Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of Emotional Contagion on Electronic Word of Mouth: An Applied study on Mobile Phone Brand Customers of Social Network Sites in Egypt

This study is aimed to investigate the effects of relationship emotional contagion on Electronic Word of Mouth (E-WOM Quality, E-WOM Quantity, Sender’s Experience), by focusing on the Mobile Phone Brand Customers of Social Network Sites in Egypt. According to the literature review, an analytical model is developed as guidance to test the relationship between the research variables. The Sample Size is 384, and the valid questionnaires are 312, an estimated 90% Of the total Sample Size. The findings show that there is a significant, positive, and direct effect of Emotional Contagion on E-WOM Quality (Cognitive, Emotional, Behavioral). The study also finds that there are significant differences between customer's opinions according to their demographic characteristics (gender, age, social status, educational level, monthly income) regarding emotional contagion, and E-WOM. The results indicated the significant differences between customers opinions according to their demographic characteristics (gender, age, social status, educational level, monthly income) regarding to emotional contagion, and E-Wom. The results found that there are no significant differences between customers opinions according to gender and monthly income. While there were significant differences between customers opinions according to educational level. Besides, there were significant differences between customers opinions about emotional contagion only according to age, these results show that there is a discrepancy between the opinions of customers about their perception of the variables of the study according to their different demographic variables, which is a logical result given that electronic customers are often affected by different factors other than regular customers
GENETIC IMPROVEMENT OF B. japonicum TO TOLERATE ABIOTIC STRESSES VIA PROTOPLAST FUSION

Two distinguished Bradyrhizobium japonicum Egyptian strains, i.e. SE8 and DO4, each was fused with the fast growing Rhizobium fredii USDA201 strain. Nine fusants were obtained in the first attempt (USDA 201 and SE8) and four fusants were obtained in the second attempt ( USDA201 and DO4). All fusants were characterized on the bases of nodulation efficiency, salt and drought tolerance, heavy metals resistance and antibiotic resistance. Electrophoretic plasmid profiles of these fusants and their parents were studied and the relatedness between them was determined according to their phylogeneric tree. Some fusants retained their nodulation efficiency and showed higher efficiencies in salt, drought and /or heavy metals resistance than their parents.
Applications of Power Series Solutions of Membrane Equilibrium Equations to the Optical Evaluation of Membrane Mirrors with Curvature

Abstract : Power series solutions of the Hencky-Campbell equilibrium equations for an initially planar circular membrane in a state of uniform pre-strain are used to analyze the performance of an in-house laboratory membrane mirror curved by a uniform vacuum pressure applied to one side. The results provide theoretical verification of the improvement in surface figure achievable by controlling the amount of pre-strain. In particular, a pre-strain of only 0.25%, corresponding to a uniform radial displacement of the edge of an 11-inch diameter membrane mirror by about 0.014 inches, is predicted to achieve nearly 95% reduction in the maximum deviation of an f/8 membrane from a reference sphere. These results are in good agreement with laboratory measurements of spherical aberration using a Hartmann wavefront sensor. The model was also used in an extensive investigation of the effects of control parameters (pressure and pre-strain) on the improvement of surface figure for lower f-number systems. The results indicate that comparable improvement in systems of f-number less than f/6 would require membrane materials capable of withstanding pre-strains on the order of 2.0% or greater.
Comparison of efficient sparse reconstruction techniques applied to inverse synthetic aperture radar images

Abstract. Compressed sensing can be a valuable method with which to acquire high-resolution images, reducing the stored amount of information. This objective may be pursued without using any prior knowledge of the images, unlike the standard information compression algorithms do. Information compression can be obtained by a simple matrix multiplication, but the process of reconstructing the original image could be very expensive in terms of computation requirements. We are interested in comparing different reconstruction techniques for compressed air-to-air inverse synthetic aperture radar images, looking for a sensible compromise between performance results and complexity. In more detail, the compared algorithms are iterative thresholding, basis pursuit and convex optimization. Furthermore, particular attention has been devoted to a more appropriate way of splitting large-sized images in order to obtain smaller matrices with uniform sparseness for reducing the computational load.
Revisiting canonical gravity with fermions

Fermions constitute an important component of matter and their quantization in presence of dynamical gravity is essential for any theory of quantum gravity. We revisit the classical formulation adapted for a background free quantization. The analysis is carried out with the Hilbert-Palatini form for gravity together with the Nieh-Yan topological term which keeps the nature of Barbero-Immirzi parameter independent of inclusion of arbitrary matter with arbitrary couplings. With dynamical gravity, a priori, there are two distinct notions of `parity' - orientation reversing diffeomorphisms and improper Lorentz rotations. The invariance properties of the action and the canonical framework are different with respect to these and gravitational origin of parity violation seems ambiguous.
TEACHING PRONUNCIATION: A STUDY ON THE SECONDARY SCHOOLS IN KHULNA, BANGLADESH

Pronunciation is an integrated and integral part of second or foreign language learning since it directly affects learners’ communicative competence as well as performance. Notwithstanding, the teaching of EFL/ESL pronunciation is still peripheral and neglected in the syllabus and classroom, especially in Khulna district in Bangladesh. It is a matter of regret that most of the school-level learners in our country are not taught English pronunciation and, hence, it is overlooked. The reasons are not far to seek. A good number of teachers in our country are not aware of the significance of teaching pronunciation and its impact on effective communication. Khulna is an industrial town, yet it is lagging behind in learning English. Although there are some government and non-government high schools here, no developed teaching and testing system of English Language is used in this region. Here the teachers follow century-old traditional testing system for writing and reading English. As for listening and speaking, there is no form of testing that can aid effective pronunciation teaching in secondary schools. Therefore, teaching pronunciation is highly ignored in this area. This paper reviews the notion of pronunciation teaching and gives a theoretical background for developing pronunciation teaching. It empirically studies the learners’ and teachers’ attitudes to the significance of accurate pronunciation teaching in English. Additionally, it reflects on their preferences and beliefs about pronunciation teaching. Finally, it provides some recommendations for effective remedial measures and feedback techniques for developing pronunciation teaching and learning in the light of the theoretical and empirical findings of the research.
Local TV Newsroom Diversity: Race and Gender of Newscasters and Their Managers

ABSTRACT Research has shown that minority newscasters can increase media interest and trust among minority viewers, while also finding people of color are underrepresented as members of the media. This study collected race and gender data of 4,317 newscasters from 64 U.S. television markets, as well as their news directors and general managers. It examines relationships between diversity of on-air staffs and race and gender of management, market size, and location. The study found news teams are significantly more diverse at stations with minorities in leadership positions. It considers the implications of these findings using representation theory.
Design of Simulink-based 3D Hexapod Robot Model and Implementation of Hexapod Robot Foothold Planning

: In this world, there are many dangerous and complicated environments in which humans cannot work. Mobile robots are an important method that people use to solve these problems. Hexapod robots are known for their excellent static and dynamic stability, as well as strong environmental adaptability in mobile robots. Hexapod robots play a critical role in overcoming complex and challenging terrain. The paper attempts to finish two different parts of the work. The first part is to construct a Simulink-based CORIN robot model, and the second part is to develop a foothold planning algorithm for hexapod robots. The highly visual robot model can be used to display the results of related algorithms. It can also be applied in the Simulink physical environment to simulate robot overturning recovery, robot wall walking and other advanced robot motions through the use of the contact force blocks. Moreover, for the foothold planning algorithm, a collision detection algorithm based on the distance between the feature points and the obstacles is implemented. 18 feature points are set on the CORIN robot to ensure that the robot optional footholds are all collision-free points. Based on the path length, the distance from the target point and the collision state, a cost function is established, which is used in the CORIN robot foothold path planning algorithm. Results show that based on the kinematics of CORIN robot leg and modeling method in the Simulink, a complete CORIN hexapod robot model that can be controlled by specifying the position of the end effector or setting three different revolute joint angles has been designed. Furthermore, a radical foothold planning algorithm that can avoid collisions and focus on reaching the target point is implemented.
Mapping the mountainous climate in the Matas de Minas region, Brazil which influences the top-quality coffee beverages

All the characteristics of the mountainous environment directly influence the coffee crops, and subsequently, on the final coffee note, that reflects the quality of the beverage produced in a region. Despite increasing coffee research, little is known about the influence of the water indices, factors, and the elements of climate on top-quality coffee production potential. Thus, the present study was carried out aiming to analyze the water indices, causes, and aspects of clime, to identify those that most contribute to the potential production of high-quality Arabica coffee beverages in a mountain environment. We considered harvesting the coffee fruits at the cherry stage in 26 municipalities in the Matas de Minas region in the Atlantic Forest Biome in the eastern state of Minas Gerais, and the International Cup of Excellence method was adopted for the sensory evaluation. The principal components analysis and the multiple linear regression (MLR) were used to relate the local environmental variables with the final grade of the coffee beverage. As a result, the Multiple Linear Regression model showed the value of 0.63 for R2. This result means that the joint variability of all the variables considered explained 63% of the changes in coffee beverage quality. And the altitude impact on the grade achieved for the coffee beverage produced in the Matas de Minas region, represented by β, was 0.008068, meaning that for every 100 meters of increase in the altitude, there is an approximate increment of 0.8 points in the final note achieved for the coffee beverage. Among all the environmental characteristics studied, the climatic factor altitude was the main contributor to the coffee top-quality production potential in the Matas de Minas region.
Multiple Buried Target Reconstruction Using a Multiscale Hybrid of Diffraction Tomography and CMA-ES Optimization

In this article, a hierarchical stochastic optimization algorithm for profiling of multiple high-contrast buried objects in large investigation domains (IDs) is presented. As this problem is highly nonlinear and ill-posed, a combination of different profiling modalities is required to tackle the challenges. First, an initialization step using the qualitative diffraction tomography (DT) method is performed to not only limit the ID to scatterers’ locations but also obtain an approximation of their dielectric permittivity range. Then, an algorithm that combines the iterative multiscaling approach (IMSA) with the reconstruction capabilities of covariance matrix adaptation evolution strategy (CMA-ES) is implemented. IMSA is a multistep strategy, which starts with coarse meshes in lower frequencies and, then, step by step, tightens the ID to the newly found domain of scatterers and uses finer meshes for partitioning them. This procedure enhances the resolution without increasing the number of unknowns. In each step, the inversion process is executed using the global optimization technique of CMA-ES. The proposed technique uses the full advantages of global optimization technique and at the same time, by executing it on a multiscaling scheme and using the initializing step, reduces the number of unknowns, the degree of freedom in the search space, and the required measured data. The numerical assessments for various scenarios are performed, which clearly shows an acceptable dielectric profile retrieval, even for inhomogeneous dielectric distributions or noisy measurements. Moreover, a comparison between CMA-ES and other global optimization algorithms is performed, which reveals the outperformance of CMA-ES in these scenarios.
Ultrasoft Fermionic Mode in Yukawa Theory at High Temperature

A possible collective fermionic excitation with ultrasoft momentum at extremely high temperature T is examined in Yukawa theory with massless fermion (coupling constant is g). We find that the fermion propagator has a pole at ω = ±|p|/3 − iζ for ultrasoft momentum p, where ζ ∼ g 4 T lng 1 , and the residue is Z ∼ g 2 . It is shown that one needs to take into account the asymptotic masses and the damping rate of hard particles to get a sensible result for such an ultrasoft fermionic mode; possible vertex correction turns out unnecessary for the scalar coupling in contrast to the gauge coupling.
Hybrid control of electrohydrodynamic 3D printer cone jet

In this article, the control of an electrohydrodynamic 3D printer is studied. The proposed controller has a hybrid piecewise linear feedback form which is designed based on a hybrid model of the printer. The hybrid model is obtained via a gray box identification process whose structure is proposed utilizing the results of the finite element simulation of the printer within the COMSOL Multiphysics software. The asymptotic stability of the hybrid control combined with a hybrid observer is proven using the Lyapunov theory. In addition, the obtained control is applied to the finite element model of the printer to check its performance which shows the success of the controlled system in tracking the reference height for the printer jet cone.
Graph Kernels by Spectral Transforms

Many graph-based semi-supervised learning methods can be viewed as imposing smoothness conditions on the target function with respect to a graph representing the data points to be labeled. The smoothness properties of the functions are encoded in terms of Mercer kernels over the graph. The central quantity in such regularization is the spectral decomposition of the graph Laplacian, a matrix derived from the graph's edge weights. The eigenvectors with small eigenvalues are smooth, and ideally represent large cluster structures within the data. The eigenvectors having large eigenvalues are rugged, and considered noise. Different weightings of the eigenvectors of the graph Laplacian lead to different measures of smoothness. Such weightings can be viewed as spectral transforms, that is, as transformations of the standard eigenspectrum that lead to different regularizers over the graph. Familiar kernels, such as the diffusion kernel resulting by solving a discrete heat equation on the graph, can be seen as simple parametric spectral transforms. The question naturally arises whether one can obtain effective spectral transforms automatically. In this paper we develop an approach to searching over a nonparametric family of spectral transforms by using convex optimization to maximize kernel alignment to the labeled data. Order constraints are imposed to encode a preference for smoothness with respect to the graph structure. This results in a flexible family of kernels that is more data-driven than the standard parametric spectral transforms. Our approach relies on a quadratically constrained quadratic program (QCQP), and is computationally practical for large datasets.
Numerical approach for enhanced oil recovery with polymer flooding using CMG-STARS program

The polymer recovery efficiency of cationic polyacrylamide (PAM) has been evaluated for use as polymer flooding in the Basra oil reservoir Initially, rheological, physical and petro physical were studied. Additionally, the polymer flooding core flooding experiment has been tested to see how well PAM solutions can be recovered. The comparative study between experimental and numerical study for PAM solution has been accomplished in terms of rheological properties , relative permeability curve and oil production. The results showed that PAM solution exhibit shear thinning effect which can efficiently improve the macroscopic sweep efficiency as well as microscopic displacement efficiency, and viscosity increase with high concentration. The overall recovery efficiency of 94.61 % from sandstone was determined for 2500ppm PAM flooding. By contrast, brine water flooding was found to be 47.76%, showing that it was less effective than PAM solution. Cost analysis is another factor that affects a project's success. To support the experimental results, a simulation by the Computer Modelling Group (CMG) study has also been conducted.
Machine Learning Algorithms for Detecting and Analyzing Social Bots Using a Novel Dataset

Social media is internet-based technology and an electronic form of communication that facilitates sharing of ideas, documents, and personal information. Twitter is a microblogging platform and is the most effective social service for posting microblogs and likings, commenting, sharing, and communicating with others. The problem we are shedding light on in this paper is the misuse of bots on Twitter. The purpose of bots is to automate specific repetitive tasks instead of human interaction. However, bots are misused to influence people’s minds by spreading rumors and conspiracy related to controversial topics. In this paper, we initiate a new benchmark created on a 1.5M Twitter profile. We train different supervised machine learning on our benchmark to detect bots on Twitter. In addition to increasing benchmark scalability, various autofeature selections are utilized to identify the most influential features and remove the less influential ones. Furthermore, over-under-sampling is applied to reduce the imbalance effect on the benchmark. Finally, our benchmark compared with other stateof-the-art benchmarks and achieved a 6% higher area under the curve than other datasets in the case of generalization, improving the model performance by at least 2% by applying over-/undersampling.
An 8 × 8 ultra‐wideband tightly coupled dipole active phased array

An 8 × 8 ultra‐wideband tightly coupled dipole active phased array with 64 T/R modules is presented in this paper. The proposed active phased array with 64 T/R modules is able to operate over a 5:1 bandwidth (2.05 ~ 10.3 GHz) in receiving mode and 8 ~ 10 GHz in transmitting mode. The array utilized tightly coupled balanced antipodal dipole antenna elements, which exhibits excellent scanning performance (±60° in E plane, ±60° in D plane and ±45° in H plane) and low cross‐polarization characteristic (<−28 dB at broadside). The tiled transmitting/receiving modules (T/R modules) can successfully control the amplitude and phase of the feeding signal in each active channel. Besides, the total gain of all channels in receiving mode and transmitting mode are 30 and 26.4 dB, respectively, which shows good performances in receiving and transmitting signals. A prototype of 16 × 16 antenna array is fabricated. The central 8 × 8 elements are active elements that are connected to 64 channels T/R module. The remaining elements are dummy elements that are connected to 50Ω loads to alleviate the edge effect. The measurements of central 8 × 8 elements are taken after the assembling of the antenna array and T/R modules. Good performances are achieved and the application potential of this phased array system is also validated.
Exclusive meson leptoproduction and spin dependent generalized parton distributions

Exclusive meson leptoproduction from nucleons in the deeply virtual exchanged boson limit can be described by generalized parton distributions (GPDs). Including spin dependence in the description requires 8 independent quark-parton and gluon-parton functions. The chiral even subset of 4 quark-nucleon GPDs are related to nucleon form factors and to parton distribution functions. The chiral odd set of 4 quark-nucleon GPDs are related to transversity, the tensor charge, and other quantities related to transversity. Different meson or pho ton production processes access different combinations of GPDs. This is analyzed in terms of t-channel exchange quantum numbers, J PC and it is shown that pseudoscalar production can isolate chiral odd GPDs. There is a sensitive dependence in various cross sections and asymmetries on the tensor charge of the nucleon and other transversity parameters.
Fine-Grained Classification of Optical Remote Sensing Ship Images Based on Deep Convolution Neural Network

Marine activities occupy an important position in human society. The accurate classification of ships is an effective monitoring method. However, traditional image classification has the problem of low classification accuracy, and the corresponding ship dataset also has the problem of long-tail distribution. Aimed at solving these problems, this paper proposes a fine-grained classification method of optical remote sensing ship images based on deep convolution neural network. We use three-level images to extract three-level features for classification. The first-level image is the original image as an auxiliary. The specific position of the ship in the original image is located by the gradient-weighted class activation mapping. The target-level image as the second-level image is obtained by threshold processing the class activation map. The third-level image is the midship position image extracted from the target image. Then we add self-calibrated convolutions to the feature extraction network to enrich the output features. Finally, the class imbalance is solved by reweighting the class-balanced loss function. Experimental results show that we can achieve accuracies of 92.81%, 93.54% and 93.97%, respectively, after applying the proposed method on different datasets. Compared with other classification methods, this method has a higher accuracy in optical aerospace remote sensing ship classification.