Miracle-dz/UniFAR-FTUs huggingface dataset:
Angry Posts Mobilize: Emotional Communication and Online Mobilization in the Facebook Pages of Western European Right-Wing Populist Leaders

The rise of right-wing populists in Western Europe has often been linked to their ability to exploit social media affordances to fuel anger. While scholarship has already examined the emotional dimension of the populist right’s online communication, with some researchers studying specifically the fuelling of anger among social media users, we still lack empirical proof of the mobilizational effectiveness of what we describe as “anger-triggering communication. ” To explore this question, in this article, we develop a statistical and topic analysis of right-wing populists’ Facebook pages in Italy, France, Spain, and Germany during the 2019 European Union (EU) election campaign. We find that (a) right-wing populists on Facebook have a significantly higher number of “Angry” Facebook reactions per post compared to their political adversaries; (b) there is a positive and significant effect of the number of Angry reactions on the number of times a post is shared; (c) Angry reactions and Shares are overrepresented in posts on immigration and security, but anger-fuelled mobilization is not limited to these topics. These findings contribute to the scholarship on social media, emotional communication, and populism, adding insights on the mobilizational effectiveness of negative campaigning. The article highlights that stoking public anger, especially around controversial issues such as immigration and security, is a rewarding tactic because it increases motivational strength, and contributes to triggering high-threshold interactions such as sharing, which, in turn, are key for achieving virality in the diffusion of political messages.
A hybrid data compression scheme using quaternary decomposition and selective multistage vector quantization

The hybrid coding method investigated uses classified vector quantization (VQ) based upon the quaternary decomposition of image blocks into 32*32, 16*16, 8*8, or 4*4 subblocks before using the DCT (discrete cosine transform) to encode the blocks. The coefficients of the DCTs are then zonal-sampled and vector quantized. The codebooks for the VQ are optimized by both the traditional Linde-Buzo-Gray (1980) method and by a simulated annealing (SA) algorithm. In addition a method of selective multistage VQ (SMVQ) is investigated in which only those vectors not matched well enough by a codebook vector are allowed a second stage of VQ. These methods produce excellent results at bit rates of 0.8586 b/pixel. for non-SMVQ methods and 0.994 b/pixel for SMQV methods for 512*512 color images. <<ETX>>
Visualization method of movement trajectory in outfielder's fly ball catching technique using baseball video

In this study, we proposed a method for detecting a player’s movement trajectory from video images to evaluate the defensive range of an outfielder in baseball, quantitatively. Using this method, we succeeded in accurately estimating and visualizing the movement trajectory of a player by identifying only specific players and estimating the homography of changes in the angle of the view by matching the feature points using SIFT.
Discovery of Myeloid-Derived Suppressor Cell-Specific Metabolism by Metabolomic and Lipidomic Profiling

The endogenous factors that control the differentiation of myeloid-derived suppressor cells (MDSCs) are not yet fully understood. The purpose of this study was to find MDSC-specific biomolecules through comprehensive metabolomic and lipidomic profiling of MDSCs from tumor-bearing mice and to discover potential therapeutic targets for MDSCs. Partial least squares discriminant analysis was performed on the metabolomic and lipidomic profiles. The results showed that inputs for the serine, glycine, and one-carbon pathway and putrescine are increased in bone marrow (BM) MDSC compared to normal BM cells. Splenic MDSC showed an increased phosphatidylcholine to phosphatidylethanolamine ratio and less de novo lipogenesis products, despite increased glucose concentration. Furthermore, tryptophan was found to be at the lowest concentration in splenic MDSC. In particular, it was found that the concentration of glucose in splenic MDSC was significantly increased, while that of glucose 6-phosphate was not changed. Among the proteins involved in glucose metabolism, GLUT1 was overexpressed during MDSC differentiation but decreased through the normal maturation process. In conclusion, high glucose concentration was found to be an MDSC-specific feature, and it was attributed to GLUT1 overexpression. These results will help to develop new therapeutic targets for MDSCs.
All-purpose Health Monitoring Belt using HIoT Devices

With the wide deployment of sensors in everyday devices like smartwatches and mobile phones, traditional types of machinery are being replaced with advanced IoT (Internet of Things) devices that offer much better portability and flexibility. They have the added benefit of being easier to implement and producing data spontaneously. IoT has already had a powerful impact on the healthcare industry, creating a new niche of healthcare devices called HIoT (Healthcare Internet of Things). When implemented accurately HIoT devices have produced results that are far richer and more precise than previous data collection models. One of the issues with having numerous devices that record singular health data points is the wide range of heterogenous visuals that are generated. Additional efforts have to be made to analyze individual data points. Dispersions caused by the dynamicity of nodes used in data collection can also shape data visuals in ways that make it harder to comprehend. Throughout this article, the differences in data collected using HIoT devices against existing and broadly used clinical healthcare tools are explored. Additionally, the possibility of combining multiple HIoT devices to collect a range of health data points is delved into. Furthermore, an investigation into seeing if comprehensive visualizations, which in turn help healthcare professionals make swifter prognoses, is also probed. To test some of these theories an architecture for an all-purpose heart monitoring belt that is useful in creating multiple health data points is proposed. The expected goal is to see if the proposed design is theoretically capable of producing information that can be further analyzed on a cloud computing platform and processed with different machine learning techniques.
Necessary and sufficient conditions for robust perfect tracking under variable structure control

The tracking control of linear MIMO systems with structured uncertainty is considered. A necessary and sufficient condition for robust asymptotic tracking employing variable structure techniques in the presence of multiplicative uncertainty is derived. The constructive proof of the theorem provides an explicit formula for controller synthesis. Copyright © 2002 John Wiley & Sons, Ltd.
Optimal synchronization in pulse-coupled oscillator networks using reinforcement learning

Abstract Spontaneous synchronization is ubiquitous in natural and man-made systems. It underlies emergent behaviors such as neuronal response modulation and is fundamental to the coordination of robot swarms and autonomous vehicle fleets. Due to its simplicity and physical interpretability, pulse-coupled oscillators has emerged as one of the standard models for synchronization. However, existing analytical results for this model assume ideal conditions, including homogeneous oscillator frequencies and negligible coupling delays, as well as strict requirements on the initial phase distribution and the network topology. Using reinforcement learning, we obtain an optimal pulse-interaction mechanism (encoded in phase response function) that optimizes the probability of synchronization even in the presence of nonideal conditions. For small oscillator heterogeneities and propagation delays, we propose a heuristic formula for highly effective phase response functions that can be applied to general networks and unrestricted initial phase distributions. This allows us to bypass the need to relearn the phase response function for every new network.
Demagnetizing Field in Nonellipsoidal Bodies

A general method for calculating the (nonuniform) demagnetizing field in ferromagnetic bodies of arbitrary shape is described. The theory is based upon the assumption that the magnitude of the magnetization vector is constant throughout the sample and that its direction coincides with the direction of the local magnetic field at any point within the sample. The total magnetic field is expressed as a series of ascending powers in M/H0, where M is the saturation magnetization and H0 the applied magnetic field. The first term of this series expansion (first‐order theory) gives the demagnetizing field for very large applied fields, i.e., for a uniformly magnetized sample. The higher‐order corrections (we consider in detail only the first correction term; second‐order theory) take account of the fact that the sample is not in general uniformly magnetized. The general theory has been applied to rectangular slabs and circular cylinders. The first‐order demagnetizing field has been calculated for rectangular slab.
Exergy analysis of solar gas turbine system coupled with Kalina cycle

This paper provides an exergy analysis of a solar gas turbine system coupled with Kalina cycle with the aim of examining the exergy destruction in key components and seeking the potential to improve the thermodynamic performance of this system. An exergy analysis mode is developed by the concept of energy level. The exergy destruction in key components is illustrated and compared with solar gas turbine system coupled with Rankine cycle. The effect of key parameters namely direct normal illuminance (DNI), concentration ratio, gas turbine inlet temperature, pressure ratio and ammonia concentration on the exergy destruction in key components has been investigated. Results indicate that exergy destruction in solar receiver and in gas turbine increases as DNI and concentration ratio rise. While the exergy destruction in heat exchanger decreases as DNI and concentration ratio rise.
Interleukin-13 reduces bone erosion in rheumatoid arthritis by up-regulating osteoprotegerin expression in fibroblast-like synoviocytes : an in vitro and in vivo study.

OBJECTIVES Bone erosion in rheumatoid arthritis (RA) is partly caused by excessive activation of osteoclasts. Osteoclasts can be derived from RA synovium and their differentiation can be inhibited by osteoprotegerin (OPG), a decoy receptor of the osteoclastogenesis-promoting cytokine receptor activator of nuclear factor κB ligand (RANKL). Fibroblast-like synoviocytes (FLSs) are the main stromal cells in the synovium that can secret OPG. The OPG secretion of FLSs can be modulated by various cytokines. Interleukin (IL)-13 can alleviate bone erosion in RA mouse models, but the mechanisms remain unclear. Therefore, we aimed to investigate whether IL-13 can induce OPG secretion by RA-FLSs, thus ameliorating bone destruction in RA by inhibiting osteoclast differentiation. METHODS OPG, RANKL, and IL-13 receptors expression by RA-FLSs were evaluated by RT-qPCR. OPG secretion was determined by ELISA. Western blot was performed to analyse OPG expression and the activation of the STAT6 pathway. IL-13 and (or) OPG siRNA pre-treated RA-FLSs conditioned medium were used in osteoclast induction to test if IL-13 can inhibit osteoclastogenesis by up-regulating OPG in RA-FLSs. Micro-CT and immunofluorescence were performed to determine if IL-13 can induce OPG expression and alleviate bone erosion in vivo. RESULTS IL-13 can promote OPG expression of RA-FLSs, and the promotion can be overcome by IL-13Rα1 or IL-13Rα2 siRNA transfection, or STAT6 inhibitor. Osteoclast differentiation can be inhibited by IL-13 pre-treated RA-FLSs conditioned medium. The inhibition can be reversed by OPG siRNA transfection. IL-13 injection can increase OPG expression in the joints while reducing bone destruction in collagen-induced arthritis mice. CONCLUSIONS IL-13 can inhibit osteoclastogenesis by up-regulating OPG in RA-FLSs through IL-13 receptors via the STAT6 pathway, thus may ameliorate bone erosion in RA.
An Improved Method for Identification of Mechanical Damage in an Isolated Transformer Winding Using FRA

It is known that the frequency response analysis (FRA) method is the most sensitive for identifying any fault or mechanical damage in transformer windings. In FRA technique, a subsequently measured response needs to be compared with a ‘reference measured response’ for detecting any faults. But in practice, sometimes ‘the reference measurement’ may not be available for some reasons; in that case the measured response alone is not sufficient for identifying any damage in the windings. In this way a limitation has been existed for the conventional FRA method. For addressing this limitation, the author’s research group recently published an innovative FRA based method for identifying the damage in single, isolated winding even in the absence of its’ healthy reference response. In this paper a new method is proposed to overcome few limitations or constraints of the earlier method proposed by this research group. The validity of this newly proposed method was verified using both the simulation results of an equivalent ladder-network model and the experimental results on a 33 kV continuous-disk winding. The pertinent results shows that the method is simple and promising for identifying the fault in the winding even in the absence of its’ healthy reference measurement.
Relationship between g-extra Connectivity and g-restricted Connectivity in Networks

The fault tolerance of a network can be measured by many parameters. Connectivity is a classic measurement parameter for evaluating the fault tolerance of a network. g-extra connectivity and g-restricted connectivity are generalizations of connectivity, which can better reflect the fault tolerance of a network. Specifically, the g-extra connectivity $\kappa_{g}(G)$ of a graph G is the minimum number of nodes whose removal will disconnect G, and each remaining component has no less than $g+1$ nodes. Furthermore, the g-restricted connectivity $\kappa^{g}(G)$ of G is the minimum number of nodes whose deletion results in a graph being disconnected and the minimum degree of each remaining component is at least g. In general, g-restricted connectivity is not equal to g-extra connectivity of a network. Therefore, many scholars often discuss g-restricted connectivity and g-extra connectivity with regard to different networks separately. In this paper, we show that g-restricted connectivity is equal to g-extra connectivity under some conditions. Then, the relationship we derived can be applied to some known networks such as the data center networks DCell and BCDC, multiprocessor network $(n,k)$-star. In addition, we construct a new network $H(G_{0},G_{1},G_{2};\mathbb{M})$ and prove that our result can be applied to it. In detail, we prove $\kappa^{g}(H(G_{0},G_{1},G_{2};\mathbb{M}))=\kappa_{g}(H(G_{0},G_{1},G_{2};\mathbb{M}))=n+g+1$ for any integers $n\geq 3$ and $ g\displaystyle \leq\lfloor\frac{n-2}{2}\rfloor$.
Characterization of 3D MEMS structural dynamics with a conformal multi-channel fiber optic heterodyne vibrometer

Insight into transient structural interactions, including coupled vibrations and modal non-degeneracy (mode splitting) is important to the development of current and next generation vibratory gyroscopes and MEMS resonators. Device optimization based on characterization of these effects is currently time consuming and limited by the requirement to perform spatially distributed measurements with existing single point sensors. In addition, the effects of interest and the diagnosis of their underlying causes and dependences are not readily revealed by traditional modal and finite element analyses. This paper, accordingly, discusses the design of a novel multi-channel fiber-optic heterodyne vibrometer which addresses this requirement directly. We describe a fiber-optic interferometer design which incorporates many standard fiber-optic telecommunications components, configured to support dynamic imaging of the real-time structural behavior of macro and micro vibratory resonators, including planar and 3D micro electromechanical systems (MEMS). The capabilities of the new sensor are illustrated by representative data obtained from a variety of 3D vibratory MEMS structures currently under development.
Computation of geometric partial differential equations and mean curvature flow

This review concerns the computation of curvature-dependent interface motion governed by geometric partial differential equations. The canonical problem of mean curvature flow is that of finding a surface which evolves so that, at every point on the surface, the normal velocity is given by the mean curvature. In recent years the interest in geometric PDEs involving curvature has burgeoned. Examples of applications are, amongst others, the motion of grain boundaries in alloys, phase transitions and image processing. The methods of analysis, discretization and numerical analysis depend on how the surface is represented. The simplest approach is when the surface is a graph over a base domain. This is an example of a sharp interface approach which, in the general parametric approach, involves seeking a parametrization of the surface over a base surface, such as a sphere. On the other hand an interface can be represented implicitly as a level surface of a function, and this idea gives rise to the so-called level set method. Another implicit approach is the phase field method, which approximates the interface by a zero level set of a phase field satisfying a PDE depending on a new parameter. Each approach has its own advantages and disadvantages. In the article we describe the mathematical formulations of these approaches and their discretizations. Algorithms are set out for each approach, convergence results are given and are supported by computational results and numerous graphical figures. Besides mean curvature flow, the topics of anisotropy and the higher order geometric PDEs for Willmore flow and surface diffusion are covered.
Near-Optimal Designs of Hybrid Precoding and Combining for Massive MIMO Systems From Lattice Decoding

Near-optimal designs consisting of a pair of finite resolution analog precoder and combiner and a baseband encoder for hybrid massive MIMO communication systems are proposed in this paper. Firstly, for any configuration of hybrid MIMO systems, several powerful upper bounds on the maximal achievable rates are presented and are used as guidelines for the proposed designs. Armed with the insights of upper bounds, designing the coefficients for finite resolution analog precoders and combiners is then regarded as a lattice decoding problem, where low-complexity lattice decoders and convex solvers are employed to yield optimal solutions with the best structure for partial connection. Simulation results show that the proposed design achieves within a negligible gap from the rate upper bound using phase shifters with low resolution, while reducing the number of required phase shifters by roughly 20% and significantly outperforming many existing designs at the same time.
Informational dynamics of censored observations

The analysis of stochastic models is often greatly complicated if there are censored observations of the random variables. This paper characterizes families of distributions which help keep tractable the analysis of such models. Our primary motivation is to provide guidance to practitioners in the selection of distributions: If a modeler feels that no member of the families we characterize is a reasonable approximation, then he will almost surely encounter serious analytic and computational problems if his data include censored observations. We characterize a family of distributions for which there exist fixed-dimensional sufficient statistics of purely censored observations. We also characterize an important subset of this family, appropriate for situations where data include both censored and exact observations. We derive the corresponding predictive distributions using arbitrary priors and present some general results relating stochastic dominance among predictive distributions to the parameters of the prior. We also analyze the cases of discrete and mixed random variables.
Biosynthesis, Characterization and Antioxidant Properties of ZnO Nanoparticles Using Punica Granatum Peel Extract as Reducing Agent

The green synthesis method of nanoparticles using plant extracts attracts great attention as a reliable, low-cost, sustainable, environmentally friendly protocol that prevents or minimizes waste generation. In this study, Punica granatum peel extract was used as the reducing plant material and zinc acetate dihydrate (Zn (CH3COO)2. 2H2O) solution was used as the starting metal. In the synthesis, optimum conditions were determined by UV visible spectroscopy using different metal ion concentrations, plant extract amount, temperature, and pH parameters. For characterization of ZnONPs synthesized at optimum conditions, Scanning Electron Microscopy (SEM), Energy Dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), Fourier Transform Infrared spectroscopy (FTIR), Dynamic Light Scattering (DLS), Zeta potential and Atomic Force Microscope (AFM) analyzes were made. It has been determined that the synthesized ZnONPs are spherical, have good stability, high purity, and nanoscale. The free radical scavenging capacity of biosynthesized ZnONPs was evaluated by DPPH analysis with different concentrations. The IC50 value was determined as 250µg ml-1.
Improved Water Strider Optimization with Deep Learning based Image Classification for Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) allows physicians to observe the digestive tract without doing surgery, at the cost of a huge volume of images should be analysed. The analysis and interpretation of WCE images will be a complicated task which needs computer aided decision (CAD) mechanism for assisting medical practitioner with the video screening and, lastly, with the diagnosis. Manual examination of WCE is a time taking process and can be benefitted from automatic detection by utilizing artificial intelligence (AI). Deep learning was a new method related to neural network. WCE was the criterion standard to identify small-bowel diseases. In this context, this study formulates an Improved Water Strider Optimization with Deep Learning Based Image Classification (IWSO-DLIC) for WCE. The presented IWSO-DLIC technique examines the WCE images for the identification of diseases. For image pre-processing, the IWSO-DLIC technique uses Wiener filtering (WF) approach. In addition, the IWSO-DLIC technique employs MobileNet feature extractor, and the hyperparameter tuning process takes place via the IWSO algorithm. Moreover, the IWSO algorithm is designed by the combination of oppositional based learning (OBL) concept with standard WSO algorithm. Finally, to classify WCE images, long short-term memory (LSTM) model is employed in this study. To demonstrate the enhanced performance of the IWSO-DLIC model, a series of simulations were performed. The simulation values stated the enhanced performance of the IWSO-DLIC technique over other recent models.