Miracle-dz/UniFAR-FTUs huggingface dataset:
Cognitive radio spectrum assignment based on quantum genetic algorithm

Cognitive radio spectrum assignment based on quantum genetic algorithm is proposed, and simulations are conducted to compare the proposed method with color sensitive graph coloring algorithm. Results show that the proposed method greatly outperforms the color sensitive graph coloring algorithm as it better optimizes network utilization. The proposed method can find the optimal solutions after only several generations, while the relative differences between solutions obtained by color sensitive graph coloring algorithm and the optimal solutions are quite large.
EFFICIENT IDENTIFICATION PROCEDURE FOR INVERSION PROCESSING

1. INTRODUCTION Model extrapolation and data inversion are two important steps in control. Model extrapolation is a step to find control wwiables that are viable for the control process. Data inversion is a step to estimate the control variables horn the sensor data The assumptions ,and goals are different in these two steps. The model extrapolation step searches for control variable that gives minimum errors between modeled and true measurements (without noise); the data inversion step searches for the walues of the control variables that give minimum errors between the modeled and observed measurements (with noise). This paper discusses the control processing used in the magnetospheric image setting. The magnetosphere exists in the region of space that surrounds the earth several hundred kilometers above the earth's surface, extends and is filled with magnetic field lines passing through the earth's surface. The magnetospheric image process is to estimate the ion-population within the field. The ion-population variables ,and the magnetospheric images have no easily modeled physical relationship. The gradient formula is hard to define, therefore gradient dependent optimization techniques are not applicable, such as Robbins-Monro SA, steepest descent, Newton-Raphson, etc. Chase and Roelof (1995) uses the Powell algorithm for magnetospheric model extrapolation. Unfortunately, the Powell method is a trial-and-error type of search algorithm and may break down in data inversion, due to high-noise sensors and multiple root problems. This paper shows that simultaneous perturbation stochastic approximation (SPSA) is more efficient than the Powell algorithm and performs well in the data inversion problem. SPSA was introduced in Span (1987) and thoroughly ,analyzed in Span (1992). The essential feature of SPSA — which accounts for its power ,and relative ease of use in solving challenging multiv,ariate optimization problems — is the underlying gradient approximation that requires
HAWC: The high altitude water Cherenkov observatory

The High Altitude Water Cherenkov Observatory (HAWC) is currently being deployed at 4100m above sea level on the Vulcan Sierra Negra near Puebla, Mexico. The HAWC observatory will consist of 250-300 Water Cherenkov Detectors totaling approximately 22,000 m2 of instrumented area. The water Cherenkov technique allows HAWC to have a nearly 100% duty cycle and large field of view, making the HAWC observatory an ideal instrument for the study of transient phenomena. With its large effective area, excellent angular and energy resolutions, and efficient gamma-hadron separation, HAWC will survey the TeV gamma-ray sky, measure spectra of galactic sources from 1 TeV to beyond 100 TeV, and map galactic diffuse gamma ray emission. The science goals, instrument performance and status of the HAWC observatory will be presented.
Cholesterol Remnants, Triglyceride-Rich Lipoproteins and Cardiovascular Risk

Randomized clinical trials with statins and other lipid-lowering drugs have shown the presence of a “residual cardiovascular risk” in those treated to “target” for LDL-cholesterol. This risk is mainly associated to lipid components other than LDL and in particular to remnant cholesterol (RC) and to lipoproteins rich in triglycerides in fasting and non-fasting conditions. During fasting, RCs correspond to the cholesterol content of the VLDL and their partially depleted triglyceride remnant containing apoB-100. Conversely, in non-fasting conditions, RCs include also cholesterol present in chylomicrons containing apoB-48. Therefore, RCs refer to total plasma cholesterol minus HDL-cholesterol and LDL-cholesterol, that is, all the cholesterol present in the VLDL, chylomicrons and in their remnants. A large body of experimental and clinical data suggests a major role of RCs in the development of atherosclerosis. In fact, RCs easily pass the arterial wall and bind to the connective matrix stimulating the progression of smooth muscle cells and the proliferation of resident macrophages. RCs are a causal risk factor for cardiovascular events. Fasting and non-fasting RCs are equivalent for predicting vascular events. Further studies on drugs effect on RC levels and clinical trials to evaluate the efficacy of RC reduction on cardiovascular events are needed.
Magnetic fields in A-type stars associated with X-ray emission

A common explanation for the observed X-ray emission of A-type stars is the presence of a hidden late-type companion. While this assumption can be shown to be correct in some cases, a number of lines of evidence suggests that low-mass companions cannot be the correct cause for the observed activity in all cases. A model explains the X-ray emission for magnetic Ap/Bp stars, focusing on the A0p star IQ Aur. In this paper we test whether this theoretical model is able to explain the observed X-ray emission. We present the observations of 13 A-type stars that have been associated with X-ray emission detected by ROSAT. To determine the mean longitudinal magnetic field strength we measured the circular polarization in the wings of the Balmer lines using FORS1. Although the emission of those objects that possess magnetic fields fits the prediction of the Babel and Montmerle model, not all X-ray detections are connected to the presence of a magnetic field. Additionally, the measured magnetic fields do not correlate with the X-ray luminosity. Accordingly, the magnetically confined wind shock model cannot explain the X-ray emission from all the presented stars.
Sustainability Learning in Organizations: Integrated Model of Learning Approaches and Contextual Factors

Employee learning plays a vital role in corporate sustainability strategy. Past attempts to map sustainability learning (SL) in organizations have fallen short in explaining the interactions between key learning approaches and the dynamics within the learning environment. This paper is based on a study conducted to explore how employees learned sustainability in organizations and the contextual factors that facilitated their learning process. The study adopted the interpretivist qualitative approach in data collection. Semi-structured interviews were used as the main method, supported by participant observation and document analysis. The findings found evidence suggesting an interplay between several types of learning approaches in SL and highlighted important contextual factors that facilitated the learning environment. Based on the findings, this paper proposes an integrated model for sustainable learning that contributes to refining understanding of the SL process and can be used to assist organizations in enhancing their SL programs. This research addresses the dearth of studies on SL in the organizational setting. It also provides theoretical contribution by providing a clearer overview regarding how SL occurs among employees based on the perspectives of social, experiential, and transformative learning theories.
Constitutive Modelling of Rubber-Like and Biological Materials with Limiting Chain Extensibility

Many rubber-like materials and soft tissues exhibit a significant stiffening or hardening in their stress-strain curves at large strains. The accurate modelling of this phenomenon is a key issue for a better understanding of the thermomechanics of rubber and the biomechanics of arteries, blood vessels, tendons and other biological tissues. In this paper, we consider several strain-energy functions that have been proposed to model this strain hardening effect in the framework of the theory of isotropic hyperelasticity. These models may be divided in two main families: power law models and limiting chain extensibility models, both of which are based on non-Gaussian statistics for their molecular structure. The known limiting chain extensibility models involve a constraint on the strain invariants. Here we consider alternative models which limit the maximum stretch. The two homogeneous deformations of uniaxial extension and pure shear are examined in detail. It is shown that simple modifications of the incompressible neo-Hookean or Varga materials to reflect limiting chain extensibility can be used to match well with the classic experimental data of Treloar. Some remarks on empirical inequalities and ellipticity are also given.
AI-driven designed protein epigenomics

The biological revolutions of computationally designed proteins, induced pluripotent stem cells (iPSCs), and the CRISPR-Cas9 system finally enables modifications that can spur deep understanding of spatial requirement of epigenetic information. This commentary describes the utility of a computationally designed protein, EED Binder (EB), when fused to dCas9 (EBdCas9) for identifying critical sites of PRC2 dependent histone H3K27me3 marks in the chromatin. By using EBdCas9 and gRNA, PRC2 function can be inhibited at specific loci, resulting in precise reduction of EZH2 and H3K27me3 marks, and in some (but not all) locations, activation of the gene and functional outcomes (such as regulation of cell cycle or trophoblast transdifferentiation). Interestingly, a functional TATA box located more than 500bp upstream of a TBX18 TSS was found to be repressed by PRC2, supporting the theory that epigenetic regulators control the repression of transcriptional elements on the promoter region. The EBdCas9 technology may provide a useful tool for controlling gene regulation through epigenomic control.
Exopolysaccharide Biosynthesis in Rhizobium leguminosarum bv. trifolii Requires a Complementary Function of Two Homologous Glycosyltransferases PssG and PssI

The Pss-I region of Rhizobium leguminosarum bv. trifolii TA1 comprises more than 20 genes coding for glycosyltransferases, modifying enzymes, and polymerization/export proteins, altogether determining the biosynthesis of symbiotically relevant exopolysaccharides. In this study, the role of homologous PssG and PssI glycosyltransferases in exopolysaccharide subunit synthesis were analyzed. It was shown that the glycosyltransferase-encoding genes of the Pss-I region were part of a single large transcriptional unit with potential downstream promoters activated in specific conditions. The ΔpssG and ΔpssI mutants produced significantly lower amounts of the exopolysaccharide, while the double deletion mutant ΔpssIΔpssG produced no exopolysaccharide. Complementation of double mutation with individual genes restored exopolysaccharide synthesis, but only to the level similar to that observed for the single ΔpssI or ΔpssG mutants, indicating that PssG and PssI serve complementary functions in the process. PssG and PssI interacted with each other in vivo and in vitro. Moreover, PssI displayed an expanded in vivo interaction network comprising other GTs involved in subunit assembly and polymerization/export proteins. PssG and PssI proteins were shown to interact with the inner membrane through amphipathic helices at their C-termini, and PssG also required other proteins involved in exopolysaccharide synthesis to localize in the membrane protein fraction.
Atomistic Modelling of Structures of Tilt Grain Boundaries and Antiphase Boundaries in β‐Silicon Carbide

Structures of symmetrical tilt grain boundaries and antiphase boundaries in β-silicon carbide are investigated by means of classical molecular dynamics simulations using the Tersoff potential. A structural unit model is used to classify the possible interface structures. Structures and energies are given for 〈001〉 tilt grain boundaries in the angular range 0° ≤ θ ≤ 53.15° and for 〈110〉 tilt grain boundaries in the range 0° ≤ θ ≤ 70.53° of tilt angles. It is found that among 〈110〉 boundaries, those containing antiphase boundaries are the most stable ones in an intermediate angular range.
Fast modelling of light beam diffraction by multilayer structures including a grating coupler

Modelling and optimization of non-linear multilayer all-optical devices including a grating coupler yield numerous prerequisite calculations in the linear regime. Indeed, it is of prime importance to know the influence of various physical and geometrical parameters (thickness and refractive index of each layer; grating groove depth, period and shape; angular spectrum of the input beam) on the diffraction properties. In this paper, it is shown how the well known (but often not well used) analytical Rayleigh-Fourier (RF) method can be used in many cases of practical interest, well above the validity limit generally admitted for a grating of sinusoidal shape (i.e. up to at least a groove depth equal to the grating period). Numerical problems due to the well known 'exponential overflow' due to evanescent waves are solved and so, up to 25 diffraction orders can be currently taken into account (up to 31 in some cases), whatever the number and thickness of substrate layers lying under the guiding grooved layer are. The use of equivalence rules for non-sinusoidal gratings is described. As a straightforward application of the plane wave calculation, the case of a finite beam is treated by taking advantage of its angular spectrum (a fast Fourier transform is used). The theoretical results obtained using the RF method are checked using the rigorous coupled wave method of Chateau and Hugonin. Finally, on a test example, dealing with a limited beam, we show that the diffraction efficiencies, calculated by the method we have implemented, are in perfect coincidence with those obtained experimentally.
To See the Invisible: An Empirical Comparison of Methods for Text-Based Sentiment Analysis of Online Contents From People With Autism Spectrum Condition

With the growing awareness of autism, more and more organizations tried to adapt their service offerings to the special needs of autistic groups. However, the extant relevant research in marketing is still limited, and lacks empirical evidence from the field as well as affective measures. Thus, we explore a secondary research method that is able to capture affective states of autistic people. This research aims to solve two critical assumption questions relevant to the feasibility to utilize online secondary data here: (1) How to efficiently discriminate autistic groups from neurotypical people among a huge amount of unstructured online data, given that the autistic identity is usually unlabeled and invisible? (2) Do the online contents by autistic groups provide good diagnosticity for sentiment analysis, given that many studies in this area doubted the emotional processing ability of autistic people? In our case study, we focused on people who were diagnosed with autism and successfully identified 664 autistic individuals, and then collected their user-generated content on Reddit. In the end, we collected 9305 sentences in 842 posts for sentiment analysis. Based on the results of three content analysis methods in our case study, we recommend a hybrid method that involves a collaboration between human coders and AI-enabled analysis. We provide a detailed procedure about how to implement this method, and also metrics that help to evaluate the analysis results. This method can significantly improve the efficiency of the coding process with an acceptable loss of data points, which solves the first assumption question. This paper further shows that autistic groups would provide emotional information in an online environment but in a different behavioral pattern at the aggregate level, which solves the second assumption question and is initial evidence to encourage future empirical research on autistic consumers to include emotional factors.
Self-Esteem and Determinants of Depression among Undergraduate Nursing Students in Dammam, Saudi Arabia

Background: Due to the extensive workload and clinical demands of nursing education, undergraduate nursing students are particularly prone to depression. Objective: to assess the level of depression and its sociodemographic and academic determinants among undergraduate nursing students. Methods: A descriptive, cross-sectional analysis of 258 nursing students recruited from a college of nursing at Saudi university. The data collection instruments used were a Patient Health Questionnaire-9, and the Rosenberg Self Esteem Scale. An analysis of variance and a regression analysis were conducted to examine the effects of self-esteem and sociodemographic and academic factors on the depression level. Results: The majority of students (64.0%) had a normal level of self-esteem but (31%) had moderate depression levels. Nursing students who were less than 23 years old were more likely to suffer from depression, and those who did not prefer nursing as a profession and believed that learning courses and study schedules were rigid were significantly more depressed than those who did not. Depressive symptom was predicted by stress during study time (В = .308, p = .000), preferred nursing as profession (В = -.171, p = .004), Grade Point Average (В = -.168, p = .005), and gender (В = .124, p = .035). Conclusion: In general, nursing students had a moderate level of depression. The factors identified in this study need to be evaluated further in order to assist in the early detection and control of depression in this significant population and reduce the negative consequences associated with the condition.
Immune Dysregulation in Acute SARS-CoV-2 Infection

Introduction: Neutralizing antibodies have been shown to develop rapidly following SARS-CoV-2 infection, specifically against spike (S) protein, where cytokine release and production is understood to drive the humoral immune response during acute infection. Thus, we evaluated the quantity and function of antibodies across disease severities and analyzed the associated inflammatory and coagulation pathways to identify acute markers that correlate with antibody response following infection. Methods: Blood samples were collected from patients at time of diagnostic SARS-CoV-2 PCR testing between March 2020-November 2020. Plasma samples were analyzed using the MesoScale Discovery (MSD) Platform using the COVID-19 Serology Kit and U-Plex 8 analyte multiplex plate to measure anti-alpha and beta coronavirus antibody concentration and ACE2 blocking function, as well as plasma cytokines. Results: A total of 230 (181 unique patients) samples were analyzed across the 5 COVID-19 disease severities. We found that antibody quantity directly correlated with functional ability to block virus binding to membrane-bound ACE2, where a lower SARS-CoV-2 anti-spike/anti-RBD response corresponded with a lower antibody blocking potential compared to higher antibody response (anti-S1 r = 0.884, P < 0.001; anti-RBD r = 0.75, P < 0.001). Across all the soluble proinflammatory markers we examined, ICAM, IL-1β, IL-4, IL-6, TNFα, and Syndecan showed a statistically significant positive correlation between cytokine or epithelial marker and antibody quantity regardless of COVID-19 disease severity. Analysis of autoantibodies against type 1 interferon was not shown to be statistically significant between disease severity groups. Conclusion: Previous studies have shown that proinflammatory markers, including IL-6, IL-8, IL-1β, and TNFα, are significant predictors of COVID-19 disease severity, regardless of demographics or comorbidities. Our study demonstrated that not only are these proinflammatory markers, as well as IL-4, ICAM, and Syndecan, correlative of disease severity, they are also correlative of antibody quantity and quality following SARS-CoV-2 exposure.
Camera self-calibration technique based on hierarchical reconstruction and bundle adjustment

Camera calibration is essential to obtaining three-dimensional information from two-dimensional image, this paper combines the method of photogrammetry and computer vision, put forward a kind of camera self-calibration based on hierarchical reconstruction and bundle adjustment. The projective reconstruction is obtained by SVD of the measurement matrix, Kruppa equation are deduced for calculating the camera parameters, then upgrade projective reconstruction to Euclidean reconstruction. Executing overall optimization to solve the inner orientation elements of the camera and the lens distortion parameters by bundle adjustment . Characteristics of this method is simple, not requested to build the field of high-precision control, just around the target for three or more images, the inner orientation elements of the camera and distortion parameters are solving ,achieving the camera self-calibration.
Propagation of sonic booms through the atmosphere

During the past 20 years the authors have been involved in developing physically based prediction calculations for sonic booms and other nonlinear atmospheric waves. Early work developed a calculation method for the effects of atmospheric absorption and dispersion on sonic booms in a quiescent atmosphere. The validity of this method has been confirmed by recent collaborative work with the University of Texas [J. Acoust. Soc. Am. 100, 3017–3027 (1996)]. More recently, the authors have been working on a method of predicting the effect of turbulence on sonic boom waveforms in the planetary boundary layer. A Monte Carlo approach has been employed so that histograms of measured variables which are needed for environmental assessment can be developed, rather than a single average value [J. Acoust. Soc. Am. 99, 147–152 (1996)]. This prediction method can be combined with the earlier sonic boom propagation algorithm to predict the ensemble of received waveforms at the ground, given the waveform in the vicinity of.
Polymer-based surface-plasmon-polariton stripe waveguides at telecommunication wavelengths

Long-range surface-plasmon-polariton (LR–SPP) waveguiding along thin gold stripes embedded in polymer is investigated in the wavelength range of 1510–1620 nm. LR–SPP intensity distributions at the output are measured for different stripe widths and thicknesses. Coupling loss of ∼0.5 dB is achieved when exciting the fundamental LR–SPP mode along 10-nm-thick stripes of 6–10 μm width with a polarization maintaining fiber. LR–SPP propagation loss of 6–8 dB/cm is estimated (at 1550 nm) and attributed to scattering from inhomogeneities of the metal stripe and polymer cladding.
An improved score interval with a modified midpoint for a binomial proportion

One of the most basic and important problems in statistical inference is the construction of the confidence interval (CI). In this paper, we propose a novel CI for a binomial proportion by modifying the midpoint of the score interval. The proposed modified interval can solve the ‘downward spikes’ problem of the score interval without enlarging the interval length. Simulation studies are carried out to illustrate the performance of the modified interval. With regard to the criterions of coverage probability, mean absolute error and expected length, our method is competitive among the several commonly used methods for constructing a CI. A real data example is also presented to show the application of our method.