Miracle-dz/UniFAR-FTUs huggingface dataset:
Ballistic Limit of UHMWPE Composite Armor under Impact of Ogive-Nose Projectile

The ballistic response of armor has been widely used to evaluate its feasibility and advantages as a protective structure. To obtain the ballistic performance and ballistic limitations of composite armor, a type of ultra–high molecular weight polyethylene (UHMWPE) composite armor is designed, which is composed of UHMWPE laminates and steel face sheets of Q235. The total thickness of the armor is 53 mm, with an in–plane dimension of 300 mm × 300 mm. Then, an experimental study of the ballistic impact response of composite armor subject to a typical ogive–nose projectile was carried out. In the velocity range of 501.1 to 1026.1 m/s, the 14.5 mm caliber armor–piercing projectile could penetrate through the composite armor. At the velocity of 433.3 m/s, the A–P projectile was embedded in the armor, leaving a bulge mark on the back sheet. Therefore, 467.2 m/s is taken as the ballistic limit of the armor under the impact of the ogive–nose projectile. In addition, a corresponding numerical simulation model is also established to predict the ballistic limit of the projectile. The numerical predictions are consistent with the experimental results. The ballistic limit obtained from the numerical simulation results is 500 m/s, which is acceptable with a relative error of 7.02%. The failure mechanism of the composite armor is also obtained. Petaling is the main dominant failure mode for both face sheets, while delamination and shear failure dominate the penetration process of UHMWPE laminates. Finally, the perforation mechanism of composite armor under the impact of an A–P projectile is analyzed with theoretical models to predict the residual velocity, the work performed during the perforation, and the resisting stress of σs in the cavity–expansion model. The experimental and numerical simulation results can provide necessary data in the analysis of the composite structure’s dynamic response under the impact of sharp head penetrators. The research results present the ballistic performance, failure mechanism, and ballistic limit of the composite armor under the impact of a typical ogive–nose projectile, which can be significant in the design of composite armor in the areas of ship shield, fortifications protection, and bulletproof structures against threats from sharp head penetrators.
First detection of surface magnetic fields in post-AGB stars: the cases of U Monocerotis and R Scuti

While several observational investigations have revealed the presence of magnetic fields in the circumstellar envelopes, jets and outflows of post-Asymptotic Giant Branch stars (PAGBs) and planetary nebulae (PNe), none has clearly demonstrated their presence at the stellar surface. The lack of information on the strength of the surface magnetic fields prevents us from performing any thorough assessment of their dynamic capability (i.e. material mixing, envelope shaping, etc). We present new high resolution spectropolarimetric (Stokes V ) observations of a sample of PAGB stars, realised with the instruments ESPaDOnS and Narval, where we searched for the presence of photospheric magnetic fields. Out of the seven targets investigated the RV Tauri stars U Mon and R Sct display a clear Zeeman signature and return a definite detection after performing a least squares deconvolution (LSD) analysis. The remaining five PAGBs show no significant detection. We derived longitudinal magnetic fields of 10.2 +/- 1.7 G for U Mon and 0.6 +/- 0.6 G for R Sct. In both cases the Stokes profiles point towards an interaction of the magnetic field with the atmosphere dynamics. This first discovery of weak magnetic fields (i.e. ~10 gauss level) at the stellar surface of PAGB stars opens the door to a better understanding of magnetism in evolved stars.
Statistical Performance Analysis on ≈ 320 Perovskite Single- and Two-junction Solar Cells and Modules from >30 Global Sources

As perovskite photovoltaics (PV) advance from the laboratory to commercial prototypes, their accurate and reliable performance testing is becoming increasingly important. The well documented dynamic response of perovskite solar cells to an external applied voltage has led to the development of steady-state performance measurement methods; however, these methods have not been widely adopted by the perovskite PV community. A key reason for this is that steady-state measurement methods take tens of minutes to complete, as opposed to conventional “fast” current-voltage (I-V) measurements usually lasting a few seconds. Fast I-Vs arise from a snapshot, almost always not a steady-state condition of the device; however, given their widespread use, the question arises: how do performance parameters of perovskite PV compare when measured with fast I-V and with a steady-state method? We compile results from ca. 320 perovskite PV cells and modules, including single junction, and two-terminal perovskite-perovskite and perovskite-Si tandems, and show that fast I-Vs can provide a useful measure of the open-circuit voltage of the devices, while the short-circuit current and the overall efficiency can be widely misestimated. We discuss implications of these findings on performance testing protocols and propose possible options for fast and accurate testing of perovskite PV.
Algebraic Multilevel Methods for Markov Chains

A new algebraic multilevel algorithm for computing the second eigenvector of a column-stochastic matrix is presented. The method is based on a deflation approach in a multilevel aggregation framework. In particular a square and stretch approach, first introduced by Treister and Yavneh, is applied. The method is shown to yield good convergence properties for typical example problems.
Tumor-Derived Exosomes and Their Role in Breast Cancer Metastasis

Breast cancer has been the most common cancer in women worldwide, and metastasis is the leading cause of death from breast cancer. Even though the study of breast cancer metastasis has been extensively carried out, the molecular mechanism is still not fully understood, and diagnosis and prognosis need to be improved. Breast cancer metastasis is a complicated process involving multiple physiological changes, and lung, brain, bone and liver are the main metastatic targets. Exosomes are membrane-bound extracellular vesicles that contain secreted cellular constitutes. The biogenesis and functions of exosomes in cancer have been intensively studied, and mounting studies have indicated that exosomes play a crucial role in cancer metastasis. In this review, we summarize recent findings on the role of breast cancer-derived exosomes in metastasis organotropism and discuss the potential promising clinical applications of targeting exosomes as novel strategies for breast cancer diagnosis and therapy.
The ionized gas in the central region of NGC 5253 - 2D mapping of the physical and chemical properties

Context. Blue compact dwarf (BCD) galaxies constitute the ideal laboratories to test the interplay between massive star formation and the surrounding gas. As one of the nearest BCD galaxies, NGC 5253 was previously studied with the aim to elucidate in detail the starburst interaction processes. Some open issues regarding the properties of its ionized gas still remain to be addressed. Aims. The 2D structure of the main physical and chemical properties of the ionized gas in the core of NGC 5253 has been studied. Methods. Optical integral field spectroscopy (IFS) data has been obtained with FLAMES Argus and lower resolution gratings of the Giraffe spectrograph. Results. We derived 2D maps for different tracers of electron density (ne), electron temperature (Te) and ionization degree. The maps for ne as traced by [Oii], [Sii], [Feiii], and [Ariv] line ratios are compatible with a 3D stratified view of the nebula with the highest ne in the innermost layers and a decrease of ne outwards. 2D maps of Te were measured from [Oiii ]a nd [Sii] line ratios; to our knowledge, this is the first time that a Te map based on [Sii] lines for an extragalactic object has been presented. The joint interpretation of the Te([Sii]) and Te([Oiii]) maps is consistent with a Te structure in 3D with higher temperatures close to the main ionizing source surrounded by a colder and more diffuse component. The highest ionization degree is found at the peak of emission for the gas with relatively high ionization in the main Giant Hii Region and lower ionization degree delineating the more extended diffuse component. We derived abundances of oxygen, neon, argon, and nitrogen. Abundances for O, Ne and Ar are constant over the mapped area within <0.1 dex. The mean 12 + log(O/H) is 8.26 ± 0.04 while the relative abundances of log(N/O), log(Ne/O) and log(Ar/O) were ∼−1.32±0.05, −0.65±0.03 and −2.33±0.06, respectively. There are two locations with enhanced N/O. The first (log (N/O) ∼− 0.95) occupies an area of about 80 pc × 35 pc and is associated to two super star clusters. The second (log (N/O) ∼− 1.17), reported here for the first time, is associated to two moderately massive (2–4 × 10 4 M� ) and relatively old (∼10 Myr) clusters. A comparison of the N/O map with those produced by strong line methods supports the use of N2O2 over N2S2 in the search for chemical inhomogeneities within a galaxy. The results on the localized nitrogen enhancement were used to compile and discuss the factors that affect the complex relationship between Wolf-Rayet stars and N/O excess.
Relevance of Conventional Microscopy in the Diagnosis of Mucormycosis during COVID-19 Pandemic

Introduction: Mucormycosis is a lethal disease which bewildered the health-care community of India during the ongoing second wave of the COVID-19 pandemic. The diagnosis is challenging considering the poor isolation in culture. Aims: The aim of the study was to emphasize the utility of potassium hydroxide (KOH) mount examination using conventional light microscopy for early diagnosis of mucormycosis in resource-limited settings. Materials and Methods: A retrospective analysis of results for all the samples including tissue biopsies, swabs, and pus received in the laboratory for KOH microscopy was done, and results were recorded. The clinical and demographic details of the patients were collected from the hospital information system. Results: A total of 75 samples from 50 patients were received in the laboratory. Out of these, 43 samples from 35 patients showed fungal hyphae (38 patients with only nonseptate hyaline hyphae, 2 with septate hyaline hyphae, and 3 samples with mixed infections). All patients except one were positive for severe acute respiratory syndrome coronavirus 2 infection. The most common age group was 45–59 years (40%), followed by 30–44 years (34.28%) with a male predominance. There was a significant difference in hemoglobin A1C (P = 0.005) and ferritin (P = 0.017) levels between laboratory-confirmed mucormycosis patients and clinically suspected mucormycosis patients without confirmation. Conclusion: Early diagnosis and initiation of targeted therapy is the cornerstone for treating mucormycosis patients. Hence, a rapid and reliable mode of diagnosis is the need of the hour. Conventional microscopy is such a tool that may be used, especially in resource-limited settings.
Vandermonde determinantal ideals

We show that the ideal generated by maximal minors (i.e., $k+1$-minors) of a $(k+1) \times n$ Vandermonde matrix is radical and Cohen-Macaulay. Note that this ideal is generated by all Specht polynomials with shape $(n-k,1, …,1)$.
A Deep Fuzzy Clustering Network for Gearbox Health Assessment

T Conducting gearbox health status evaluation can effectively obtain the operating status of the equipment so as to develop maintenance plans. A gearbox health condition evaluation method based on deep fuzzy clustering neural networks (DFCNN) is proposed. The proposed DFCNN incorporates the high-level feature extraction layer of Generalized Supervised Deep Autoencoders (GSDAE) into an improved fuzzy C-Means (KMDFCM) clustering algorithm. GSDAE is used to eliminate the variation of feature parameters due to differences in operating conditions, and then KMDFCM is used for unsupervised condition assessment. Taking a planetary gearbox as an example, it is shown that the proposed method provides a new idea for solving the health condition assessment of gearboxes.
Spin Orbit Torque-based Crossbar Array for Error Resilient Binary Convolutional Neural Network

Convolutional Neural Network (CNN) is one of the most important Deep Neural Networks (DNN) classes that helps solving many tasks related to image recognition and computer vision. Their classical implementations by using conventional CMOS technologies and digital design techniques are still considered very energy-consuming. Floating point CNN relies primarily on MAC (Multiply and ACcumulate) operation. Recently, cost-effective Bite-wise CNN based on XNOR and bit-counting operations have been considered as a possible hardware implementation candidate. However, the Von-Neumann bottleneck due to intensive data fetching between memory and the computing core limits their scalability on hardware. XNOR-BITCOUNT operations can be easily implemented by using In Memory Computing (IMC) paradigms executed on a memristive crossbar array. Among emerging memristive devices, the Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) offers the possibility to have a higher ON resistance that allows reducing the reading current, since all the crossbar array is read in parallel. This could contribute to a further reduction of energy consumption, paving the way for much bigger crossbar designs. This study presents a crossbar architecture based on SOT-MRAM with very low energy consumption; we study the impact of process variability on the synaptic weights and perform Monte-Carlo simulations of the overall crossbar array to evaluate the error rate. Simulation results show that this implementation has lower energy consumption with respect to other memristive solutions with 65.89 fJ per read operation. The design is also quite robust to process variations, with very low reading inaccuracies up to 10 %.
Low-Latency Multi-Kernel Polar Decoders

Polar codes have been receiving increased attention for application in beyond 5G networks. They offer low-complexity decoding algorithm and can achieve symmetric channel capacity. However, the majority of research works have focused on the codes constructed by the binary kernel (<inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> polarization matrix) which bounds the code length to an integer power of 2. Multi-kernel polar codes have been proposed as a method that allows the construction of polar codes with sizes different from powers of 2 by mixing multiple kernels of different dimensions. A hardware implementation based on the successive cancellation (SC) algorithm found in the literature shows that it suffers from a long decoding latency. In this paper, we design and implement a multi-kernel decoder based on the fast-simplified SC (fast-SSC) algorithm to decrease the decoding latency. It can decode any code constructed by binary and ternary (<inline-formula> <tex-math notation="LaTeX">$3 \times 3$ </tex-math></inline-formula>) kernels featuring flexible code length, code rate, and kernel sequence. FPGA implementation results reveal that a polar code of length <inline-formula> <tex-math notation="LaTeX">$N = 1536$ </tex-math></inline-formula>, rate <inline-formula> <tex-math notation="LaTeX">$\mathcal {R} = 1/2$ </tex-math></inline-formula> with Processing Element (<inline-formula> <tex-math notation="LaTeX">$P_{e}$ </tex-math></inline-formula>) value of <inline-formula> <tex-math notation="LaTeX">$P_{e} = 240$ </tex-math></inline-formula>, gains 84.6% lower latency compared to the original algorithm. Also, the architecture supports polar codes constructed by purely-binary and purely-ternary kernels. A polar code of length <inline-formula> <tex-math notation="LaTeX">$N = 1024$ </tex-math></inline-formula>, rate <inline-formula> <tex-math notation="LaTeX">$\mathcal {R} = 1/2$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$P_{e} = 120$ </tex-math></inline-formula> achieves an information throughput of 432 Mbps.
Sparsity Constrained Minimization via Mathematical Programming with Equilibrium Constraints

Sparsity constrained minimization captures a wide spectrum of applications in both machine learning and signal processing. This class of problems is difficult to solve since it is NP-hard and existing solutions are primarily based on Iterative Hard Thresholding (IHT). In this paper, we consider a class of continuous optimization techniques based on Mathematical Programs with Equilibrium Constraints (MPECs) to solve general sparsity constrained problems. Specifically, we reformulate the problem as an equivalent biconvex MPEC, which we can solve using an exact penalty method or an alternating direction method. We elaborate on the merits of both proposed methods and analyze their convergence properties. Finally, we demonstrate the effectiveness and versatility of our methods on several important problems, including feature selection, segmented regression, MRF optimization, trend filtering and impulse noise removal. Extensive experiments show that our MPEC-based methods outperform state-of-the-art techniques, especially those based on IHT.
Robust discretization in quantized tensor train format for elliptic problems in two dimensions

In this work we propose an efficient black-box solver for two-dimensional stationary diffusion equations, which is based on a new robust discretization scheme. The idea is to formulate an equation in a certain form without derivatives with a non-local stencil, which leads us to a linear system of equations with dense matrix. This matrix and a right-hand side are represented in a low-rank parametric representation -- the quantized tensor train (QTT-) format, and then all operations are performed with logarithmic complexity and memory consumption. Hence very fine grids can be used, and very accurate solutions with extremely high spatial resolution can be obtained. Numerical experiments show that this formulation gives accurate results and can be used up to $2^{60}$ grid points with no problems with conditioning, while total computational time is around several seconds.
Analysis of a Novel Fault-Tolerant Multi-Tooth Flux-Switching Permanent Magnet Machine

Flux-switching permanent-magnet machines with multi-tooth stator structure have attracted wide attention due to their advantages of high torque density and high efficiency. Based on it, a fault-tolerant multi-tooth flux-switching permanent-magnet machine (FTMFSPM) is proposed and compared to the conventional fault-tolerant flux-switching permanent-magnet (FTFSPM) machine. In order to evaluate the electromagnetic performance of proposed FTMFSPM machine, a comparison of the electromagnetic performance of proposed FTMFSPM machine and conventional FTFSPM machine is conducted by finite-element analysis (FEA), and the FEA-predicted results indicate that the proposed FTMFSPM machine exhibits better electromagnetic performances than FTFSPM machine, such as more sinusoidal and larger back EMF, high average torque, smaller torque ripple, higher self-inductance and lower mutual-inductance.
The speed of convergence in the renewal theorem

In this article we study a diophantine property of probability measures on R. We will always assume that the considered measures have an exponential moment and a drift. We link this property to the points in C close to the imaginary axis where the Fourier-Laplace transform of those measures take the value 1 and finally, we apply this to the study of the speed in Kesten's renewal theorem on R.
Resurgence Analysis of Quantum Invariants: Seifert Manifolds and Surgeries on The Figure Eight Knot

We provide a resurgence analysis of the quantum invariants of all Seifert fibered three manifold, with oriented base of genus zero and at least three exceptional fibers. This generalizes results of Gukov-Putrov-Marin\"o and Chun. Building on work of the first named author and Hansen, we also give a resurgence analysis of the quantum invariants of a family of hyperbolic three manifolds obtained by surgery along the figure eight knot.
Mock modular forms whose shadows are Eisenstein series of integral weight

The purpose of this article is to give a simple and explicit construction of mock modular forms whose shadows are Eisenstein series of arbitrary integral weight, level, and character. As application, we construct forms whose shadows are Hecke's Eisenstein series of weight one associated to imaginary quadratic fields, recovering some results by Kudla, Rapoport and Yang (1999), and Schofer (2009), and forms whose shadows equal $\Theta^{2k}(z)$ for $k\in \{1,2,3,4\}$, where $\Theta(z)$ denotes Jacobi's theta function.
Decoupling facial motion features and identity features for micro-expression recognition

Background Micro-expression is a kind of expression produced by people spontaneously and unconsciously when receiving stimulus. It has the characteristics of low intensity and short duration. Moreover, it cannot be controlled and disguised. Thus, micro-expression can objectively reflect people’s real emotional states. Therefore, automatic recognition of micro-expressions can help machines better understand the users’ emotion, which can promote human-computer interaction. What’s more, micro-expression recognition has a wide range of applications in fields like security systems and psychological treatment. Nowadays, thanks to the development of artificial intelligence, most micro-expression recognition algorithms are based on deep learning. The features extracted by deep learning model from the micro-expression video sequences mainly contain facial motion feature information and identity feature information. However, in micro-expression recognition tasks, the motions of facial muscles are subtle. As a result, the recognition can be easily interfered by identity feature information. Methods To solve the above problem, a micro-expression recognition algorithm which decouples facial motion features and identity features is proposed in this paper. A Micro-Expression Motion Information Features Extraction Network (MENet) and an Identity Information Features Extraction Network (IDNet) are designed. By adding a Diverse Attention Operation (DAO) module and constructing divergence loss function in MENet, facial motion features can be effectively extracted. Global attention operations are used in IDNet to extract identity features. A Mutual Information Neural Estimator (MINE) is utilized to decouple facial motion features and identity features, which can help the model obtain more discriminative micro-expression features. Results Experiments on the SDU, MMEW, SAMM and CASME II datasets were conducted, which achieved competitive results and proved the superiority of the proposed algorithm.
Robust Wide-Area Damping Controller for Inter-Area Oscillation with AC/DC Tie-Line

Low-frequency inter-area oscillation is a critical concern in the inter-linked power system. Numerous investi-gations are being carried out in order to damp these inter-area oscillations. High-voltage direct current (HVDC) links are now available to adequately mitigate these oscillations, while strengthening system stability. Hence, in this work, a robust wide area damping controller (WADC) is designed to modulate the power flow through HVDC tie-line for damping inter-area oscillation in power system. First step involves the determination of crucial inter-area modes by using modal analysis approach. Next, geometric technique is used to choose an appropriate wide-area feedback signal. Then, based on above information a robust $H_{\infty}$ controller is designed in order to effectively damp out inter-area oscillation. Finally, the proposed WADC is validated on HVDC integrated 4-machine 11-bus system to ensure its efficacy.