Miracle-dz/UniFAR-FTUs huggingface dataset:
Black holes, star clusters, and naked singularities: numerical solution of Einstein’s equations

We describe a new method for the numerical solution of Einstein’s equations for the dynamical evolution of a collisionless gas of particles in general relativity. The gravitational field can be arbitrarily strong and particle velocities can approach the speed of light. The computational method uses the tools of numerical relativity and N-body particle simulation to follow the full nonlinear behaviour of these systems. Specifically, we solve the Vlasov equation in general relativity by particle simulation. The gravitational field is integrated by using the 3 + 1 formalism of Arnowitt, Deser and Misner. Physical applications include the stability of relativistic star clusters the binding energy criterion for stability, and the collapse of star clusters to black holes. Astrophysical issues addressed include the possible origin of quasars and active galactic nuclei via the collapse of dense star clusters to supermassive black holes. The method described here also provides a new tool for studying the cosmic censorship hypothesis and the possibility of naked singularities. The formation of a naked singularity during the collapse of a finite object would pose a serious difficulty for the theory of general relativity. The hoop conjecture suggests that this possibility will never happen provided the object is sufficiently compact (≤M) in all of its spatial dimensions. But what about the collapse of a long, non-rotating, prolate object to a thin spindle? Such collapse leads to a strong singularity in newtonian gravitation. Using our numerical code to evolve collisionless gas spheroids in full general relativity, we find that in all cases the spheroids collapse to singularities. When the spheroids are sufficiently compact the singularities are hidden inside black holes. However, when the spheroids are sufficiently large there are no apparent horizons. These results lend support to the hoop conjecture and appear to demonstrate that naked singularities can form in asymptotically flat space-times.
TOWARD GLOBAL DROUGHT EARLY WARNING CAPABILITY Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting

Drought is a global problem that has far-reaching impacts, especially on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF), and the recent progress made toward its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional, and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach.
Thermodynamic Reasons for Perception--Action Cycles

An argument is developed to show that the origin and evolution of the perceptual guidance of movements and the movement enhancement of opportunities to perceive, that is, perception-action cycles, have a direct and deep connection with thermodynamic principles. The cornerstones of the argument are: (a) maximum entropy production as a physical selection principle (thermodynamic fields will behave in such a fashion as to get to the final state-minimize the field potential or maximize the entropy-at the fastest possible rate given the constraints); (b) the inexorability of order production (order production is inexorable because order produces entropy faster than disorder); (c) evolution as a global phenomenon (the Earth system at its highest level evolves as a single global entity); and (d) information in Gibson's law-based, specificational sense (invariant relations exist between higher order properties of structured energy distributions and their environmental sources). In the coordination of self-organiz.
COMMISSION 30: RADIAL VELOCITIES

The past three-year period has seen steady efforts to collect large numbers of radial-velocity (RV) measurements, as well as important applications of radial velocities to astrophysics. Improvements in precision continue to be driven largely by exoplanet research. A workshop entitled “Astronomy of Exoplanets with Precise Radial Velocities” took place in August of 2010 at Penn State University (USA), and was attended by some 100 researchers from around the world. The meeting included thorough discussions of the current capabilities and future potential of the radial velocity technique, as well as data analysis algorithms to improve precision at visible and near-infrared wavelengths.
Broadside radiation from periodic leaky-wave antennas

A double-strip grating leaky-wave antenna consisting of two strips per unit cell is analyzed. The stopband behavior exhibited at broadside scan in the single-strip grating antenna is characteristic of all periodic leaky-wave antennas having a single strip per unit cell, and results in a drastic increase in the attenuation rate of the leaky wave as the beam is scanned to broadside. By nearly eliminating this stopband behavior, the double-strip leaky-wave antenna can scan from backward end fire to forward end fire without any large frequency regions of high attenuation. An approximate design rule for the double-strip antenna is discussed, and results are presented to show how the antenna may be further optimized to achieve the minimum possible variation in attenuation as the beam is scanned through broadside. Although the stopband behavior is never completely eliminated with the addition of the extra strip, the optimum design shows an almost negligible region of rapidly varying attenuation near broadside. >
Frequency Filter for Time Integrations

Abstract A simple filter for controlling high-frequency computational and physical modes arising in time integrations is proposed. A linear analysis of the filter with leapfrog, implicit, and semi-implicit, differences is made. The filter very quickly removes the computational mode and is also very useful in damping high-frequency physical waves. The stability of the leapfrog scheme is adversely affected when a large filter parameter is used, but the analysis shows that the use of centered differences with frequency filter is still more advantageous than the use of the Euler-backward method. An example of the use of the filter in an actual forecast with the meteorological equations is shown.
Noninvasive Measurement of Particle-Settling Velocity and Comparison with Stokes’ Law

AbstractAn image-analysis technique was applied for noninvasive measurements of settling rate of polystyrene latex spheres of 6, 10, 20, 50, 100, and 160 μm diameters in a quiescent environment. A CCD (charged couple device) camera and strobe light were used to capture in situ images of the particles, which were analyzed using image-analysis software. To overcome the challenge of noninvasive measurement of very slow-moving particles, variable pixel resolutions were chosen depending on settling speed so that the separation of the particle images in a given camera exposure time was sufficient to determine the temporal displacements of the particles with precision. Particle-settling behavior was not disrupted for any kind of sample handling, and measurements were made without hindering particle settling and allowing particle movement under gravity alone. Measurements were compared with settling rates calculated with Stokes’ law, and the validity of Stokes’ law under low particle Reynolds number (R<0.1) was i.
An Ultra-Low Power (86 nW) Low-Voltage (0.6 V) Self-Biased Instrumentation Amplifier for Bio-Medical Applications

In this article, an operational transconductance amplifier (OTA)-based self-biased, ultra-low power instrumentation amplifier is proposed. The design is performed in SCL 0.18 μm technology under 0.6 V power supply. The total current drawn from the supply is 144 nA, resulting in ultra-low power consumption of 86 nW. The entire circuit is self-biased by an in-built current reference circuit. The instrumentation amplifier produces a fixed gain of 61 dB with unity gain bandwidth of 4.1 KHz and input RMS noise voltage of 2.01 μVrms. The amplifier is aimed for bio-medical applications and is capable of amplifying a signal as low as 0.5 μV with an ultra-low frequency of 1 Hz also, serving the range of even the weakest possible bio-potential signal quite efficiently.
Medical Diagnosis and Identification of Covid -19 by Intelligent IoT System and Resnet 18 Bilinear Deep Greedy Network

An international health crisis has been caused by the widespread COVID-19 epidemic. COVID-19 patient diagnoses are made using deep learning, although this necessitates a massive radiography data collection in order to efficiently deliver an optimum result. This paper presents a novel Intelligent System with IoT sensors for covid 19 and "Bilinear Resnet 18 Deep Greedy Network," which is effective with a limited amount of datasets. Despite peculiarities brought on by a small dataset, the suggested approach could successfully combat the anomalies of over fitting and under fitting. The suggested architecture ensures a successful conclusion when the trained model is correctly evaluated using the provided X-ray datasets of COVID-19 cases. The recommended model offers accuracy of 97%, which is superior to existing methodologies. Better precision, recall, and F1 score are provided; which are 98%, 96%, and 96.94% respectively, which is better than other existing methodology.
Unipolar resistive switching in insulating niobium oxide film and probing electroforming induced metallic components

We have observed unipolar-type resistance switching in an ultrathin niobium oxide film. An analysis of the temperature dependence of the resistance switching transport revealed that low-resistance state showed a type of electrical conduction typically observed in metals. The modification in chemical binding states of the film in different resistance states was studied using x-ray photoelectron spectroscopy. The analysis of XPS showed that metallic suboxides NbOδ (δ ≪ 2), decomposed from some of Nb2O5 and NbO2 components of the film, were created after electroforming process, suggesting that the metallic suboxides are constituting elements of metallic channels in the low resistance state.
A Lightweight Image Decolorization Approach based on Contrast Preservation

Nowadays, color-to-gray conversion known as decolorization is widely used not only in image processing but also in machine learning and deep learning-based applications, due to its lower complexity. In the conventional decolorization process, in general, fixed conversion coefficients are used for whole images. Using fixed coefficients, namely weighting parameters, for the whole images may lead to deterioration of the quality of original color images. On the other hand, using unfixed conversion coefficients, which depend on the contents of the images, demonstrates better decolorization performance with respect to the fixed coefficients. The critical points in the decolorization process are to preserve spatial information, such as contrast preserving, and to have less computational complexity. In this study, a very fast decolorization method is proposed without sacrificing contrast preservation. The proposed method exploits the gradient-based correlation similarity approach, in which global and local contrast information is considered for a total of 66 distinct weighting coefficients. Under the CADIK dataset, COLOR250 dataset and high resolution images, these weighting coefficients are reduced by using the frequency of best weighting coefficients. Experimental results show that the proposed method can be used in real-time without compromising the image decolorization performance.
Reconfiguration Strategy of Distribution Network Based on Hatchable Particle Swarm Optimization

With the development of automation, distribution network reconfiguration is aiming at bringing economy and reliability to the network. Particle swarm optimization is a kind of intelligent algorithm used to deal with network reconfiguration. But it has the defect which is easy to fall into the local optimum. In order to improve the defect, this paper proposes the hatchable particle swarm optimization. First, the mathematical model of distribution network reconfiguration is established. Then, this paper adds the hatching idea of oviparous animals in nature into the algorithm, so that the algorithm gains the ability to hatch more individuals which have great chance to have greater fitnesses. Next, this paper adopts dynamic parameters of the algorithm instead of static ones. Due to these improvements, the global searching ability of the particle swarm is strengthened, and the solving speed of the algorithm has been enhanced. Finally, the advantages of the new algorithm are verified by simulation of IEEE33 nodes distribution network system in both ordinary and failure recovery situations, which show the greater optimization ability and convergence performance of hatchable particle swarm optimization.
Detecting Misogynia in Italian Tweets: A Linguistic and Contextual based Method

Social media provides a platform for people to freely share their lives and feelings. However, with the increase of people's use of social media, the dark side of social media has gradually attracted people's attention. The anonymity of social media has led many people to use it to harass, abuse, and attack others, with women being a common target group. Twitter, one of the world's most well-known social media platforms, is used by hundreds of millions of people worldwide to communicate and share information every day. Although the artificial intelligence system for detecting hate comments has been applied to the maintenance of social media, it is still difficult to distinguish hate speech against women. That is because misogynistic comments are often subtle, and the currently commonly applied detection methods do not sufficiently consider the contextual information of the text. Therefore, we propose a BERT-based misogyny detection method that can fully integrate contextual information and context to classify texts. The experimental results show that our method has significant improvement compared with other methods.
Design of a Highly Directive, Wideband and Compact Endfire Antenna Array for 5G Applications

Antenna array implementation becomes a necessary requirement for millimeter wave (mmW) 5G applications for improving the directivity to overcome the limitations of high attenuation losses at mmW frequencies. In this work, a compact 1X4 linear endfire antenna array has been designed using a full corporate feeding network for operation at 28 GHz frequency. The antenna element in the proposed array is a modified antipodal Vivaldi antenna, which has been designed by incorporating corrugations and metal director to achieve the size reduction and improve the radiation parameters. This compact antenna array has dimensions of 35.3 X 36.6 mm, peak gain of 13.67 dBi, and wide impedance bandwidth in the range of 24.3 GHz to 34 GHz, making it ideal for implementation in 5G cellular applications.
The evolution of disc galaxies

We briefly describe the physical processes that are included in our semi–analytic model of hierarchical galaxy formation. We review some of the low redshift properties of one such model constructed assuming a ∧CDM cosmology. We examine the evolutionary paths of typical bright disc galaxies in this model. This case study serves to illustrate the generic features of galaxy evolution in hierarchical models. It is demonstrated that the richness of galaxy evolution in this model is hidden when one looks at the evolution of global properties such as the galaxy luminosity function. We also quantify a generic prediction of hierarchical galaxy formation, namely that galaxies were physically smaller in the past. This model is consistent with recent high redshift observations and the reported evolution of galaxy sizes lends general support to hierarchical galaxy formation.
Study of the influence of parameters to explore the Properties of Graphene Nanoribbon Field Effect Transistors

Graphene based devices are impelling the innovations for the final implementation of flexible electronic devices within new perspectives of the future technological developments. Specially, GNRFET has placed in a superior position due to the existence of finite bandgap and the ability to tune the same by a simple way (of varying the width). Therefore, we have explored the properties of GNRFET by using some analytic approach with a view to assess its efficacy as a Field Effect Transistor (FET). Reduction of a 2-dimensional (2D) Poisson equation to a one-dimensional (1D) form in an effective manner is the main of focus of this method. The approach was used to study the influence of geometrical and electrical parameters on I-V characteristics of the GNRFET. The results of this work pave the way for the possibilities for device engineers to characterize GNRFETs based on the requirements of a specific device application. The resulting convergence of the inflection point is an interesting feature of our study.The existence of a critical voltage, beyond which the current rises in its value with temperature, is an pre-eminent feature of our GNRFET model.
EEG-Based Brain Computer Interface for Emotion Recognition

Emotion recognition using electroencephalography (EEG) signal could be a current focus in brain-computer interface research, that is convenient and a reliable technique. EEG-based emotion detection studies are employed in a very spread of fields, including defence, aerospace, and medicine, among others. The purpose of this study is to discover the relationship between EEG signals and human emotions. EEG signals are commonly used to categorise emotions into three groups: positive, negative, and neutral. We first extracted features from the EEG signals in order to classify emotions and used a deep learning classifier: recurrent neural network (RNN) and gated recurrent unit (GRU). Second, a Muse EEG headband with four electrodes (TP9, AF7, AF8, TP10) is used to record brain activity. Positive and negative emotional states are elicited with lucid valence film clips, and neutral resting data with no stimuli is also recorded for one minute per session. EEG data was collected for 3 minutes per state from two people (one male and one female) (positive, neutral, and negative) [5]. This study helps to spot human emotions supported by EEG signals within the brain-computer interface and helps to know the emotion of the mind.
Investigation of the Efficiency of CsGeI3-based solar cell using SCAPS-1D modeling and simulation

This paper proposes a fully lead (Pb)-free CsGeI<inf>3</inf>-based Perovskite Solar Cell (PSC) modeled and simulated using Solar cell Capacitance Simulator-1D (SCAPS-1D). With this heterojunction structure, an improved power conversion efficiency (PCE) of about 19.30% has been obtained. PCBM and P3HT are used as electron transfer layer (ETL) and hole transfer layer (HTL) respectively. The effect of change of acceptor density and defect density with the absorber layer thickness has been studied theoretically in this paper. The optimized thickness of the absorber layer is found out to be 1.4 and best values of photovoltaic parameters are obtained for an acceptor density of 5×10<sup>17</sup> cm<sup>-3</sup> and defect density of 10<sup>14</sup> cm<sup>-3</sup>. The final photovoltaic performance for the solar cell structure of glass/FTO/PCBM/CsGeI<inf>3</inf>/P3HT/Ag are as follow: J<inf>SC</inf> = 25 mA/cm<sup>2</sup>, V<inf>OC</inf> = 1.060 V, PCE = 19.30%, FF = 87.40%.
Thermal Transport in High Porosity Cellular Metal Foams

The heat dissipation capability of highly porous cellular metal foams with open cells subject to forced air convection is studied using a combined experimental and analytical approach. The cellular morphologies of six FeCrAlY (an iron-based alloy) foams and six copper alloy foams with a range of pore sizes and porosities are quantified with the scanning electronic microscope and image analysis. Experimental measurements on pressure drop and heat transfer for copper foams are carried out. A numerical model for forced convection across opencelled metal foams is subsequently developed, and the predictions are compared with those measured. Reasonably good agreement with test data is obtained, given the complexity of the cellular foam morphology and the associated momentum/energy transport. The results show that cell size has a more significant effect on the overall heat transfer than porosity. An optimal porosity is obtained based on the balance between pressure drop and overall heat transfer, which decreases as the Reynolds number is increased. Nomenclature ˜ a = specific surface area per unit volume C f = heat capacity of fluid C I = inertial coefficient d f = diameter of cell edge ligament d p = pore size FI = inertial variable f = friction factor H = channel height h sf = interfacial heat transfer coefficient
Low-frequency noise in diamond solution-gated field effect transistors

Electronic noise is investigated in diamond solution-gated field effect transistors. They exhibit 1/f-type noise, which is evaluated according to Hooge’s empirical relation. Correcting for the contribution of access regions, the Hooge parameter is found to be inversely proportional to the carrier density, suggesting that the noise originates from fluctuations in the number of charge carriers. Trapping and detrapping of charge carriers at dislocations of the diamond crystal is considered the main source of noise. An effective gate voltage noise of 16 μV rms is observed, which is comparable to the noise of similar devices based on Si and GaN.
Detection of Microaneurysms Using Gabor Walsh-Hadamard Transform and Deep Autoencoder

An essential step in preventing the loss of vision and blindness followed by diabetic retinopathy is the identification of red lesions in retinal imaging (DR). Microaneurysms (MAs) are often been detected and are the primary reason for lesions and lead to DR. Hence, detection of MAs is essential for monitoring DR. Due to the low contrast fundus image and the wide range of imaging settings, it can be difficult to identify these lesions. In this research, we concentrate on creating automated approaches to handle the challenge of MAs identification intelligently. Several potential microaneurysm candidates are retrieved in the first stage of the proposed technique utilizing a gradient weighting methodology and an iterative thresholding strategy. For each candidate, a new set of characteristic features on the basis of the Gabor Walsh-Hadamard Transform (GWHT) is retrieved in the following stage along with intensity and shape parameters. To differentiate the MA from non-MA candidates, the combined array of characteristic features is then given to a deep autoencoder neural network (DAENN) classifier. On three datasets that are openly accessible, the proposed approach was tested. Specificity, FROC, and Sensitivity analysis were used to assess the efficiency of the identification process. Additionally, the proposed methodology from lesion-based analysis identified the MAs with 95.7% of sensitivity and an average of 7 false-positives for every image. These findings favorably compare with the leading results that have been made public to date.
Finite Size Scaling in Ecology

Finite size scaling techniques are applied to ecology and are shown to predict relationships between power-law exponents as well as a connection between scaling phenomena in physics and ecology. The framework presented here ought to be useful for testing the hypothesis of scale invariance in the spatial distribution of species across a landscape. PACS numbers: 87.23. Cc