a human through various electrophysiological signals. Electrodes (sensors) placed on the scalp are used to detect these signals, or by using electrodes implanted inside the brain. Usually, BCI can detect brain activity through different neuroimage methods, but the most preferred is Electroencephalography (EEG) because it is a non-invasive and non-critical method. BCI systems applications are very helpful in restoring functionalities to people suffering from disabilities due to different reasons. In this study, a novel brain-to-text BCI system is presented to predict the word that the subject is thinking. This brain-to-text can assist mute people or those who cannot communicate with others due to different diseases to restore some of their abilities to interact with the surrounding environment and express themselves. In addition, brain-to-text may be used in different control or entertainment applications. EMOTIV™ Insight headset has been used to collect EEG signals from the subject’s brain. Feature extraction of EEG signals for BCI systems is very important to classification performance. Statistical-based feature extraction has been used in this system to extract valuable features to be used for classification. The datasets are sentences involving some commonly used words in English and Romanian languages. The results of the English language elucidated that K-Nearest neighbour (KNN) has a prediction accuracy of 86.7%, 86.1% for Support Vector Machine (SVM), and 79.2% for Linear discriminant analysis (LDA), while the Romanian language has a prediction accuracy of 96.1%, 97.1%, and 94.8% for SVM, LDA, and KNN respectively. This system is a step forward in developing advanced brain-to-text BCI prediction systems.
Marked rebound of agricultural fire emissions in Asia after the outbreak of COVID-19

East and South Asia are major hotspots of crop straw burning worldwide, with profound impacts on air quality and climate change. The Northeast China Plain (NECP) and Punjab, India, are two of the most fertile areas for crop production, which have large-scale agricultural fires during post-harvest seasons. Leveraging established fire-emission databases and satellite-retrieved agricultural fire spots, we show that, while the years 2018 and 2019 recorded low agricultural fire emissions in both the NECP and Punjab, probably due to the implementation of crop straw sustainable management, fire emissions markedly rebounded in 2020, reaching about 190% and 150% of 2019 levels, respectively. The COVID-19 lockdown measures somewhat disrupted eco-friendly crop straw management through restrictions on labor and transportation availability, such that farmers may have had to burn off crop wastes to clear up the land. We further demonstrate that the increased fire emissions in the NECP resulted in serious particulate matter pollution during the fire season in spring 2020, as opposed to considerable decreases in particles from fossil fuel emissions caused by the COVID-19 lockdown. This study suggests the unintended impacts of the COVID-19 pandemic on the agricultural sector and human health.
Binuclear Atoms : a model to explain Low Energy Nuclear Reactions

In this paper we show that Helium-like Hydrogen spectra obtained by Randell L. Mills [1] and Ultra Dense Hydrogen obtained by Holmlid [2] are interpretable as experimental evidences of Binuclear Atoms. The Hydrogen Binuclear Atom, a model proposed 25 years ago, is a metastable configuration in which the two nuclei are held together at a very short distance in an atom-like configuration. This should be a peculiar configuration of the Hydrogen molecule where nuclei are characterized by a high kinetic energy, and nuclear motion is coupled with electronic motion. This is a completely different model from the usual Born-Oppenheimer picture of atoms and molecules we are used to, where nuclei oscillate about their equilibrium positions and electronic motion is decoupled from the nuclear one. The identification of Helium-like Hydrogen Spectra and Ultra Dense Hydrogen as Binuclear Atoms has a strong impact on one of the main objections to Low Energy Nuclear Reactions (LENR): the overcoming of the Coulomb barrier thus identifying a sound mechanism for the occurrence of LENR. Far for being conclusive, this work has the only aim to take this hypothesis more seriously, and focus the attention of people interested in LENR mechanism on this subject.
The Association of Dietary Magnesium and Prediabetes in Childbearing Chinese Women: Results from China Nutrition and Health Surveillance (2015–2017)

Although several studies have shown the relationship between magnesium and diabetes, there are few studies regarding the association between magnesium status and prediabetes. This study aimed to examine the association between dietary magnesium intake and the risk of prediabetes in childbearing Chinese women (18–44 years). A total of 1981 participants without diabetes were randomly selected from China Nutrition and Health Surveillance (CNHS) in 2015 by considering the regional types and monitoring points, including 1420 normal fasting glucose (NFG) people and 561 prediabetes cases. The Food Frequency Questionnaire (FFQ) and inductively coupled plasma mass spectrometry (ICP-MS) measured dietary and plasma magnesium. The results of this study showed that dietary magnesium intake was inversely associated with fasting plasma glucose. For every 100 mg of magnesium intake, there was a 0.052 mmol/L (95% CI 0.019, 0.085) reduction in fasting plasma glucose (p = 0.002). Compared with the lowest intake quartile (<350.10 mg/day), the third and fourth quartiles (≥405.41 mg/day) reduced the odds risk of prediabetes (OR = 0.63, 95% CI 0.46, 0.88, p = 0.020) by approximately 37%. The dose-response curves for the association between magnesium intake and prediabetes followed an L shape. The odds ratio of prediabetes decreased significantly with increasing dietary Mg intake at less than 410 mg/day, and then the curve leveled off or slightly increased. This study found a negative association between dietary magnesium intake and prediabetes in childbearing Chinese women. The risk of prediabetes was reduced with increasing dietary magnesium intake, and the threshold value was 410 mg/day. This suggests that childbearing women also need to pay attention to their magnesium status.
Partial restoration of chiral symmetry in cold nuclear matter: the ϕ-meson case

The work presented at this workshop is divided into two parts. In the first part, the mass and decay width of the ϕ-meson in cold nuclear matter are computed in an effective Lagrangian approach. The medium dependence of these properties are obtained by evaluating kaon-antikaon loop contributions to the ϕ-meson self-energy, employing medium-modified kaon masses calculated using the quark-meson coupling model. The loop integral is regularized with a dipole form factor, and the sensitivity of the results to the choice of cutoff mass in the form factor is investigated. At normal nuclear matter density, we find a downward shift of the ϕ mass by a few percent, while the decay width is enhanced by an order of magnitude. Our results support the literature which suggest that one should observe a small downward mass shift and a large broadening of the decay width. In the second part, we present ϕ-meson-nucleus bound state energies and absorption widths for four selected nuclei, calculated by solving the KleinGordon equation with complex optical potentials. The attractive potential for the ϕ-meson in the nuclear medium originates from the in-medium enhanced KK¯ loop in the ϕ-meson self-energy. The results suggest that the ϕ-meson should form bound states with all the nuclei considered. However, the identification of the signal for these predicted bound states will need careful investigation because of their sizable absorption widths.
A comparison of numerical methods for the Rayleigh equation in unbounded domains

A second-order finite difference and two spectral methods, including a Chebyshev tau and a Chebyshev collocation method were implemented to determine the linear hydrodynamic stability of an unbounded shear flow. The velocity profile of the basic flow in the stability analysis mimicks that of a one-stream free mixing layer. Local and global eigenvalue solution methods are used to determine individual eigenvalues and the eigenvalue spectrum, respectively. The calculated eigenvalue spectrum includes a discrete mode, a continuous spectrum associated with the equation singularity and a continuous spectrum associated with the domain unboundedness. The efficiency and the accuracy of these discretization methods in the prediction of the eigensolutions of the discrete mode were evaluated by comparison with a conventional shooting procedure. Their capabilities in mapping out the continuous eigenvalue spectra are also discussed.
Unintended Consequences of a Transition to Synchronous, Virtual Simulations for Interprofessional Learners

The coronavirus pandemic shifted in-person environments to virtual environments. Little is known about the effectiveness of fully synchronous, virtual interprofessional education (IPE). This study aims to compare two IPE cases that occurred in-person pre-pandemic and virtual during-pandemic. Two cases are analyzed: a medical error care and a charity care case. Participants were students from various health science disciplines. Assessments were captured through The Interprofessional Collaborative Competency Attainment Survey (ICCAS). Effect sizes were calculated for the pre-and post-surveys and analyzed using Cohen’s d for independent samples. From the in-person collection period, a total of 479 students participated in the medical error simulation and 479 in the charity care simulation. During the virtual collection period, a total of 506 students participated in the medical error simulation and 507 participated in the charity care simulation. In the data for the virtual simulations, the medical error case study maintained a large effect size (0.81) while the charity care simulation had a lesser impact (0.64 effect size). Structural details of the patient cases may be a critical variable. Future research is needed to better understand how health science students can obtain more training to notice the subtle cues from patients assessed through telemedicine modalities.
Swift monitoring observations of Mrk 231: detection of ultraviolet variability

We analyse 168 Swift monitoring observations of the nearest broad absorption line quasar Mrk 231 in the ultraviolet (UV) and X-ray bands, where we detect significant variability in the UV (~2246 A) light curve with a null probability of 4.3 × 10-10 for a constant model. Separately, from an archival sample of Swift observed active galactic nuclei (AGNs), we measure the relation between UV excess variance and luminosity, finding that the normalized UVexcess variance decreases with luminosity. Comparing to thismean relation, the normalized UV excess variance of Mrk 231 is smaller, however within the scatter characterizing the full population. The upper limit of the X-ray excess variance is consistent with other AGN. The power spectrum density (PSD) of the UV light curve can be well fit by a power-law model with a slope of 1.82 ± 0.14 between 10-7.5 and 10-6 Hz, consistent with those for typical AGN, with no obvious quasi-periodical oscillation peaks. The UV variability and its power spectrum suggest that a significant amount of theUVemission of Mrk 231 is from the accretion disc. The consistencies in the normalized UV variability and the shape of the PSD between Mrk 231 and other normal AGN suggest that the origin of UV variability of broad absorption line quasars is similar to other AGN, and dust scattering at large scales such as the torus is not a dominating process for the UV emission of Mrk 231. Significant scattering, if present, is constrained to smaller than ~10 light days. We perform lagged correlation analysis between the UV and X-ray light curves and find the correlation insignificant within the present data.
A modal analysis of segregation of inertial particles in turbulence

An asymptotic solution is derived for prediction of segregation of heavy inertial particles in spatially and temporally varying flows. The general solution, which classifies as the Fredholm integral equation of the second kind, is a function of the spectrum of the second invariant of the velocity gradient tensor of the underlying flow. We introduce a one-dimensional canonical flow oscillating at a single frequency to investigate the behavior of the Lyapunov exponent under a wide range of flow conditions. We show that in a straining flow the Lyapunov exponent can be positive or negative (either dispersion and segregation can occur), whereas it is always positive in a rotating regime. The minimum requirement for trajectory crossing is also predicted. The trajectory crossing never occurs in a rotating regime, whereas it occurs in a straining regime if the Lyapunov exponent is positive. Our analysis shows the Lyapunov exponent, normalized by the particle relaxation time, is minus one half at the minimum and grows linearly at small and nonlinearly with a power of one half at large oscillation amplitudes. The direct numerical simulations confirm these predictions. The extension of our analysis to multimodal excitation is discussed and applied to a two-dimensional synthetic straining and a three-dimensional isotropic forced turbulent flow. We show the results are analogous to that of the one-dimensional case, underscoring the relevance of our canonical setting and the fact that particle segregation arises from the general rather than particular solution of the Stokes drag equation. In comparison to two preexisting models, our model has a more general form and reproduces them under further simplifying assumptions. In contrast to these models that are valid only at small Stokes numbers, our model is valid at small and large Stokes numbers.
Personal‐computer‐based system for real‐time reconstruction of the three‐dimensional ionosphere using data from diverse sources

We present new capabilities of our system for monitoring the ionosphere over a fixed geographical area with dimensions of the order of several thousand kilometers. The system employs a nonlinear representation for electron density that ensures a nonnegative solution. The multidimensional nonlinear inverse problem is efficiently solved using a combination of the Newton‐Kontorovich method and Tikhonov's regularization technique for ill‐posed problems. The system is able to utilize a variety of types of ionospheric data, which are as follows: networks of ground‐ and space‐based (satellite mounted) dual‐frequency GPS receivers provide time series of oblique absolute total electron content (TEC) and/or relative TEC data (directly calculated from the raw dual‐frequency group delays and phase delays, respectively), TEC data from ground‐ or space‐based receivers operating with dual‐frequency beacons mounted on low‐Earth orbit (LEO) satellites, vertical TEC data from orbiting radio altimeters (such as Jason satellite), in situ electron density data from plasma probes on LEO satellites (such as Challenging Minisatellite Payload for Geophysical Research and Application), and electron density profiles from sounders. The resulting solution for the distribution of electron density is guaranteed to be smooth in space and time and to agree with all input data within errors of measurement. Real time performance is attained on a single personal computer with 5 min data refreshment period. Operation of the system is tested on real data with various data types simultaneously present. A new form of the stabilizing functional is developed to ensure reasonable assimilation of the in situ electron density data.
U(1) Charges and Moduli in the D1-D5 System

The decoupling limit of the D1-D5 system compactified on T^4\times S^1 has a rich spectrum of U(1) charged excitations. Even though these states are not BPS in the limit, BPS considerations determine the mass and the semiclassical entropy for a given charge vector. The dependence of the mass formula on the compactification moduli situates the symmetric orbifold Sym^N(T^4) x T^4 conformal field theory in the moduli space. A detailed analysis of the global identifications of the moduli space yields a picture of multiple weak-coupling limits - one for each factorization of N into D1 and D5 charges d1 and d5=N/d1 - joined through regions of strong coupling in the CFT moduli space.
Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future

Lung cancer is one of the malignancies with higher morbidity and mortality. Imaging plays an essential role in each phase of lung cancer management, from detection to assessment of response to treatment. The development of imaging-based artificial intelligence (AI) models has the potential to play a key role in early detection and customized treatment planning. Computer-aided detection of lung nodules in screening programs has revolutionized the early detection of the disease. Moreover, the possibility to use AI approaches to identify patients at risk of developing lung cancer during their life can help a more targeted screening program. The combination of imaging features and clinical and laboratory data through AI models is giving promising results in the prediction of patients’ outcomes, response to specific therapies, and risk for toxic reaction development. In this review, we provide an overview of the main imaging AI-based tools in lung cancer imaging, including automated lesion detection, characterization, segmentation, prediction of outcome, and treatment response to provide radiologists and clinicians with the foundation for these applications in a clinical scenario.