Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanically Robust and Flexible GO/PI Hybrid Aerogels as Highly Efficient Oil Absorbents

Herein, mechanically robust and flexible graphene oxide/polyimide (GO/PI) hybrid aerogels (GIAs) were fabricated by a facile method, in which the mixed suspensions of the water-soluble polyimide precursor and graphene oxide (GO) sheets were freeze-dried, which was followed by a routine thermal imidation process. The porous GIAs obtained not only exhibit excellent elasticity and extremely low density values (from 33.3 to 38.9 mg.cm−3), but they also possess a superior compressive strength (121.7 KPa). The GIAs could support a weight of up to 31,250 times of its own weight, and such a weight-carrying capacity is much higher than that of other typical carbon-based aerogels. Having such a porous structure, and high strength and toughness properties make GIAs ideal candidates for oil spill cleanup materials. The oil/organic solvents’ absorption capacity ranges from 14.6 to 85, which is higher than that of most other aerogels (sponges). With their broad temperature tolerance and acidic stability, the unique multifunctional GIAs are expected to further extend their application range into extreme environments.
Development and characterization of 6-gap bakelite Multi-gap Resistive Plate Chamber

The Multi-gap Resistive Plate Chamber (MRPC) is an advanced form of Resistive Plate Chamber (RPC) detector where the gas gap is divided into sub-gaps. MRPCs are known for their good time resolution and detection efficiency for charged particles. They have found suitable applications in several high energy physics experiments like ALICE in LHC, CERN, Geneva, Switzerland and STAR in RHIC, BNL, U.S.A. As they have very good time resolution and are of low cost, they can be a suitable replacement for very expensive scintillators used in Positron Emission Tomography Imaging. The MRPCs that are being used nowadays are developed with glass electrodes. We have made an attempt to develop a 6-gap MRPC using bakelite electrodes. The outer electrodes are of dimensions 15 cm × 15 cm × 0.3 cm and the inner electrodes are of dimension 14 cm × 14 cm × 0.05 cm. The glossy finished electrode surfaces have not been treated with any oil like linseed, silicone for smoothness. The performance of the detector has been studied measuring the efficiency, noise rate and time resolution with cosmic rays. This effort is towards the development of a prototype for Positron Emission Tomography with the Time-Of-Flight technique using MRPCs. Details of the development procedure and performance studies have been presented here.
Electromagnetic wave propagation in an almost circular bundle of closely packed metallic carbon nanotubes

An equivalent-multishell approach for the approximate calculation of the characteristics of electromagnetic waves propagating in almost circular (azimuthally symmetric), closely packed bundles of parallel, identical, and metallic carbon nanotubes (CNTs) yields results in reasonably good agreement with a many-body technique, for infinitely long bundles when the number of CNTs is moderately high. The slow-wave coefficients for azimunthally symmetric guided waves increase with the number of metallic CNTs in the bundle, tending for thick bundles to unity, which is characteristic of macroscopic metallic wires. The existence of an azimuthally nonsymmetric guided wave at low frequencies in a bundle of a large number of finite-length CNTs stands in contrast to the characteristics of guided-wave propagation in a single CNT. The equivalent-multishell approach yields the polarizability scalar and the antenna efficiency of a bundle of finite-length CNTs in the long-wavelength regime over a wide frequency range spanning the terahertz and the near-infrared regimes. Edge effects give rise to geometric resonances in such bundles. The antenna efficiency of a CNT bundle at the first resonance can exceed that of a single CNT by four orders of magnitude, which is promising for the design and development of CNT-bundle antennas and composite materials containing CNT-bundles as inclusions.
DRL based Energy-Efficient Radio Resource Allocation Algorithm in Internet of Robotic Things

Communications among user equipment (UE) play a pivotal role in the coordination and information sharing in order to accomplish the predefined collaborative tasks of UE via the internet of robotic things (IoRT). Cloud radio access network (C-RAN) emerges as one of the most compelling architectures to ensure the UE demands. However, to optimize the power usage by fulfilling UE demand over a long operational period, the radio resource allocation (RRA) in C-RAN requires to be more visionary. To solve this challenge, we propose a deep reinforcement learning (DRL) based algorithm consisting of two different value-based networks. One network generates the target value for the second network for the purpose of better convergence. Under the same UE demands, simulation results verify that the proposed DRL algorithm outperforms the Deep Q Network (DQN) and conventional approaches in terms of power consumption.
Strongly Near Voronoi Nucleus Clusters

This paper introduces nucleus clustering in Voronoi tessellations of plane surfaces with applications in the geometry of digital images. A \emph{nucleus cluster} is a collection of Voronoi regions that are adjacent to a Voronoi region called the cluster nucleus. Nucleus clustering is a carried out in a strong proximity space. Of particular interest is the presence of maximal nucleus clusters in a tessellation. Among all of the possible nucleus clusters in a Voronoi tessellation, clusters with the highest number of adjacent polygons are called \emph{maximal nucleus clusters}. The main results in this paper are that strongly near nucleus clusters are strongly descriptively near and every collection of Voronoi regions in a tessellation of a plane surface is a Zelins'kyi-Soltan-Kay-Womble convexity structure.
Decomposition Methods for Nonlinear Optimization and Data Mining

We focus on two central themes in this dissertation. The first one is on decomposing polytopes and polynomials in ways that allow us to perform nonlinear optimization. We start off by explaining important results on decomposing a polytope into special polyhedra. We use these decompositions and develop methods for computing a special class of integrals exactly. Namely, we are interested in computing the exact value of integrals of polynomial functions over convex polyhedra. We present prior work and new extensions of the integration algorithms. Every integration method we present requires that the polynomial has a special form. We explore two special polynomial decomposition algorithms that are useful for integrating polynomial functions. Both polynomial decompositions have strengths and weaknesses, and we experiment with how to practically use them. After developing practical algorithms and efficient software tools for integrating a polynomial over a polytope, we focus on the problem of maximizing a polynomial function over the continuous domain of a polytope. This maximization problem is NP-hard, but we develop approximation methods that run in polynomial time when the dimension is fixed. Moreover, our algorithm for approximating the maximum of a polynomial over a polytope is related to integrating the polynomial over the polytope. We show how the integration methods can be used for optimization. The second central topic in this dissertation is on problems in data science. We first consider a heuristic for mixed-integer linear optimization. We show how many practical mixed-integer linear have a special substructure containing set partition constraints. We then describe a nice data structure for finding feasible zero-one integer solutions to systems of set partition constraints. Finally, we end with an applied project using data science methods in medical research.
SL(2) over the octonions

We interpret an open orbit in a 32-dimensional representation space of Spin(9,1) x SL(2,R) as a substitute for the non-existent group of invertible 2x2 matrices over the octonions and study various natural homogeneous subspaces. The approach is via twistor geometry in eight dimensions.
Total Variation Denoising on Hexagonal Grids

This work combines three paradigms of image processing: i) the total variation approach to denoising, ii) the superior structure of hexagonal lattices, and iii) fast and exact graph cut optimization techniques. Although isotropic in theory, numerical implementations of the $BV$ seminorm invariably show anisotropic behaviour. Discretization of the image domain into a hexagonal grid seems perfectly suitable to mitigate this undesirable effect. To this end, we recast the continuous problem as a finite-dimensional one on an arbitrary lattice, before focussing on the comparison of Cartesian and hexagonal structures. Minimization is performed with well-established graph cut algorithms, which are easily adapted to new spatial discretizations. Apart from producing minimizers that are closer in the $\ell^1$ sense to the clean image for sufficiently high degrees of regularization, our experiments suggest that the hexagonal lattice also allows for a more effective reduction of two major drawbacks of existing techniques: metrication artefacts and staircasing. For the sake of practical relevance we address the difficulties that naturally arise when dealing with non-standard images.
Itô’s theorem and metabelian Leibniz algebras

We prove that the celebrated Itô’s theorem for groups remains valid at the level of Leibniz algebras: if is a Leibniz algebra such that , for two abelian subalgebras and , then is metabelian, i.e. . A structure-type theorem for metabelian Leibniz/Lie algebras is proved. All metabelian Leibniz algebras having the derived algebra of dimension are described, classified and their automorphisms groups are explicitly determined as subgroups of a semidirect product of groups associated to any vector space .
Thermodynamic and spectral properties of compressed Ce calculated using a combined local-density approximation and dynamical mean-field theory

We have calculated thermodynamic and spectral properties of Ce metal over a wide range of volume and temperature, including the effects of 4f electron correlations, by the merger of the local-density approximation and dynamical mean-field theory (DMFT). The DMFT equations are solved using the quantum Monte Carlo technique supplemented by the more approximate Hubbard-I and Hartree-Fock methods. At a large volume we find Hubbard split spectra, the associated local moment, and an entropy consistent with degeneracy in the moment direction. On compression through the volume range of the observed γ-α transition, an Abrikosov-Suhl resonance begins to grow rapidly in the 4f spectra at the Fermi level, a corresponding peak develops in the specific heat, and the entropy drops rapidly in the presence of a persistent, although somewhat reduced, local moment. Our parameter-free spectra agree well with experiment at the α- and γ-Ce volumes, and a region of negative curvature in the correlation energy leads to a shallowness in the low-temperature total energy over this volume range which is consistent with the γ-α transition. As measured by the double occupancy, we find a noticeable decrease in correlation on compression across the transition; however, even at the smallest volumes considered, Ce remains strongly correlated with residual Hubbard bands to either side of a dominant Fermi-level structure. These characteristics are discussed in light of current theories for the volume collapse transition in Ce.
Fat-tailed distribution derived from the first eigenvector of a symmetric random sparse matrix

Many solutions for scientific problems rely on finding the first (largest) eigenvalue and eigenvector of a particular matrix. We explore the distribution of the first eigenvector of a symmetric random sparse matrix. To analyze the properties of the first eigenvalue/vector, we employ a methodology based on the cavity method, a well-established technique in the statistical physics. A symmetric random sparse matrix in this paper can be regarded as an adjacency matrix for a network. We show that if a network is constructed by nodes that have two different types of degrees then the distribution of its eigenvector has fat tails such as the stable distribution (α < 2) under a certain condition; whereas if a network is constructed with nodes that have only one type of degree, the distribution of its first eigenvector becomes the Gaussian approximately. The method consisting of the cavity method and the population dynamical method clarifies these results.
Possibilities and Caveats of Implicit Language Aptitude Measurements

This paper probes into implicit language aptitude (LA), a set of cognitive abilities that showcase learners’ unconscious, automatic second language (L2) attainment. Implicit LA is becoming an important part of aptitude research, and advances the understanding of L2 learning mechanism. This paper critiques the construct of implicit LA and proposes that implicit learning ability and implicit memory ability are two key components of implicit LA. We then explore the measurements of implicit LA. We raise a number of caveats derived from the validation of these measurements, and discuss how to circumvent these issues. One apparent reason for these caveats is the lack of empirical research that focuses exclusively on implicit LA, and thus, aptitude researchers are encouraged not only to include implicit LA measurements in their test batteries, but also to address the connection between the construct and its measurements.
FINITE TIME BLOWUP FOR A SUPERCRITICAL DEFOCUSING NONLINEAR WAVE SYSTEM

We consider the global regularity problem for defocusing nonlinear wave systems $$ \Box u = (\nabla_{{\bf R}^m} F)(u) $$ on Minkowski spacetime ${\bf R}^{1+d}$ with d'Alambertian $\Box := -\partial_t^2 + \sum_{i=1}^d \partial_{x_i}^2$, the field $u: {\bf R}^{1+d} \to {\bf R}^m$ is vector-valued, and $F: {\bf R}^m \to {\bf R}$ is a smooth potential which is positive and homogeneous of order $p+1$ outside of the unit ball, for some $p >1$. This generalises the scalar defocusing nonlinear wave (NLW) equation, in which $m=1$ and $F(v) = \frac{1}{p+1} |v|^{p+1}$. It is well known that in the energy sub-critical and energy-critical cases when $d \leq 2$ or $d \geq 3$ and $p \leq 1+\frac{4}{d-2}$, one has global existence of smooth solutions (for dimensions $d \leq 7$ at least) from arbitrary smooth initial data $u(0), \partial_t u(0)$. In this paper we study the supercritical case where $d = 3$ and $p > 5$. We show that in this case, there exists smooth potential $F$ for some sufficiently large $m$ (in fact we can take $m=40$), positive and homogeneous of order $p+1$ outside of the unit ball, and a smooth choice of initial data $u(0), \partial_t u(0)$ for which the solution develops a finite time singularity. In fact the solution is discretely self-similar in a backwards light cone. The basic strategy is to first select the mass and energy densities of $u$, then $u$ itself, and then finally design the potential $F$ in order to solve the required equation. The Nash embedding theorem is used in the second step, explaining the need to take $m$ relatively large.
Treatment of Periodontal Inflammation in Diabetic Rats with IL-1ra Thermosensitive Hydrogel

Periodontitis is a chronic inflammatory disease that is considered to be the main cause of adult tooth loss. Diabetes mellitus (DM) has a bidirectional relationship with periodontitis. Interleukin-1β (IL-1β) is an important pre-inflammatory factor, which participates in the pathophysiological process of periodontitis and diabetes. The interleukin-1 receptor antagonist (IL-1ra) is a natural inhibitor of IL-1, and the balance between IL-1ra and IL-1β is one of the main factors affecting chronic periodontitis (CP) and diabetes. The purpose of this study is to develop a drug carrier that is safe and nontoxic and can effectively release IL-1ra, which can effectively slow down the inflammation of periodontal tissues with diabetes, and explore the possibility of lowering the blood sugar of this drug carrier. Therefore, in this experiment, a temperature-sensitive hydrogel loaded with IL-1ra was prepared and characterized, and its anti-inflammatory effect in high-sugar environments in vivo and in vitro was evaluated. The results showed that the hydrogel could gel after 5 min at 37 °C, the pore size was 5–70 μm, and the cumulative release of IL-1ra reached 83.23% on the 21st day. Real-time polymerase chain reaction (qRT-PCR) showed that the expression of IL-1β, Interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α) inflammatory factors decreased after the treatment with IL-1ra-loaded thermosensitive hydrogel. Histological evaluation and micro-computed tomography (Micro-CT) showed that IL-1ra-loaded thermosensitive hydrogel could effectively inhibit periodontal inflammation and reduce alveolar bone absorption in rats with diabetic periodontitis. It is worth mentioning that this hydrogel also plays a role in relieving hyperglycemia. Therefore, the temperature-sensitive hydrogel loaded with IL-1ra may be an effective method to treat periodontitis with diabetes.
tt-geometry of Tate motives over algebraically closed fields

We study Tate motives with integral coefficients through the lens of tensor triangular geometry. For some base fields, including $\overline{\mathbb{Q}}$ and $\overline{\mathbb{F}_{p}}$ , we arrive at a complete description of the tensor triangular spectrum and a classification of the thick tensor ideals.
Single-phase Inverter with Common Ground and Voltage Boost Operation

This paper presents a single-phase boost inverter with the common ground operation. A switched inductor-based boost inverter is proposed. The primary feature of the proposed inverter topology is that it produces an ac output voltage higher than the dc input voltage. Traditional voltage source inverters have AC output voltage less than DC input voltage. The switches operate at a high frequency to use smaller passive components that improve system efficiency. The design requirements of components are presented for the inverter. The analysis of this inverter is performed through simulations in MATLAB/Simulink environment. The study is validated through a 1kW inverter at a DC input voltage of 72V and AC output voltage of 230V, achieving maximum efficiency of 97%.
Detection of COVID-19 Using Classification of an X-Ray Image Using a Local Binary Pattern and K-Nearest Neighbors

The recently identified coronavirus pneumonia, which was later given the name COVID-19, is a virus that can be fatal and has affected more than 300,000 individuals around the world. Because there is currently no antiviral therapy or vaccine that has been granted approval by the FDA to cure or prevent this sickness, an automatic method for disease identification is required because of the fast global distribution of this exceedingly contagious and lethal virus. A unique machine learning strategy for automatically detecting this ailment was discovered. Machine learning approaches should be applied in essential jobs in infectious illnesses. As a result, our major aim is to use computer vision algorithms to identify COVID-19 without the need for human interaction. This paper suggested using image processing to classify objects and make early detections using X-ray pictures. Features are extracted for this region using a variety of techniques, including (LBP), (HOG), and use K-Nearest Neighbor algorithm (KNN) for classification, with training percentages of 50%, 60%, 70%, 80%, and 90%. Experiments indicated that using the suggested approach to identify X-ray photos of corona patients, it is feasible to diagnose the disease using X-ray images by training the device on the image data set (about 2,400 photos). The results were tested on the average of the samples taken (random 2000 images) each time and the measurement of multiple training ratios (50%, 60%, 70%, 80%, and 90%). The experimental findings revealed remarkable prediction accuracy in all investigated scenarios, ranging from 85% to 99%.
A Human Motion Capture System Based on MEMS Inertial Measurement

Motion capture system is usually used to measure the body posture and track the trajectory of motion in three-dimensional space. With the emergence of MEMS inertial sensors that is low power consumption and high integration, the motion capture system based on MEMS inertial measurement has been applied various market prospect. In this paper, we realized a human motion capture system based on inertial sensors, which is low-cost, real-time. The system is mainly composed of sensor measurement unit and pose estimation unit on PC. The sensor unit exchanges data with PC through general WiFi network. A sensor unit includes 9-axis inertial sensors (three-axis gyroscope, three-axis accelerometer and three-axis magnetometer), MCU and network communication module. The sensor units were attached in some parts of the human body to collect the 9-axis information of limbs in real time. The 9-axis information was transmitted to the pose estimation unit through the network communication module. The orientation of single sensor unit was estimated although madgwick algorithm. The pose estimation unit used human skeleton model and vector representation to reconstruct and track the whole-body posture. Finally, qualitative and quantitative tests were carried out. Experiments showed that the system had good dynamic characteristics and high precision.
Dynamic coupling of ferromagnets via spin Hall magnetoresistance

The synchronized magnetization dynamics in ferromagnets on a nonmagnetic heavy metal caused by the spin Hall effect is investigated theoretically. The direct and inverse spin Hall effects near the ferromagnetic/nonmagnetic interface generates longitudinal and transverse electric currents. The phenomenon is known as the spin Hall magnetoresistance effect, whose magnitude depends on the magnetization direction in the ferromagnet due to the spin transfer effect. When another ferromagnet is placed onto the same nonmagnet, these currents are again converted to the spin current by the spin Hall effect, and excites the spin torque to this additional ferromagnet, resulting in the excitation of the coupled motions of the magnetizations. The in-phase or antiphase synchronization of the magnetization oscillations, depending on the value of the Gilbert damping constant and the field-like torque strength, is found in the transverse geometry by solving the Landau-Lifshitz-Gilbert equation numerically. On the other hand, in addition to these synchronizations, the synchronization having a phase difference of a quarter of a period is also found in the longitudinal geometry. The analytical theory clarifying the relation among the current, frequency, and phase difference is also developed, where it is shown that the phase differences observed in the numerical simulations correspond to that giving the fixed points of the energy supplied by the coupling torque.
On the sharpness of a certain spectral stability estimate for the Dirichlet Laplacian

We consider a spectral stability estimate by Burenkov and Lamberti concerning the variation of the eigenvalues of second order uniformly elliptic op- erators on variable open sets in the N-dimensional euclidean space, and we prove that it is sharp for any dimension N. This is done by studying the eigenvalue problem for the Dirichlet Laplacian on special open sets inscribed in suitable spherical cones.