Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic detection of brain states using time varying functional connectivity from rs-frmri in noninvasive brain stimulation

Focal disturbances in the cerebral activity modulated by transcranial magnetic stimulation (TMS) produce alterations in brain connectivity at the global level. Those effects can be studied using time-varying functional connectivity (TVFC) based on functional magnetic resonance imaging at rest (rs-fMRI). The characteristics of these alterations could be modeled using machine learning algorithms for patient classification. This study used hidden Markov models (HMM) to evaluate temporal variations in functional connectivity after stimulation of two different brain areas (Frontal and Occipital). We modeled the dynamics of 15 resting-state networks in 12 states by calculating the fractional occupancy, mean lifetime, and interval time of each state. We then compared the difference between fMRI sessions, PRE and POST-stimulus, observing significant differences for both conditions, specially after frontal stimulation. This finding could be extended to the characterization of pathologies where local disturbances have a global impact in the functional connectivity, such as Epilepsy.
Comparative Performance Study of Hybrid Si/SiC Insulated-Gate Bipolar Transistors

This paper studies the performance of hybrid Si/SiC IGBTs, in comparison with conventional Si devices, in the prospect of future commercialization of all-SiC IGBTs. Both commercially available and custom-developed device models are evaluated, via LTspice® simulations, in a simple step-down DC-DC conversion application. For the simulation models of commercial, discrete, hybrid IGBTs, devices by Infineon Technologies® are considered, with voltage and current ratings of up to 650 V and 75 A, respectively. High-performance SiC Schottky diodes are also employed, as IGBT antiparallel diodes, in various scenarios. Simulations are carried out for various IGBT cases and operating conditions (i.e., temperature, switching frequency), whereas the device power losses (conduction and switching) are estimated for each case. Finally, simulation results highlight the minimized power losses and the superior performance of hybrid Si/SiC IGBTs, compared to standard Si devices.
Detection of fault location in branching power distribution network using deep learning algorithm

An immediate response is desired when any failure on overhead power distribution systems has occurred, and the TDR(Time Domain Reflecting) method has been introduced to detect fault detection locations. However, accurate detection of the fault point is difficult due to waveform distortion and decrease in amplitude of TDR pulse in complex networks with multiple branches and electric power distribution equipment such as transformers and switchgear. A method for detecting fault points from TDR waveforms using deep learning is proposed in this study. The proposed method can be applied to detect fault locations and fault types in branching power distribution networks where multiple reflected waves are observed. Since the deep learning algorithm requires a large amount of waveform data, we developed a fast simulation method to create the data. To simulate the circuit rapidly, the power distribution line was treated as a transmission line, thereby deriving the fundamental matrix of the transmission line. Additionally, the equivalent circuit model of the power distribution network was represented by cascading the fundamental matrix. The TDR waveform data was obtained rapidly by calculating the equivalent circuit using MATLAB. We used this to create many TDR waveform data of an overhead distribution network model with multiple branches and performed fault locations detection using a deep learning algorithm. As a result, it was shown that the location of accident was identified with 96.8% accuracy.
THE EFFECT OF GRAVITATION ON THE POLARIZATION STATE OF A LIGHT RAY

In the present work, detailed calculations have been carried out on the rotation of the polarization vector of an electromagnetic wave due to the presence of a gravitational field of a rotating body. This has been done using the general expression of Maxwell’s equation in curved spacetime. Considering the far-field approximation (i.e., the impact parameter is greater than the Schwarzschild radius and rotation parameter), the amount of rotation of the polarization vector as a function of impact parameter has been obtained for a rotating body (considering Kerr geometry). The present work shows that the rotation of the polarization vector cannot be observed in the case of Schwarzschild geometry. This work also calculates the rotational effect when considering prograde and retrograde orbits for the light ray. Although the present work demonstrates the effect of rotation of the polarization vector, it confirms that there would be no net polarization of an electromagnetic wave due to the curved spacetime geometry in a Kerr field.
Mechatronics Engineering Aspects of VHF band Antenna Design of Industry 4.0 Applications

The paper focuses on space & commercial aircrafts’ Very High Frequency (VHF) communication ‘Dipole- Fed Corner Reflector’ (DFCRA) antenna design with mechatronics engineering method & application in industry 4.0. It aims to collect relevant articles describing recent modern perspectives in antenna design for metric-wave Cyber-Physical system (CPS) applications. Areas of interest for this issue include but are not limited to mechatronics engineering synergy of antenna design techniques such as weight reduction, size optimization and arrays characteristics optimization.
Nonlinear Model Predictive Control for Longitudinal and Lateral Dynamic of Autonomous Car

Autonomous cars, when two essential variables influence tracks to determine their stability, namely position control and speed control. Speed is a control variable in longitudinal motion because it controls car movement along the X axis. Steering is a control variable in the lateral control system because it controls car movement along the Y axis. Some of the studies before, most of them only paid attention to one variable between speed and steering, and other variables are considered constant. Therefore, in this study, multivariable control was carried out using Nonlinear Model Predictive Control (NMPC) to determine the speed and steering of the car. Nonlinear Model Predictive Control (NMPC) predicts control inputs in the next few steps. The use of the predicted value results is to minimize the execution time required by the car control system. Because the system's complexity in autonomous cars is high, thus it is better to process data faster. The NMPC process produces a car's steering angle and speed value that must be achieved when tracking on a predetermined track. The results showed that the proposed control strategy could precisely set the vehicle to follow the target's trajectory.
Double-peaked ion spectra in the lobe plasma - Evidence for massive ions. [in geomagnetic tail]

Analysis of the data returned from the three Rice lunar surface Suprathermal Ion Detector Experiments (Side) has shown that the boundary layer and plasma mantle observed near the earth extend to the lunar orbit as an extensive region of antisunward plasma flow occupying a major portion of the lobes of the geomagnetic tail. At lunar distances we call this the lobe plasma. The lobe plasma appears as a narrow peak in the ion energy spectrum usually around 100 eV. More recent analysis of Side data has revealed the occasional occurrence of a second peak in the ion spectrum during geomagnetically active periods. This peak is similar to a secondary peak reported by Frank et al. [1977] in the boundary layer energy spectrum. This secondary peak occurs at higher energy than the principal peak, generally in the range 1–2.75 keV. Both spectra are narrow, the upper peak frequently being seen in one or two channels of the analyzer. The inferred temperature for both peaks is usually less than kTi ∼ 10 eV in the look direction of the instrument. The differential ion flux for the secondary peak is estimated to be generally less than 3% of that for the primary peak. On the assumption that both peaks arise from flowing protons we find that the ratio of the bulk velocity of the secondary peak to that of the primary peak ranges between 4.4 and 5.4. We suggest two hypotheses for the origin of these ions. Either (1) they consist of two streams of protons traveling with different bulk velocities and therefore originating from two different source regions, or (2) they are two streams traveling with approximately the same bulk velocity but with the primary peak composed of protons and the secondary peak composed of more massive ions. By using the second hypothesis (same bulk velocity) the Side data can be fitted well if a lunar surface potential between +35 and +91 V is assumed and if the secondary peak ions have a mass of 14 or 16 and unit charge. The most likely candidate is ionospheric O+. On the assumption that the the secondary peak is O+, by fitting to a convected Maxwellian we obtain ion number densities between 5 × 10−5 and 10−3/cm³. These numbers should be treated with caution because of the narrowness of the ion angular and energy distribution. In both hypotheses an ionospheric source is postulated, and an energization process is needed to account for the observed ions.
Three-algebras in N=5, 6 superconformal Chern-Simons theories: Representations and relations

In this work we present 3-algebraic constructions and representations for three-dimensional N=5 supersymmetric Chern-Simons theories, and show how they relate to theories with additional supersymmetries. The N=5 structure constants give theories with Sp(2N)xSO(M) gauge symmetry, as well as more exotic symmetries known from gauged supergravity. We find explicit lifts from N=6 to 8, and N=5 to 6 and 8, for appropriate gauge groups.
Bit-level Malicious Traffic Detection Based on Markov Images and Deep Learning

In recent years, with the rapid development of network services, network malicious attacks have become a serious security threat, causing huge economic losses. How to quickly and accurately detect and identify malicious network traffic is critical to network security. Since traditional machine learning-based methods are limited by feature engineering, malicious traffic grayscale maps and deep learning-based methods become effective solutions. However, converting the original traffic into grayscale images leads to information loss, which affects the detection effect. To solve this problem, this paper provides a malicious traffic i dentification me thod ba sed on Ma rkov feature images and deep learning, called MKCNN. This method regards the application layer data of the network session as a Markov chain, extracts the Markov probability transition matrix and converts it into a Markov image, and then uses a convolutional neural network to identify the Markov image. Experiments are performed on the publicly available USTC-TFC2016 dataset. The experimental results show that the method can detect malicious traffic w ith a n a ccuracy o f 9 9.99%, a nd c ompared w ith the existing methods, the method based on Markov feature images proposed in this paper has obvious advantages in detection accuracy.
Healbot: NLP-based Health Care Assistant for Global Pandemics

Since it was detected, coronavirus (also known as COVID-19) has become a worldwide epidemic. The surge in patients has made it challenging for hospitals and medical professionals to keep up due to the increasing number of recorded incidents. When the pandemic starts, it is getting really hard to visit a medical specialist, even in more remote areas. According to the Johns Hopkins university’s Covid dashboard, approximately 220 million Covid cases were reported worldwide [2]. According to government hospital reports, 666,086 cases were found in Sri Lanka [3]. It’s a massive amount to handle for the health sector and the country. Consequently, there are many deaths reported every day as a result of the challenges in inpatient care. Because all patients are treated in their homes, this must be done efficiently. Only the most urgent cases are being treated in hospitals. Hospitals and quarantine centers are overcrowded. People in remote areas are also trying to treat the disease without knowing anything about it because they have limited access to information. This is because it needs a Chatbot to help with diagnosing Covid symptoms at home, and to assist patients in finding the right treatment options. An artificial intelligence (AI) Chatbot has been developed with the goal of diagnosing COVID-19 exposure and advising rapid remedies. As part of this analysis, relevant past research was reviewed to establish the best reliable approach for predicting COVID-19 in people. There was an integration of Logistic Regression, Decision Trees, and Random Forests to develop the model. The model was trained with the clinical data taken from the COVID-19 patients and machine learning models are evaluated to see how accurate they are. It was determined how accurate the algorithms were. The patients who were infected with COVID-19 were examined by using implemented prototype to predict the severity level and the trained model makes use of RASA Framework, FastAPI, and MongoDB for the purpose of making predictions. The accuracy of the trained model and random sample tests seems similar. So, the prototype’s efficacy is perfectly matched. In rural areas, it can help patients by providing them with proper advice on how to treat, what preventive measures are, and what nearby health services are. It can also help patients by reducing the psychological damage that may be caused by their isolation from other people.
Parallel Two-Stage Multiple Hypotheses Testing Model of Track-Before-Detect

Multiple hypotheses testing (MHT)-based Track-before-Detect (TBD) method is a widely known algorithm to detect infrared dim small target. However, the hypotheses space in this kind of method is so big that the computation cost and storage requirement inevitably increase, resulting hardly realized in hardware. To solve the problem, a parallel two stage multiple hypotheses testing model is proposed. First, a parallel MHT model is designed to save the possible trajectories. By using the proposed MHT, all trajectories can be parallelly processed and the trajectories search process is simplified, greatly reducing computation cost. Secondly, for each pixel on the current image, the trajectories search range is divided into 4 different areas, and two-stage MHT is performed in each area to obtain the accumulation energy. By using two-stage strategy, the trajectories number is reduced exponentially, greatly saving storage space. Simulations experimental results show the superiority of the proposed parallel two stage MHT in terms of detection probability and computational complexity under low signal-to-noise ratio (SNR) condition. It can detect the infrared point target when SNR is 1.5 with a detection probability of 90.6% and a false alarm rate of 0.1%.
Implementation of one-dimensional quantum walks on spin-orbital angular momentum space of photons

Photons can carry spin angular momentum (SAM) and orbital angular momentum (OAM), which can be used to realize a qubit system and a high-dimension system, respectively. This spin-orbital system is very suitable for implementing one-dimensional discrete-time quantum random walks. We propose a simple scheme of quantum walks on the spin-orbital angular momentum space of photons, where photons walk on the infinite OAM space controlled by their SAM. By employing the recent invention of an optical device, the so-called ``$q$-plate,'' our scheme is more simple and efficient than others because there is no Mach-Zehnder interferometer in the scheme.
Variable Stiffness Soft Actuator using Layer Jamming for Food Handling

This study proposes a novel hybrid actuator combining PneuNet with layer jamming actuation to address the problems with conventional soft pneumatic actuators such as stability and lack of integrated locking mechanisms. The introduction of sandpaper-based layer jamming element is made as a solution. A set of experiments were conducted to identify suitable sandpaper grit-level and possible layering arrangement for the jamming section. P220 grit sandpaper showed the best flexural strength change which was suitable for the jamming element. The hybrid actuator was tested to identify the deviation from the typical PneuNet actuator in the performance of bending angle and generated tip force. The novel actuator showed a 25% reduction in bending angle while producing a 30° maximum bending angle at 160 kPa pressure. Tip force of the novel actuator didn’t show a significant difference and it produced 2.3 N force at 160 kPa. The integrated jamming element can act as a locking mechanism for the novel actuator. Due to the relaxation of the PneuNet, actuating locking mechanism shows an average 30% bending angle reduction compared to the initial bending angle. A two-finger gripper was produced using the hybrid actuator and it was successfully tested on soft food handling with the integrated locking feature.
A Novel Identification Method of Helmet Wearing Based on Human Pose Estimation

Safety helmet is one of the most widely used and important head safety protection equipment for laborers, widely used in machinery, construction, mines, transportation, metallurgy, electric power and other industries. However, due to the lack of safety awareness, some laborers often don’t wear safety helmets correctly. Therefore, how to correctly identify operators who are not wearing safety helmets becomes very necessary. The existing helmet wearing recognition method has low recognition accuracy in complex posture and complex background, and it is easy to misjudge wearing other hats as wearing helmets. In this paper, an improved helmet recognition method is proposed to try to solve the above problems. First, for each image of human body, the joint information of the human body is obtained by human pose estimation, and then the subimages of his head-neck are cropped from the aforementioned human body image using an improved three-point localization method, and finally, a classification network is designed to classify all the obtained head-neck subimages to determine whether the laborer wears a helmet or not. The experimental results show that the accuracy of the helmet recognition method proposed in this paper can reach 99.0%, which is significantly higher than the comparison methods, and has strong robustness in complex environments.
Structure of sunspot penumbral filaments: a remarkable uniformity of properties

Context. The sunspot penumbra comprises numerous thin, radially elongated filaments that are central for heat transport within the penumbra, but whose structure is still not clear. Aims. We aim to investigate the fine-scale structure of these penumbral filaments. Methods. We perform a depth-dependent inversion of spectropolarimetric data of a sunspot very close to solar disk center obtained by Solar Optical Telescope/Spectropolarimeter onboard the Hinode spacecraft. We have used a recently developed, spatially coupled 2D inversion scheme, which allows us to analyze the fine structure of individual penumbral filaments up to the diffraction limit of the telescope. Results. Filaments of different sizes in all parts of the penumbra display very similar magnetic field strengths, inclinations, and velocity patterns. The temperature structure is also similar, although the filaments in the inner penumbra have cooler tails than those in the outer penumbra. The similarities allowed us to average all these filaments and to subsequently extract the physical properties common to all of them. This average filament shows upflows associated with an upward-pointing field at its inner, umbral end (head) and along its axis, as well as downflows along the lateral edge and strong downflows in the outer end (tail) associated with a nearly vertical, strong, and downward-pointing field. The upflowing plasma is significantly, i.e., up to 800 K, hotter than the downflowing plasma. The hot, tear-shaped head of the averaged filament can be associated with a penumbral grain. The central part of the filament shows nearly horizontal fields with strengths in the range of 1 kG. The field above the filament converges, whereas a diverging trend is seen in the deepest layers near the head of the filament. The fluctuations in the physical parameters along and across the filament increase rapidly with depth. Conclusions. We put forward a unified observational picture of a sunspot penumbral filament. It is consistent with such a filament being a magneto-convective cell, in line with recent magnetohydrodynamic simulations. The uniformity of its properties over the penumbra sets constraints on penumbral models and simulations. The complex and inhomogeneous structure of the filament provides a natural explanation for a number of long-running controversies in the literature.
Designing an Android-based mobile app to address issues with online shopping

Recently, most companies have develop mobile applications since phones are the main way to use the Internet rather than computers. Based on the evidence, the year 2021 witnessed a huge increase in the number of online purchases, especially during the corona pandemic and other diseases that emerged after it. This research, therefore, aimed to develop a mobile application that can provide ease of shopping and help its users to take advantage of discount prices across their region, especially during the COVID-19 lockdown. ‘Discount Guide’ was a great solution during this time and forever. This application is designed to allow the seller and the buyer as well to sell or buy discounted items. This app was built using the Java language, Android studio, Gradle and JUI to design the app effectively and more easily. As a result, the customer gets the benefit from using the app for online shopping on their Android devices. Keywords: Android, application, mobile, MySQL, online shopping, smartphone;
Exploring fundamental physics with neutron stars

In this lecture, we give a first introduction to neutron stars, based on fundamental physical principles. After outlining their outstanding macroscopic properties, as obtained from observations, we infer the extreme conditions of matter in their interiors. We then describe two crucial physical phenomena which characterize compact stars, namely the gravitational stability of strongly degenerate matter and the neutronization of nuclear matter with increasing density, and explain how the formation and properties of neutron stars are a direct consequence of the extreme compression of matter under strong gravity. Finally, we describe how multi-wavelength observations of different external macroscopic features (e.g. maximum mass, surface temperature, pulsar glitches) can give invaluable information about the exotic internal microscopic scenario: super-dense, isospin-asymmetric, superfluid, bulk hadronic matter (probably deconfined in the most central regions) which can be found nowhere else in the Universe. Indeed, neutrons stars represent a unique probe to study the properties of the low-temperature, high-density sector of the QCD phase diagram. Moreover, binary systems of compact stars allow to make extremely precise measurements of the properties of curved space-time in the strong field regime, as well as being efficient sources of gravitational waves.
Distillation of logic-qubit entanglement assisted with cross-Kerr nonlinearity

Logic-qubit entanglement has attracted much attention in both quantum communication and quantum computation. Here, we present an efficient protocol to distill the logic-qubit entanglement with the help of cross-Kerr nonlinearity. This protocol not only can purify the logic bit-flip error and logic phase-flip error, but also can correct the physical bit-flip error completely. We use cross-Kerr nonlinearity to construct quantum nondemolition detectors. Our distillation protocol for logic-qubit entanglement may be useful for the practical applications in quantum information, especially in long-distance quantum communication.