Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison between Swift and pre-Swift gamma-ray bursts

The gamma-ray burst (GRB) mission Swift has made a much deeper GRB survey than any previous one. I present a systematical comparison between GRB samples detected with pre-Swift missions and those from Swift, in order to investigate whether they show any statistical difference. Our Swift GRB sample includes the bursts detected by Swift/BAT before 2007 September. With both flux-limited surveys and redshift-known GRB samples, I show that, apparently, the observed distributions of the redshifts, T90, and log N – log P are significantly different, but not for the spectral hardness ratio, fluence and Eiso. The redshifts of the Swift GRB sample are statistically larger than those of pre-Swift GRBs, with a mean of 1.95±0.17 compared to ~ 1 for pre-Swift GRBs. The cosmological effect on the observables is thus considerable. This effect on the spectral hardness ratio, fluence and Eiso is cancelled out, and the distributions of these quantities indeed do not show significant differences between the Swift and pre-Swift GRBs. Taking this effect into account, I found that the corrected distributions of T90 for long GRBs and log N – log P observed with Swift/BAT are also consistent with those observed with CGRO/BATSE. These results indicate that the Swift and pre-Swift GRBs are from the same population.
Revealing the time lag between slope stability and reservoir water fluctuation from InSAR observations and wavelet tools— a case study in Maoergai Reservoir (China)

ABSTRACT Reservoir water fluctuation in supply and storage cycle have strong triggering effects on landslides on both sides of reservoir banks. Early identification of reservoir landslides and revealing the relationship between slope stability and the triggering factors including reservoir level and rainfall, are of great significance in further protecting nearby residents’ lives and properties. In this paper, based on the small baseline subset time series method (SBAS-InSAR), the potential landslides with active displacements in the river bank of Maoergai hydropower station in Heishui County from 2018 to 2020 were monitored with Sentinel-1 data. As a result, a total of 20 unstable slopes were detected. Subsequently, it was found through a gray correlation analysis that the fluctuation of the reservoir water level is the main triggering factor for the displacement on unstable slopes. This paper applied wavelet tools to quantify the time lag between slope stability and reservoir water fluctuation, revealing that the displacement exhibits a seasonal trend, whose high-frequency signal displacement has an interannual period (1 year). Based on the Cross Wavelet Transform (XWT) analysis, under the interannual scale of one year, the reservoir water fluctuation and nonlinear displacement show a clear common power in wavelet. Additionally, a time lag of 65–120 days between slope stability and reservoir water fluctuations has been found, indicating that the non-linear displacements were behind the water level changes. Among the factors affecting the time lag, the elevation of the points and their distance to the bank shore show Pearson’s correlation coefficients of 0.69 and 0.70, respectively. The observed time lag and correlations could be related to the gradual saturation/drainage processes of the slope and the drainage path. This paper demonstrates the technical support to quantitatively reveal the time lag between slope stability and reservoir water fluctuation by InSAR and wavelet tools, providing strong support for the analysis of the mechanisms of landslides in Maoergai reservoir area.
Numerology-Aware MCOT Selection for Improving Fairness in 5G NR-U Networks

NR-U enables 5G to access the unlicensed bands. However, a 5G NR-U transmitter has to follow the Listen Before Talk (LBT) procedure, where the transmitter has to sense the channel for a while prior to its transmission. Since NR-U is a scheduled technology, the transmission begins only at slot boundaries. But the LBT procedure may end before reaching the slot boundary, and then the transmitter has to wait for a gap period to align with the slot boundary. 5G has different numerologies, and the slot duration of each numerology is different. When nodes with different numerologies are contending for channel access, the node with a lower slot duration has a high probability of winning the channel since, after the LBT procedure, its next slot mostly comes sooner, which leads to fairness issues. In this letter, we propose an algorithm to improve the fairness in multinumerology contention by adaptively changing the Maximum Channel Occupation Time (MCOT) of each numerology according to the slot duration and the number of nodes in each numerology.
Medial axis transformation with single-pixel and connectivity preservation using Euclidean distance computation

A novel medial axis transformation (MAT) algorithm extracted from the Euclidean distance transform of a binary image is presented. The extracted MAT satisfies the following properties: reconstructivity, rotation-invariance, connectivity, and single-pixel width. The preservation of properties is proved, and some experimental results are shown. The skeleton is trimmed by removing short branches to make it simpler and useful for object recognition. <<ETX>>
Molecular Capture of Mycobacterium tuberculosis Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences

The application of whole genome sequencing of Mycobacterium tuberculosis directly on clinical samples has been investigated as a means to avoid the time-consuming need for culture isolation that can lead to a potential prolonged suboptimal antibiotic treatment. We aimed to provide a proof-of-concept regarding the application of the molecular capture of M. tuberculosis genomes directly from positive sputum samples as an approach for epidemiological and drug susceptibility predictions. Smear-positive sputum samples (n = 100) were subjected to the SureSelectXT HS Target Enrichment protocol (Agilent Technologies, Santa Clara, CA, USA) and whole-genome sequencing analysis. A higher number of reads on target were obtained for higher smear grades samples (i.e., 3+ followed by 2+). Moreover, 37 out of 100 samples showed ≥90% of the reference genome covered with at least 10-fold depth of coverage (27, 9, and 1 samples were 3+, 2+, and 1+, respectively). Regarding drug-resistance/susceptibility prediction, for 42 samples, ≥90% of the >9000 hits that are surveyed by TB-profiler were detected. Our results demonstrated that M. tuberculosis genome capture and sequencing directly from clinical samples constitute a potential valid backup approach for phylogenetic inferences and resistance prediction, essentially in settings when culture is not routinely performed or for samples that fail to grow.
Research on fusion algorithm of infrared and visible image based on generative adversarial network and attention mechanism

Generative adversarial network was proposed in 2014, and its core is a two-person zero-sum game, which improves the quality of generated images through the mutual game between the generator and the discriminator. In 2018, Ma et al. applied Generative Adversarial Networks to the field of infrared and visible image fusion. The generator generates an image that retains both the infrared intensity and visible light detail texture information. By inputting the visible image as "true data" into the discriminator, it retains more detailed texture parts in the visible image in the fusion result, and finally obtains a nice effect. However, since the model only uses one discriminator, it loses some infrared intensity information and detail information in the infrared image. The gradient of the loss function is not enough to describe the detailed texture of visible light, and information such as brightness and contrast is not considered, so the final fusion result has poor visual effect. This paper builds an infrared and visible image fusion network based on the LSGAN framework and uses dual discriminators, and introduces a convolutional attention module in the generator to make the fusion image pay more attention to infrared intensity information. It is proposed to use MS-SSIM loss function to constrain the generated image, so that the fused image has a higher structural similarity with the source image. Through qualitative and quantitative analysis, it is proved that this method retains the main infrared intensity information while retaining the detailed texture information of the visible image.
Poly(styrene-b-isobutylene-b-styrene) Triblock Copolymer Fiber Generation with Centrifugal Spinning, and its Potential Application in Oil Collection

In this paper the creation of poly(styrene-b-isobutylene-b-styrene) triblock copolymer (SIBS) fiber creation with centrifugal spinning is discussed. The centrifugal spinning procedure was tested at various concentrations, rotation speeds, and needle inner diameters (ID). At the most stable condition of 25 wt% and ID = 0.55 mm needle, fibers with 4.75. 3 $\mu$m average diameter were acquired. Electrospinning of SIBS with polyethylene glycol was also carried out for the sake of comparison, which yielded beaded fibers at the given conditions. The productivity of centrifugal spinning was roughly two orders of magnitude higher than that of the electrospinning. The measured water contact angle on these fiber mats was 133-135°, demonstrating a highly hydrophobic property. Contact angle experiments with sunflower oil showed perfect wetting, highlighting the potential for SIBS fiber mats to be used in the separation of oil from water.
Numerical Methods for Solution of the Extended Linear Quadratic Control Problem

Abstract In this paper we present the extended linear quadratic control problem, its efficient solution, and a discussion of how it arises in the numerical solution of nonlinear model predictive control problems. The extended linear quadratic control problem is the optimal control problem corresponding to the Karush-Kuhn-Tucker system that constitute the majority of computational work in constrained nonlinear and linear model predictive control problems solved by efficient MPC-tailored interior-point and active-set algorithms. We state various methods of solving the extended linear quadratic control problem and discuss instances in which it arises. The methods discussed in the paper have been implemented in efficient C code for both CPUs and GPUs for a number of test examples.
Generalized Least Squares Applied to Time Varying Parameter Models

This paper shows the formal equivalence of Kalman filtering and smoothing techniques to generalized least squares. Smoothing and filtering equations are presented for the case where some of the parameters are constant. The paper further shows that generalized least squares will produce consistent estimates of those parameters that are not time varying. complicate the mathematics the results. This paper shows the formal equivalence of Kalman filtering and smoothing techniques to generalized least squares. Smoothing and filtering equations are presented for the case where some of the parameters are constant. The paper further shows that generalized least squares will produce consistent estimates of those parameters that are not time varying. The paper by Sant provides a useful service to the profession by showing clearly the formal equivalence of Kalman filtering and smoothing methods with generalized least squares. In addition, he derives the ap- propriate form of the filtering and smoothing equations for a model with both constant and time varying parameters. Although much of what is contained in this paper has either appeared elsewhere in the literature or is known to practitioners (and therefore is assumed to be obvious to others), much of the literature is
Double-pass fiber Raman laser-a powerful and widely tunable in the ultraviolet, visible, and near-infrared fiber Raman laser for biomedical investigations

We present the operating principle, general features, and advantages of a novel concept for the development of powerful and widely tunable fiber Raman lasers (FRL's), based on the high-power pumping of large-core low-loss multimode fibers using a simple double-pass laser arrangement with Littrow-prism-tuned emission. Basic experimental results obtained with various modification double-pass FRL schemes, which provide the generation of both discretely tunable emission in the UV/blue (360-493 nm) at /spl lambda//sub p/=355 nm and continuously tunable emission in the visible/near-IR (0.54-1.01 /spl mu/m) at /spl lambda//sub p/=532 nm, are reported. The double-pass FRL schemes permit powerful and widely tunable spectral components to be generated, due to the double passing of nonlinear-converted laser emission through the optical fiber, maximum cavity feedback, the use of multimode fibers, and high-power pumping.
NFATc1 Regulation of Dexamethasone-Induced TGFB2 Expression Is Cell Cycle Dependent in Trabecular Meshwork Cells

Although elevated TGFβ2 levels appear to be a causative factor in glaucoma pathogenesis, little is known about how TGFβ2 expression is regulated in the trabecular meshwork (TM). Here, we investigated if activation of the cytokine regulator NFATc1 controlled transcription of TGFβ2 in human TM cells by using dexamethasone (DEX) to induce NFATc1 activity. The study used both proliferating and cell cycle arrested quiescent cells. Cell cycle arrest was achieved by either cell–cell contact inhibition or serum starvation. β-catenin staining and p21 and Ki-67 nuclear labeling were used to verify the formation of cell–cell contacts and activity of the cell cycle. NFATc1 inhibitors cyclosporine A (CsA) or 11R-VIVIT were used to determine the role of NFATc1. mRNA levels were determined by RT-qPCR. DEX increased TGFβ2 mRNA expression by 3.5-fold in proliferating cells but not in quiescent cells or serum-starved cells, and both CsA and 11R-VIVIT inhibited this increase. In contrast, the expression of other DEX/NFATc1-induced mRNAs (myocilin and β3 integrin) occurred regardless of the proliferative state of the cells. These studies show that NAFTc1 regulates TGFβ2 transcription in TM cells and reveals a previously unknown connection between the TM cell cycle and modulation of gene expression by NFATc1 and/or DEX in TM cells.
Using Extraneous Information and GMM to Estimate Threshold Parameters in Tar Models

A prominent class of nonlinear time series models are threshold autoregressive models. Recently work by Kapetanios (2000) has shown in a Monte Carlo setting that the superconsistency property of the threshold parameter estimates does not translate to superior performance in small samples. Another issue concerning inference for the threshold parameters relates to estimation of their standard errors. As the asymptotic distribution of the threshold parameters is neither normal nor nuisance parameter free, an outstanding issue is how to obtain standard errors and confidence intervals for them. This paper aims to address these issues. In particular, we suggest that using extraneous information on the location of the threshold parameters may lead to better estimates. The extraneous information comes in the form of moment conditions that relate residuals of standard threshold models to shocks driving other variables. Additionally the paper considers the problem of estimating standard errors and confidence intervals for threshold parameter estimates. We suggest use of the bootstrap for this problem.
Ultrahigh-resolution liquid crystal display with gray scale

An atomic force microscope was used to write planar alignment patterns on a polyimide-coated glass substrate. Paired with a substrate treated for homeotropic alignment, the resulting hybrid liquid crystal cell produced fixed gray scale images with pixel sizes of order 1 μm. The physics and efficacy of this architecture are compared to a cell having planar alignment at both substrates.
Symbolic target detection in SAR imagery via rotationally invariant-weighted feature extraction

This article introduces the application of a physics-based symbolic image partitioning method to detect targets in synthetic aperture radar (SAR) imagery. ‘Targets’ in this case refer to vehicular objects which produce a distinct radar return pattern, and have spatial characteristics that are known a priori. The proposed Rotationally Invariant Symbolic Histogram (RISH) detection method co-analyses both target and speckle statistics, and significantly reduces computational requirements by partitioning the data into a discrete number of state representations. RISH requires only one pass for robust detection, unlike other SAR detection methods which rely on difference metrics calculated using multiple passes. To improve performance in high-resolution data, RISH uses a weighted feature extraction algorithm to avoid the common requirement of processing each pixel of the image equally. The weighted structure extracts geometrically undefined and rotationally invariant target features. This article details the analysis of 24 experimentally obtained very high-frequency (VHF)-band SAR magnitude images using this novel approach to SAR target detection. In localizing small (~8.4 m2) foliage-concealed targets, without the aid of pre-processing, this method results in high performance characteristics (90% true positive) with a low Type-II error rate of 6.4 false alarms per 1 × 106 m2. With the addition of change detection, RISH lowers the error rate by 85%.
Chemical composition in fast rotators main sequence stars

Using public data of the Ultraviolet and Visual Echelle Spectrograph Paranal Observatory Project (UVES POP) the projected rotational velocities, v sin i, for 16 stars in the eld of the galactic cluster IC 2391 (o Vel Cluster) were obtained using the method of the Fourier transform (FT). We found that only 12 of these objects are cluster members and separating them in two sets (main sequence stars and evolved stars) a correlation of the rotation velocity with the eectiv e temperature was found, indicating a nearly equal orientation of the rotation axis for the member stars. The ratios N/C and O/C obtained for the main sequence stars increase with v sin i and show evidence of mixing induced by rotation.
Cultural differences in ethics and values during conflict resolution: A disentangling approach

Using a social motives framework, I tested two process models that linked ethical beliefs of Relativism and Idealism to conflict resolution preferences of Compromise and Forcing through social values (Face, Collectivism, Dignity, and Honor). Cultural relativity of the models was assessed across three-cultural subgroups (Confucian, Hindu, and Aristotelian). Results revealed that the process model for Idealism held across cultures as Idealism was indirectly related to Forcing through Dignity and Honor. However, the results suggested that the process model for Relativism differed across cultural groups as Relativism was indirectly related to Compromise through both Face and Collectivism for Confucian cultures, but indirectly related to Compromise through Collectivism for Hindu cultures, and indirectly related to Compromise through Face for Aristotelian cultures. By examining the relationships between ethical beliefs and social values commonly attributed to cultural differences in conflict resolution preferences, the study disentangles the culture-specific and culture-free relationships which are important to resolving interpersonal conflict.
Medical Images Classification Using Hybrid Deep Learning CNN-PSO-GA

Convolutional Neural Network (CNN) has made outstanding achievements in image processing and detection. The recent research uses CNN to classify the medical images, but this performance depends on its hyperparameters chosen by the programmer. Choosing these parameters is a difficult process if done manually, so there is a need to find out alternative methods. To solve this problem, the researchers hybridized a CNN with particle swarm optimization (PSO) to find better values for these hyperparameters. PSO was hybridized using genetic algorithm to solve the retired particle problem. The purpose of this research is to take advantage of the achievements of deep learning in classifying medical images. The proposed model was tested with three datasets: malaria, COVID-19, and pneumonia. The model achieved 99.5%, 100%, and 99.7% accuracy for the above datasets respectively. These results were compared with the results of the standard CNN; the proposed model surpassed the standard CNN in overall performance.
Analytical optimization of the torque of a permanent-magnet coaxial synchronous coupling

This paper recalls the analytical formula of the tangential force between two permanent magnets and shows that this force does not vary linearly with the magnet depth. This formula is then applied to calculate the torque transmitted by a permanent magnet coaxial synchronous coupling. Such an analytical approach allows the analysis of the influence of various parameters characteristic of this coupling such as the number of pairs of poles, its mechanical airgap, the magnet-width and magnet-length, the mean radius of the airgap, and the angular shifting between the two rotors. These analytical expressions make an efficient optimization of the coupling structure in compliance with practical or economical constraints possible.
Incompressibility of maps and the homotopy invariance of Čech cohomology

dimension of a space Y is the largest integer r for which every map Y a normal space with dimension less than r, the loop map is compressible. are determined for the i2-compressibility dimension of Eilenberg-Maclane spaces of type (Z, 2n) and (Z p k, 2ri). In application this is used to settle the question as to when Cech cohomology based on finite covers is a homotopy invariant functor.
Collisional processes induced in the n=3 helium sublevels by electrons in a low-pressure plasma. Application to the electron density measurement

Quenching and excitation transfers in helium induced in the 3S, 3P and 4D states by electron collisions are investigated in a pulsed plasma by means of laser induced fluorescence (LIF) spectroscopy. The averaged cross sections compare well within a factor 1-3 with the Born-Bethe approximation and the Griem formulae. The dependence of these cross sections on the oscillator strength and the energy of the transitions are well fulfilled. Finally it is shown that from the measurement of the quenching rate of the 3889 AA LIF signal in a low pressure plasma it is possible to determine the electron density with a reasonable accuracy.
An Improved Integral Sliding Mode Control for PMSM Drives Based on New Variable Rate Reaching Law With Adaptive Reduced-Order PI Observer

This article focuses on speed control in a permanent-magnet synchronous motor (PMSM). To promote the speed tracking performance and anti-disturbance property of the PMSM speed regulation system against various unknown disturbances, such as parameter mismatch, load torque variation, friction force, and so on, an improved integral sliding mode control (IISMC) method based on a new variable rate reaching law (NVRRL) and an adaptive reduced-order proportional integral observer (AROPIO) is proposed. The NVRRL method can reduce the chattering, and convergence time of the system state at the same time compared to the existing sliding mode reaching law. An anti-windup sliding mode surface is proposed to overcome the overshoot problem of the integral sliding mode surface when the reference speed is large and rises in steps or large ramps. To further improve the system tracking accuracy and anti-disturbance ability, an AROPIO is used to estimate lumped disturbances and provides feedforward compensation for the IISMC. The simulation and experimental results show that the proposed IISMC + AROPIO method not only achieves high speed tracking accuracy and fast convergence speed but also achieves strong anti-interference ability.