Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasonic harmonic generation from fatigue-induced dislocation substructures in planar slip metals and assessment of remaining fatigue life

An analytical model is presented of the microelastic-plastic nonlinearities resulting from the interactions of a stress perturbation with dislocation substructures and cracks that evolve during cyclic fatigue of planar slip metals. The interactions are quantified by a material nonlinearity parameter β extracted from acoustic (ultrasonic) harmonic generation measurements. The β parameter for a given fatigue state is highly sensitive to the volume fractions of active persistent Luders bands (PLBs) and PLB internal stresses, as well as to the densities, loop lengths, and dipole heights of the dislocation monopoles and dipoles that form the PLBs. The β parameter is predicted to increase monotonically with the increase in the hardness of the metal during cyclic loading, thus allowing an unambiguous assessment of the remaining life of the material. The model is applied to the calculation of β as a function of percent full fatigue life of IN100 nickel-base superalloy. The theoretical predictions are in good agre.
KIT Gene Mutation Causing Piebaldism Associated with Multiple Café Au-Lait Like Macules and Freckling: Delineating a Cause of this Coexistence

Piebaldism is a rare genetic disorder of congenital leukoderma caused by mutation in KIT proto-oncogene receptor tyrosine kinase. We present a 10-year-old boy with congenital depigmented macules suggestive of piebaldism associated with café au lait macules and skin fold freckling complicating the diagnosis. A diagnosis of piebaldism was made via exome sequencing that showed a pathogenic variant of KIT gene with no pathogenic variants of NF1 or SPRED1 gene. Our current understanding of the KIT tyrosine kinase function may provide a better explanation into this phenotypic coexistence and does not necessarily represent an overlap with Neurofibromatosis type 1.
MODEL AGNOSTIC FEATURE SELECTION FOR FAIRNESS

The study aims to introduce a model agnostic, filter based framework for fair feature selection. While in the field of fairness, numerous models have been deployed to achieve fair and unbiased results but questions that arise are (i) How often the tasks are designed in association with fairness concepts? (ii) How often we check for dependency between protected attributes (also known as sensitive attributes; e.g. race, age, sex etc.) and other features in the data to assess fairness instead of employing an algorithm right away? (iii) How often we use a framework that yields a fair set of features? An obvious answer could be that fairness techniques and fairness criteria are not yet central to the industrial work and we tend to substitute temporary solutions for the sake of attaining accuracy in the results. Another reason could be that the classifier-independent methodologies are not available explicitly as an implementable solution in a straightforward setting. Fair ML is a field concerned with making data and prediction fair but comes with some level of trade-off between accuracy and fairness. Fairness is a subjective concept and has diverse definitions associated to it, it also depends on different scenarios concerning an individual or a group. The majority of technical fairness interventions are algorithmic, which makes them computationally expensive and complex to implement. Our goal is to answer the questions mentioned above by developing a model-agnostic, fast, and easily implementable feature selection framework based on task level formulations using information theory. This feature selection technique will focus on selecting a subset of features based on the reliance of protected attributes (race, sex, age etc.) on other random variables present in the data and maximal dependency of features with the target.
Influence of Binaural Beats Stimulation of Gamma Frequency over Memory Performance and EEG Spectral Density

Similar to short-term memory, working memory cannot hold information for a long period of time. Studies have shown that binaural beats (BB) can stimulate the brain through sound, affecting working memory function. Although the literature is not conclusive regarding the effects of BB stimulation (stim) on memory, some studies have shown that gamma-BB stim (40 Hz) can increase attentional focusing and improve visual working memory. To better understand the relationship between BB stim and memory, we collected electroencephalographic data (EEG) from 30 subjects in 3 phases—a baseline, with gamma-BB stim, and control stim—in a rest state, with eyes closed, and while performing memory tasks. Both EEG data and memory task performance were analyzed. The results showed no significant changes in the memory task performance or the EEG data when comparing experimental and control conditions. We concluded that brain entrainment was not achieved with our parameters of gamma-BB stimulation when analyzing EEG power spectral density (PSD) and memory task performance. Hence, we suggest that other aspects of EEG data, such as connectivity and correlations with task performance, should also be analyzed for future studies.
Assessment of Negative Bias Temperature Instability Due to Interface and Oxide Trapped Charges in Gate-All-Around TFET Devices

The effects of negative bias temperature instability (NBTI) on emerging tunnel FET (TFET) devices are setting of important reliability concern for analog/digital circuit design. In this work, NBTI effects in gate-all-around tunnel FET (GAA TFET) devices have been thoroughly analyzed and the unstressed p-type GAA TFET transfer characteristics are calibrated with the experimentally published data to tune the models used for the numerical analysis. The GAA device is stressed for different time durations and for different temperatures to analyze the capture and emission probabilities of the trapped charges. The effect of oxide trapping, interface trapping, and trapping of charges at several regions in the channel are analyzed individually, as well as collectively. The sub-threshold voltage of the TFET device is also investigated and it has been found that the change in the sub-threshold slope is not at all degraded by NBTI stress.
Morphological Macular Changes Under Brolucizumab Treatment for Neovascular Age-Related Macular Degeneration Refractory to Previous Anti-VEGF Treatment Compared with Treatment-Naive Eyes

Purpose To evaluate the morphological macular changes and fluid dynamics under brolucizumab treatment in eyes refractory to previous anti–vascular endothelial growth factor (anti-VEGF) treatment for neovascular age-related macular degeneration (nAMD) compared with treatment-naive eyes. Methods Retrospective study of all eyes treated with brolucizumab for nAMD between 2020 and 2021 with a fixed injection regimen and one year follow-up. Treatment-naive eyes (TN) were compared with eyes refractory to previous treatment with bevacizumab, ranibizumab, or aflibercept (RT). The primary outcome measure was change of best-corrected visual acuity (BCVA). Secondary outcome measures included foveal central thickness (FCT), presence of intra- or subretinal fluid (IRF, SRF) and presence of pigment epithelial detachment (PED) at any time point during treatment in both groups. Results Seventeen TN eyes and 17 RT eyes were included. Mean BCVA and mean FCT in TN eyes had significantly improved after 3 months and continued to improve during treatment (p<0.05 and p=0.001, respectively). In RT eyes, mean BCVA did not change significantly while mean FCT had improved after 3 months of treatment and remained stable thereafter. SRF or PED were more frequent in RT eyes compared with TN eyes (p=0.003 and p=0.005, respectively). Conclusion After 3 months of treatment, the BCVA increased significantly only in TN eyes, while the FCT was significantly reduced in both groups. IRF appears to be similarly seen in both groups after the loading phase; however, SRF and PED appear to be more frequent in the RT eyes compared with TN eyes.
Nonreciprocal diffraction through dielectric gratings with two-dimensional chirality

It is generally assumed that the propagation of light through a dielectric medium is always reciprocal under reversal of the propagation direction. We show that in an all-dielectric diffractive system nonreciprocal polarization changes are possible for individual diffracted beams when the diffractive medium possesses two-dimensional chirality. This nonreciprocity is characterized by different eigenstates for the system for the forward and reverse directions respectively, yet it is also entirely consistent with the predictions of the Lorentz reciprocity lemma.
Sagittarius A(asterisk) and the positions of infrared sources in the galactic center

Observations of the central region of the Galaxy have been made with the University of Rochester 32 x 32 element InSb array camera in the 1-5 micron wavelength range. The 2.2-micron images have been used to determine positions of infrared features in comparison with that of the galactic center compact radio source Sgr A(asterisk). No infrared source lies precisely at the Sgr A(asterisk) position in the images. However, the infrared source closest to the Sgr A(asterisk) position is a previously little studied source directly south of IRS 7 that was called IRS 16NW and not the often referred to IRS 16C. IRS 16NW is coincident with one of the very red objects seen in various 1-micron images of this region.
Reusing devices with memory in device independent quantum key distribution

Recently, it has been suggested [3] that device independent approaches to quantum key distribution may be of limited utility, since standard protocols could leak key to the adversary when devices are used repeatedly. We propose a means by which devices with memories could be reused from one run of a quantum key distribution protocol to the next while bounding the leakage to Eve, under the assumption that one run of the protocol could be completed securely using devices with memories.
Facile Synthesis of Cyclic Polyamidine with High Cationic Degree Using Environmentally Benign Approach

An environmentally benign approach was developed to fabricate cyclic polyamidine via polymerization in supercritical carbon dioxide (SCCO2) and subsequently amidinization in water. Synthetic parameters were evaluated using response surface methodology. In comparison with aqueous solution polymerization for the fabrication of PNVF-co-PAN, polymerization using SCCO2 is favorable to promote the yield and viscosity of PNVF-co-PAN and diminished reaction time on account of excellent solvation capacity and transport property of SCCO2. Replacing the traditional water solution with SCCO2 as a green solvent could heighten the purity of PNVF-co-PAN by virtue of the excellent extraction of SCCO2. The cationic degree (5.66 mmol/g) of polyamidine fabricated by environmentally benign approach was significantly higher than that in previous reported studies.
Nonparametric mixed-effects models for repeated binary data arising in serial dilution assays : An application to estimating viral burden in AIDS

Abstract This article develops methods for estimating treatment effects in mixed-effects models using outcome data gathered from serial dilution assays. Our application allows us to estimate the viral burden of HIV infection before and after antiviral treatment from cell dilution assays. This assay is designed to determine the infectious units per patient peripheral blood mononuclear cell (PBMC). The infectious unit is the amount of virus required to produce detectable HIV infection in PBMC's from healthy, uninfected donors. At each dilution level of the patient cells, one observes whether or not it was possible for the virus from these cells to infect donor cells. Thus the assay result for each subject consists of a series of repeated binary outcomes. We propose an analytic approach in which patient-specific titers (measures of viral burden) are modeled as random effects from an unknown distribution, and treatment effects are modeled as fixed. This approach makes use of all assay results, even if many as.
Wavelet matrices: algorithms and applications

Filter bank and multiresolution analysis have been widely used for wavelets. However, for multi-dimensional signals, the convolution algorithm needed for filter bank and multiresolution analysis is too complicated. In this paper, we propose a basic wavelet matrix, which can have either perfect reconstruction or desired result according to the chosen filter properties. The basic wavelet matrix method can be applied to pyramid wavelet decomposition, visual-based wavelet decomposition, tensor product, wavelet packets and adaptive tree-structured decomposition. Edge effects, the choice of filters are also discussed.
Molecular and infrared observations of the conical reflection nebula 1548C27

We present 12 CO J=2-1 and 3-2 and infrared observations of the region around the optical conical reflection nebula and jet source 1548C27. The CO data show that this source lies on the edge of a large molecular cloud. Wings of emission are evident in the higher transition, and the morphology indicates that this gas lies at the interface between the reflection nebula and the molecular cloud. A dynamical distance of 2.4 kpc is derived from the cloud velocity, indicating that the source is considerably more distant than previously thought. This implies a source mass and luminosity consistent with an Ae/Be-type star
Machine learning in biophotonics: progress and challenges

Machine learning has paved the way for many breakthroughs in recent years, from the development of powerful computers to advances in image and voice recognition. The technique is applied to a wide range of problems, yet it is particularly useful in solving biomedical questions such as how cells function, get infected by microorganisms, and respond to drugs. Separately, biophotonics is the science involving the mixing of life and light sciences, where numerous health challenges have been studied. However, regardless of outstanding achievements and contributions to life sciences and medicine, there are significant challenges and exciting opportunities in both biophotonics and machine learning. In this article, we provide an overview of machine learning in biophotonics and its applications in cell imaging, diagnosis, and treatment. In biophotonics, machine learning is used to classify, detect, and segment cells or pathogens. Our group aims to use machine learning to detect, classify, and differentiate normal cells from diseased cells for diagnostics at point-of-care settings. It is noteworthy that we focus on the challenges of working with high-dimensional data such as continuous spectra from optical imaging and spectral features from cell imaging. The resulting increase in technological advances has led to an ever-increasing need for easy access, faster, and more efficient methods of healthcare. Applications of machine learning in biophotonics are already providing the answer in many ways, and through it, there is the potential to fully transform healthcare in the future.
Nocturnal Low-Level-Jet-Dominated Atmospheric Boundary Layer Observed by a Doppler Lidar over Oklahoma City during JU2003

Abstract Boundary layer wind data observed by a Doppler lidar and sonic anemometers during the mornings of three intensive observational periods (IOP2, IOP3, and IOP7) of the Joint Urban 2003 (JU2003) field experiment are analyzed to extract the mean and turbulent characteristics of airflow over Oklahoma City, Oklahoma. A strong nocturnal low-level jet (LLJ) dominated the flow in the boundary layer over the measurement domain from midnight to the morning hours. Lidar scans through the LLJ taken after sunrise indicate that the LLJ elevation shows a gradual increase of 25–100 m over the urban area relative to that over the upstream suburban area. The mean wind speed beneath the jet over the urban area is about 10%–15% slower than that over the suburban area. Sonic anemometer observations combined with Doppler lidar observations in the urban and suburban areas are also analyzed to investigate the boundary layer turbulence production in the LLJ-dominated atmospheric boundary layer. The turbulence kinetic ener.
CURRENT SHEET ENERGETICS, FLARE EMISSIONS, AND ENERGY PARTITION IN A SIMULATED SOLAR ERUPTION

We investigate coronal energy flow during a simulated coronal mass ejection (CME). We model the CME in the context of the global corona using a 2.5D numerical MHD code in spherical coordinates that includes coronal heating, thermal conduction, and radiative cooling in the energy equation. The simulation domain extends from 1 to 20 Rs. To our knowledge, this is the first attempt to apply detailed energy diagnostics in a flare/CME simulation when these important terms are considered in the context of the MHD equations. We find that the energy conservation properties of the code are quite good, conserving energy to within 4% for the entire simulation (more than 6 days of real time). We examine the energy release in the current sheet as the eruption takes place, and find, as expected, that the Poynting flux is the dominant carrier of energy into the current sheet. However, there is a significant flow of energy out of the sides of the current sheet into the upstream region due to thermal conduction along field lines and viscous drag. This energy outflow is spatially partitioned into three separate components, namely, the energy flux flowing out the sides of the current sheet, the energy flowing out the lower tip of the current sheet, and the energy flowing out the upper tip of the current sheet. The energy flow through the lower tip of the current sheet is the energy available for heating of the flare loops. We examine the simulated flare emissions and energetics due to the modeled CME and find reasonable agreement with flare loop morphologies and energy partitioning in observed solar eruptions. The simulation also provides an explanation for coronal dimming during eruptions and predicts that the structures surrounding the current sheet are visible in X-ray observations.
Prospective Use of Probiotics to Maintain Astronaut Health during Spaceflight

Maintaining an astronaut’s health during space travel is crucial. Multiple studies have observed various changes in the gut microbiome and physiological health. Astronauts on board the International Space Station (ISS) had changes in the microbial communities in their gut, nose, and skin. Additionally, immune system cell alterations have been observed in astronauts with changes in neutrophils, monocytes, and T-cells. Probiotics help tackle these health issues caused during spaceflight by inhibiting pathogen adherence, enhancing epithelial barrier function by reducing permeability, and producing an anti-inflammatory effect. When exposed to microgravity, probiotics demonstrated a shorter lag phase, faster growth, improved acid tolerance, and bile resistance. A freeze-dried Lactobacillus casei strain Shirota capsule was tested for its stability on ISS for a month and has been shown to enhance innate immunity and balance intestinal microbiota. The usage of freeze-dried spores of B. subtilis proves to be advantageous to long-term spaceflight because it qualifies for all the aspects tested for commercial probiotics under simulated conditions. These results demonstrate a need to further study the effect of probiotics in simulated microgravity and spaceflight conditions and to apply them to overcome the effects caused by gut microbiome dysbiosis and issues that might occur during spaceflight.
Enhancing the Classification Accuracy of Rice Varieties by Using Convolutional Neural Networks

The aim of this study is to enhance the classification accuracy of rice varieties that are quite similar in external observation. In this study, 17 rice grain varieties popularly planted in Vietnam are classified with an Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) models. The two CNN models (modified VGG16 and modified ResNet50) are based on pre-trained VGG16 and Resnet50 models. Two datasets are used in the experiments: a feature dataset extracted using an extended improved local ternary pattern (extended ILTP) method, and an image dataset generated with a data augmentation technique. The feature dataset was fed into the ANN, while the image dataset was fed into the CNN models. The highest classification accuracies of ANN, modified VGG16, and modified ResNet50 models are 92.82%, 96.41%, and 97.88%, respectively. The results show that the modified VGG16 and ResNet50 models significantly improved classification accuracy of the 17 varieties of rice. In addition, the experiments show that the dimensions of the image dataset can affect the performance of the CNN models. This research can be developed for applications of rice varieties classification and identification.
Dynamics of multibody systems in planar motion in a central gravitational field

Multibody systems in planar motion are modelled as two or more rigid components that are connected and can move relative to each other. The dynamics of such multibody systems in planar motion in a central gravitational force field is analysed. The equations of motion of the system include the equations for the orbital motion of the bodies, the orientation (attitude) of the assembly, and the relative orientation (shape) of the bodies with respect to each other. Dynamic coupling between these degrees of freedom gives rise to complex dynamical systems that are usually not integrable. Relative equilibria, corresponding to circular orbits of the multibody system, are obtained. The free dynamics has a symmetry due to a cyclic coordinate. Routh reduction is carried out to eliminate this coordinate and obtain the reduced dynamics. The stability of the relative equilibria is analysed using the Routh stability criterion when it is applicable; an expansion of the Hamiltonian in normal form is used otherwise. We apply the general results to a multibody system consisting of two hinged planar bodies, each modelled as a rigid massless link with a point mass at one end with their other ends connected by a hinge joint. We obtain the relative equilibria of this model, and carry out a stability analysis for the relative equilibria. Numerical simulations using a symplectic integrator are carried out for perturbations to these relative equilibria, to confirm their stability properties.
Artificial Retina Using Thin-Film Photodiodes and Thin-Film Transistors

An artificial retina using thin-film photodiodes (TFPDs) and thin-film transistors (TFTs) is proposed, which is expected to be suitable for living bodies, low-emitting and cost-effective, and which is an improvement of the system-on-panel devices in which the in-pixel and pixel-to-pixel operations are executed using TFTs. The characteristics of a TFPD and TFTs are measured, and their models are made for a circuit simulator. Circuits of the retina pixel and retina array with some improvements are invented, and it is confirmed using a circuit simulator that the artificial retina using TFPDs and TFTs can operate. Edge enhancement is also confirmed, and its dependence on the characteristic deviations of TFTs is analyzed.