Miracle-dz/UniFAR-FTUs huggingface dataset:
Finite Difference Method Applied to an Unsteady Magnetohydrodynamic Newtonian Fluid with Wall Slip in Darcy-Forchheimer Porous Medium

This study investigated the effects of magnetic field, thermal radiation and wall slip on a Newtonian, incompressible, viscous fluid flowing through a saturated porous medium. Boundary conditions for slip at the wall hold in the fluid. The governing equations are formulated, simplified and non-dimensionalised. The dimensionless equations were solved with the help of Crank Nicolson’s finite difference method. This method converges faster and it is unconditionally stable. Numerical results are presented with the aid of graph to account for the fluid parameters affecting the flow on velocity and temperature profiles.
Potential Role of AGR2 for Mammalian Skin Wound Healing

The limited ability of mammals to regenerate has garnered significant attention, particularly in regard to skin wound healing (WH), which is a critical step for regeneration. In human adults, skin WH results in the formation of scars following injury or trauma, regardless of severity. This differs significantly from the scarless WH observed in the fetal skin of mammals or anamniotes. This review investigates the role of molecular players involved in scarless WH, which are lost or repressed in adult mammalian WH systems. Specifically, we analyze the physiological role of Anterior Gradient (AGR) family proteins at different stages of the WH regulatory network. AGR is activated in the regeneration of lower vertebrates at the stage of wound closure and, accordingly, is important for WH. Mammalian AGR2 is expressed during scarless WH in embryonic skin, while in adults, the activity of this gene is normally inhibited and is observed only in the mucous epithelium of the digestive tract, which is capable of full regeneration. The combination of AGR2 unique potencies in postnatal mammals makes it possible to consider it as a promising candidate for enhancing WH processes.
Selective Tracer Signals of the QCD Plasma State

The research programme of high-energy nucleus-nucleus physics is focused on the study of the fundamental theory of the strong interaction – Quantum Chromo Dynamics (QCD) – in extreme conditions of temperature, density and small parton momentum fraction (low-x) – see e.g. [1] for a recent review. By colliding two heavy nuclei at ultrarelativistic energies one expects to form a hot and dense deconfined medium whose collective (colour) dynamics can be studied experimentally. Lattice QCD calculations [2] predict a new form of matter at energy densities (well) above ecrit ≈ 1 GeV/fm consisting of an extended volume of deconfined and chirally-symmetric (bare-mass) quarks and gluons: the Quark Gluon Plasma (QGP) [3]. Direct information on the thermodynamical properties (like temperature, energy or particle densities, .) and transport properties (such as viscosity, diffusivity and conductivity coefficients) of the QGP can be obtained by comparing the results for a given observable ΦAA measured in nucleus-nucleus (AA, “QCD medium”) to those measured in proton-proton (pp, “QCD vacuum”) collisions as a function of centre-of-mass (c.m.) energy √ sNN, transverse momentum pT , rapidity y, reaction centrality (impact parameter b), and particle type (mass m). Schematically:
Violent relaxation and the R1/4 law

Violent relaxation occurs in a finite region in space with the espace energy as no special threshold. After the escape of positive energy particles and redistribution of weakly bound particles, an isotropic equilibrium function, which reproduces de Vaucouleurs' empirical R 1/4 law for the surface-brightness distribution in elliptical galaxies, is predicted in a natural way. The residuals from the R 1/4 law fit the surface photometry of the standard elliptical NGC 3379. In this simple picture the R 1/4 law reflects neither initial conditions nor internal velocity distribution, but requires a deep central potential.
Power spectral analysis of enhanced scintillation of Quasar 3C459 due to Comet Halley

The radio source 2314+038 (3C459) showed enhanced scintillations on three days at a solar elongation of about 90° as the plasma tail of Halley's Comet swept across it on six days during 16-21 December 1985. If we assume that the plasma velocities in the tail were not constant everywhere, but increased linearly from about 50 kms-1 at the tail axis to the normal average solar wind velocity of 400 kms-1 at the edges where the tail merged with the solar wind, a power spectral analysis of the scintillations shows two ranges of the rms electron density variation t:. N and scale size a. In particular, these are a fine scale zone near the axis where a is in the range 9 to 27 km and t:. N in the range 2 to 5 cm -3 and a zone near the edges with a and t:. N in the ranges 100 to 265 km and 0·4 to 0·8 cm-3 respectively. The assumption of a single velocity of 100 kms-1 throughout the tail shows similar fine scales near the tail axis and large scales near the edges. The scale sizes in that case range from about 18 km at the axis to about 70 km at the edges, corresponding to t:. N of 3·3 and 0·85 cm-3 respectively. A comparison with the results obtained by Slee et al. (1987) shows that there is no radial variation of t:. N. The tail-lag is seen to playa crucial role in determining the correct occulting geometry and the path of the source through the tail.
Wound Care: Wound Management System

Wound care is a critical aspect of healthcare that involves treating and managing various types of wounds, typically caused by injuries, surgery, or chronic diseases such as diabetes. Chronic wounds can be particularly challenging to manage and often require 3 to 6 months of long-term care. In a few instances, healing durations are highly unpredictable and can vary depending on the severity of the wound, the patient’s overall health, and other factors such as medication, nutrition, age, comorbidity, environment, etiology, and immune system function. A chronic wound can significantly impact the quality of life, causing pain, discomfort, limited mobility, higher healthcare cost, and even mortality in severe cases. Effective wound care is crucial for promoting complete and timely healing and reducing the risk of complications that may lead to amputation, infection, and other potentially life-threatening outcomes. This work aims to develop a system that automizes to determine the wound boundaries leveraging the DeepLabV3+SE, measures the wound characteristics such as size and area, and wound shape using a pipeline of morphological operations and connected component analysis modules. The proposed system’s performance was evaluated using the publicly available dataset. Results demonstrate that the DeepLabV3+SE has outperformed with significantly high dice and IOU scores of 0.923 and 0.924, respectively, compared with several state-of-the-art methods.
Optimization methods in data mining and machine learning

Large quantities of data are generated in business, industry, and government. The purpose of this research is to develop tools to interpret this data using mathematical programming techniques for machine learning. The fundamental problem of machine learning considered is the discrimination between the elements of two or more sets in $R\sp{n}. $ Each dimension of the space represents a feature or attribute of the elements of the set. In this research, we propose several methods of determining both linear and nonlinear discrimination functions using mathematical programming. First, we investigate the problem of constructing a linear discriminant function from a geometric perspective. Under this framework we show the close relationship between existing approaches: two linear programming methods: the robust linear programming problem (RLP) and the multisurface method (MSM); and a quadratic generalized optimal plane program (GOP). A novel variation of RLP is proposed (RLP-P) combining the benefits of all three approaches. Theoretical and computational studies of the methods are performed. Two novel methods for constructing linear discriminants are proposed using parametric bilinear programming. In the first method, a parametric error function is introduced that combines minimizing the number of points misclassified and the magnitude of the misclassified points. The second method is a feature selection problem that explicitly minimizes the number of features in each multivariate decision. We propose two versions: feature minimization with bounded accuracy and limited feature minimization. A Frank-Wolfe method is used to solve the bilinear subproblems. Computational results compare favorably with linear programming and other popular classification methods. Finally, we propose a piecewise-nonlinear classification function for discriminating between more than two sets. This function is found by mapping the original attributes into a higher dimensional space and performing piecewise-linear discrimination in that space. This approach is advantageous since when the problem is mapped into a higher dimension feature space the number of variables and constraints in the dual problem does not grow. We propose a novel approach using the two class linear programming problem RLP-P on the multiclass data. Computationally, the RLP-P method performed superior to a prior linear programming method as well as quadratic programming problems.
The Role of Electronic Applications in Transitioning and Developing the Performance of Digital Government Services: Tawakkalna Application for Covid-19 in Saudi Arabia

The Covid-19 pandemic has witnessed the emergence of a new and vital dimension in digital government service – a way of dealing with the outbreak and keeping citizens a s safe as possible. However, it is important to know what citizens themselves think about the applications they have to use. This paper explores Saudi citizens’ attitudes and experiences with the Saudi government’s smart app Tawakkalna, which provides these services, as expressed on the Twitter hashtag # Tawakkalna. A random sample of 766 tweets were analysed during the period 31. 3. 20 to 26.3. 21using a theoretical framework comprising the social constructionist approach, the third electronic wave theory and the Information Systems Success model. The analysis showed that Saudis were largely positive about the services provided and that any negative comments were to do with technical difficulties encountered. The government and other private institutions had a major role in persuading citizens to adopt the app, although influential individuals also had an impact. The study confirmed Twitter as an ideal platform for measuring Saudi public opinion.
Immune Gut Tolerance in Infant Nutrition …..Prebiotics, Probiotics, Postbiotics from Emerging Concept to Application

to develop infant formulas inducing the maturation of immunity and the microbiota that can be observed in breastfed delivered vaginally, representing reference infants. Streptococcus thermophilus, Lactobacillus reuteri DSM 17938, Bifidobacterium breve (BC50), Bifidobacterium lactis Bb12, Lactobacillus fermentum (CECT5716), and Lactobacil - lus rhamnosus GG (LGG) are some of the probiotics added to infant formula, according to a literature review of the past 10 years. The most frequently used prebiotics in published clinical trials are Fructo-Oligosaccharides (FOSs), Galacto-Oligosaccharides (GOSs), and Human Milk Oligosaccharides (HMOs). This review sums up the expected benefits and effects for infants of pre-, pro-, syn-, and postbiotics added to infant formula regarding the microbiota, immunity, and allergies.
Degree estimates as a measure of inference calculation

Scalar inference (SI), e.g., utterances containing the quantifier some being enriched to mean some but not all, is a central topic in semantics-pragmatics. Of recent interest in the experimental literature is the phenomenon of scalar diversity: that different lexical scales exhibit variation is how likely they are to lead to SI. However, studies of scalar diversity have almost exclusively relied on a particular experimental task: the inference task. In this paper, we argue that the inference task suffers from a number of shortcomings: namely, that it biases by providing participants with the stronger alternative and that it obscures pragmatic inferences other than SI. Instead we offer as an alternative a degree estimate task to investigate utterances containing scalar terms. We use the degree estimate task to reassess previous inference task-based findings from the literature on how two manipulations (discourse context and only) affect the likelihood of inference calculation. Our results show that the two tasks produce results that differ from each other in subtle but important ways.
Double-Sampled Echo Planar Imaging with Point Spread Function Mapping at 1.5 Tesla

The presence of Nyquist ghost artifacts and image distortion are two major drawbacks of Echo Planar Imaging (EPI), a popular choice for Functional Magnetic Resonance Imaging (fMRI). Recently, Double Sampled EPI (DS-EPI) has been proposed to eliminate Nyquist ghost artifacts at the expense of an increase in image distortion due to off resonance effects (1). Removing this distortion using digital filtering techniques is a challenging ill-posed inverse problem. While Point Spread Function (PSF) Mapping has been shown to be superior at removing distortion from EPI images when compared to commonly used field mapping techniques (2), both PSF Mapping and field mapping methods are ineffective at removing Nyquist ghost artifacts present in conventional EPI images. This abstract investigates the combination of DS-EPI for removing Nyquist ghost artifacts and PSF Mapping for removing distortion at 1.5 Tesla for fMRI experiments.
Radiation in the Near Zone of a Center-Fed Linear Antenna

The electromagnetic fields far from any antenna can be conveniently described as the sum of the radiation fields of a series of oscillating point multipoles, of which the leading term is a dipole in many cases of practical interest. The form of the fields associated with the nth multipole is independent of the details of the physical layout of the antenna (other than that the layout determines the magnitudes of the multipole moments). However, close the to antenna the electromagnetic fields include quasistatic components as well as radiation terms. A well-known argument due to Hertz [1, 2] gives the fields in the near and far zone of an ideal point dipole. In this and two companion notes [3, 4] we explore examples in which analytic expression can be given for the near and far zone fields of antennas of finite dimensions. Here, the task is to describe the electromagnetic fields, and the Poynting vector [5], produced by oscillating currents of angular frequency ω that flow along a pair of conductors of length a each, that are fed by, say, a coaxial cable at their common ends, as shown in the figure below.
An introduction to quadrature and other numerical integration techniques

The objective in numerical integration is the approximation of a definite integral using numerical techniques. There are a large number of numerical integration methods in the literature and this article overviews some of the most common ones, namely, the Newton-Cotes formulas, including the trapezoidal and Simpson’s rules, and the Gaussian quadrature. Different procedures are compared and illustrated with examples. Discussions about more advanced numerical integration procedures are also included.
COMPARISON OF GLM, GLMM AND GEE POISSON MATHEMATICAL MODELING PERFORMANCE (Case Study: Number of Pulmonary Tuberculosis Patients in Indonesia in 2019-2021)

This study aims to compare the performance of data modeling with Poisson regression with Generalized Linear Model (GLM), Generalized Linear Mixed Model (GLMM), and Generalized Estimating Equation (GEE) modeling. The case study used is a factor that affects the number of Pulmonary Tuberculosis cases in Indonesia with panel data. Based on the AIC criteria, the smallest BIC and RMSE GLMM models perform better than GLM and GEE. In addition, GLMM modeling also has a coefficient of determination value. The results showed that the percentage of the population smoking and the percentage of Unmet Kesehatan had a significant positive influence on the number of Pulmonary Tuberculosis cases. In contrast, the percentage of households who had access to handwashing with soap and proper drinking water significantly negatively influenced the number of Pulmonary Tuberculosis cases. Therefore, the public needs awareness to reduce cigarette consumption and increase environmental access, such as adequate and clean drinking water sources and handwashing.
Implementing Clustering using CSI by K-means

This paper presents a new clustering method called Correlation Similarity Indexing (CSI). It deals with the aspect of instances, which are not been pre-classified in early and do not have a defined attribute to be associated with them. CSI use to reveal the unnoticed class of regarded items. It forms patterns into different group of finding similarity between them with the similarity measure space calculated on the given documents. CSI concentrates in dividing the identified datasets into a number of several clusters that should not have an overlapping aspect with each other. CSI performs proximity analysis to identify the dissimilarity measure between the neighborhoods assigned to the document. It deals with the similarity measure space result between the objects. A cluster level process is proposed to perform the object extraction with similarities. If the given object is in the form of an image then they can also to be clustered by finding the similarity between them by using CSI. CSI involves the K-means clustering algorithm perception build over to it and their action are also to be performed in the aspect of clustering done on documents and images or even in the regional aspect. CSI finds the number of clusters and then groups the similar item sets into respective clusters using K- means have been done on in this system. CSI can be used for large class of applications such as GIS system, Image database exploration, Medical imaging etc. I. INTRODUCTION With the rapid advancement in clustering concepts, new proposal regarding the algorithm have achieved tremendous availability for efficient clustering involved in the concept of mixing. Here, we propose a new algorithm based on clustering concepts known as Correlation Similarity Indexing (CSI). With the indexing at first, the varieties of similarities involved in the documents are to be identified then with the use of K-means algorithm, clustering the similarities have been grouped and the results have been noted for further references of CSI.
using a many-boson symmetric-polynomial basis a

We extend earlier work on fully symmetric polynomials for three-boson wave functions to arbitrarily many bosons and apply these to a light-front analysis of the low-mass eigenstates of � 4 theory in 1+1 dimensions. The basis-function approach allows the resolution in each Fock sector to be independently optimized, which can be more efficient than the preset discrete Fock states in DLCQ. We obtain an estimate of the critical coupling for symmetry breaking in the positive mass-squared case.
Electronically designed amorphous carbon and silicon

We present a new approach to modeling materials. We show that Hellmann–Feynman forces associated with gap states may be used to drive the system to a preferred electronic structure that is also a total energy minimum. We use a priori information about the electronic gap to construct realistic models of tetrahedral amorphous carbon and silicon. We show that our method can be used to obtain continuously tunable concentration of tetrahedrally bonded carbon atoms in models of amorphous carbon. The method is carried out in the tight‐binding approximation to produce computer‐models of amorphous silicon that have fewer structural and optical defects than their conventional MD counterparts. We end by presenting a first test‐case for the ab initio (plane‐wave LDA) implementation of the method.
The Role of Adipokines in Health and Disease

Adipokines are cell-signaling proteins secreted by adipose tissue that has been related to a low-grade state of inflammation and different pathologies. The present review aims to analyze the role of adipokines in health and disease in order to understand the important functions and effects of these cytokines. For this aim, the present review delves into the type of adipocytes and the cytokines produced, as well as their functions; the relations of adipokines in inflammation and different diseases such as cardiovascular, atherosclerosis, mental diseases, metabolic disorders, cancer, and eating behaviors; and finally, the role of microbiota, nutrition, and physical activity in adipokines is discussed. This information would allow for a better understanding of these important cytokines and their effects on body organisms.
Photonic Force Microscopy: simulation of principles and applications

The Photonic Force Microscope is a novel scanning probe microscope based on optical tweezers to hold a probe, which fluctuates in its position due to thermal noise. The three-dimensional position of the probe is detected with high temporal resolution and spatial precision by analyzing the interference of unscattered and forward scattered light. We present the theoretical framework of the optical forces acting on the probe, as well as of the detection signal due to coherent scattering and describe thermal noise position fluctuations by a Brownian dynamics simulation. As an application we simulate the temporal and spatial behavior of a probe tethered to the coverslip (according to the molecular kinesin/microtubule system) at different laser powers and different anchor positions.
Effects of Coronavirus Pandemic on U.S Economy: D-Vine Regression Copula Approach

The rapid spread of Covid-19 since January 2020 has dramatically affected financial markets and economies worldwide, especially in the United States. This paper aims to utilize the regression model of D-Vine Copula introduced by Kraus to investigate the effects of each of three input variables (number of Corona cases, number of deaths, and news). We analyze the influence of those inputs on our three response variables, which are three famous indices in the U.S(S&P 500, NASDAQ100, Dow Jones). First, we examine the impact of the unemployment rate on financial markets and the economy using jobless claims reported by the department of labor during the first five months of the outbreak in the United States. Findings demonstrate that the fitted quantile curves of all input variables suggest that variable death has the most negative effect on S&P500, Dow Jones. In addition, variable news has the most negative influence on NASDAQ100, and we conclude that variable D (GDP news) has the most effect on all mentioned indices.
Information technologies for solving problems of grouping objects based on cluster analysis methods

The article examines the approaches of using information technologies for grouping objects based on the use of cluster analysis methods. Based on Data Mining mining tools, approaches for cluster analysis are considered. The main methods of cluster analysis are considered. For example, the demand for goods in shopping centers is considered and an analysis of customer preferences is carried out. Clustering of objects was carried out on a sufficiently large set of data (transactions) using the Deductor Studio tool. It is shown that for clustering objects with an unknown number of classes, the g-means method is the most preferable. Clusters with the most popular sets have been identified.