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Existence of nontopological solutions of the self-dual Einstein-Maxwell-Higgs equations on compact surfaces(vol 60, 94, 2021)
No full textRegarding the article, "Existence of nontopological solutions of the self-dual Einstein-Maxwell-Higgs equations on compact surfaces", [Calculus of Variations and Partial Differential Equations 60(3) (2021), Article No. 94, 1-24], we add a condition on string numbers in Theorem 1.2 and correct the setup of a function space for degree theory. We also modify some parts of the proof according to the correction
Revealing the Dual-Layered Solid Electrolyte Interphase on Lithium Metal Anodes via Cryogenic Electron Microscopy
No full textIt is crucial to comprehend the effect of the solid electrolyte interphase (SEI) on battery performance to develop stable Li metal batteries. Nonetheless, the exact nanostructure and working mechanisms of the SEI remain obscure. Here, we have investigated the relationship between electrolyte components and the structural configuration of interfacial layers using an optimized cryogenic transmission electron microscopy (CryoTEM) analysis and theoretical calculation. We revealed a unique dual-layered inorganic-rich nanostructure, in contrast to the widely known simple specific component-rich SEI layers. The origin of stable Li cycling is closely related to the Li-ion diffusion mechanism via diverse crystalline grains and numerous grain boundaries in the fine crystalline-rich SEI layer. The results can elucidate a particular issue pertaining to the chemical structure of SEI layers that can induce uniform Li diffusion and rapid Liion conduction on Li metal anodes, developing stable Li metal batteries
Direct 18F-Fluorosulfurylation of Phenols and Amines Using an [18F]FSO2+Transfer Agent Generated In Situ
No full textWe report the direct radiofluorosulfurylation method for the synthesis of 18F-labeled fluorosulfuryl derivatives from phenols and amines using an [18F]FSO2+ transfer agent generated in situ. Nucleophilic radiofluorination is achieved even in a hydrous organic medium, obviating the need for azeotropic drying and the use of cryptands. This unprecedented, operationally simple isotopic functionalization facilitates the reliable production of potential radiotracers for positron emission tomography, rendering facile access to SuFEx radiochemistry
Water-Soluble Cellulose As a New Class of Green CH4 Hydrate Inhibitors: Insights from Experiments and Molecular Dynamics Simulations
No full textCellulose is a cheap, ecofriendly, and abundant organic polymer. In this study, physically or chemically modified microcrystalline cellulose (MCC) was evaluated as a kinetic hydrate inhibitor (KHI) for CH4 hydrates using experimental and computational methods. To overcome the strong hydrophobicity of MCC, high-pressure homogenized cellulose (HPHC) was prepared by dispersing MCC homogeneously in water, and surface-modified ionic cellulose (SMIC) was obtained by attaching an ionic liquid (1,3-dimethylimidazolium methylphosphite, [DMIM][MP]) to the surface of MCC. The onset temperature and gas uptake of CH4 hydrates were experimentally measured to examine the inhibition performance of HPHC and SMIC. Experimental results demonstrated that both HPHC and SMIC functioned as potential KHIs and SMIC had better inhibition capability than HPHC. Molecular dynamics simulations were performed to reveal the inhibition mechanisms of the KHIs during cage formation and hydrate growth. It was found that the different inhibition effects of the KHIs were caused by a combination of multiple inhibition mechanisms, including both their interactions with water and cage adsorption. Given the improved hydrophilicity and inhibition performance through physical or chemical modification of the naturally derived organic compound, cellulose holds great potential as a novel and green KHI
Non-Uniform Metasurface-Integrated Circularly Polarized End-Fire Dipole Array Antenna
No full textThis paper presents a high-gain wideband circularly polarized antenna composed of an end-fire dipole array antenna integrated with a metasurface. The antenna consists of a two-layer cascaded non-uniform metasurface made up of 4 x 4 circular patches with cross-slots of unequal lengths placed above an end-fire dipole array antenna with an air gap between the structures. The end-fire dipole array antenna comprises four equally spaced dipole elements, and each dipole is connected to a parallel stripline printed on the front and back sides of the substrate. The metasurface, which is made up of a circular patch with 2 x 2 center patches that have a different radius than the outer patches, and the cross-slots of unequal lengths are used for the polarization conversion of a linearly polarized wave to a circularly polarized wave. The measured reflection coefficients for |S11| <-10 dB yielded an impedance bandwidth of 25.6-31.8 GHz (21.5%), a 3-dB axial ratio (AR) bandwidth of 26.1-30.5 GHz (15.5%), a 3-dB gain bandwidth of 26.0-31.1 GHz (17.4%) with a peak gain of 11.0 dBic, and a radiation efficiency of more than 80% in the axial ratio bandwidth
Effects of Korean Writing Instruction in the Context of English Medium Instruction ??? Changes in Motivation and Self-Efficacy
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Dynamic Fourier ptychography with deep spatiotemporal priors
No full textFourier ptychography (FP) involves the acquisition of several low-resolution intensity images of a sample under varying illumination angles. They are then combined into a high-resolution complex-valued image by solving a phase-retrieval problem. The objective in dynamic FP is to obtain a sequence of high-resolution images of a moving sample. There, the application of standard frame-by-frame reconstruction methods limits the temporal resolution due to the large number of measurements that must be acquired for each frame. In this work instead, we propose a neural-network-based reconstruction framework for dynamic FP. Specifically, each reconstructed image in the sequence is the output of a shared deep convolutional network fed with an input vector that lies on a one-dimensional manifold that encodes time. We then optimize the parameters of the network to fit the acquired measurements. The architecture of the network and the constraints on the input vectors impose a spatiotemporal regularization on the sequence of images. This enables our method to achieve high temporal resolution without compromising the spatial resolution. The proposed framework does not require training data. It also recovers the pupil function of the microscope. Through numerical experiments, we show that our framework paves the way for high-quality ultrafast FP
Tracking Multiple Unmanned Aerial Vehicles through Occlusion in Low-Altitude Airspace
No full textIn an intelligent multi-target tracking (MTT) system, the tracking filter cannot track multi-targets significantly through occlusion in a low-altitude airspace. The most challenging issues are the target deformation, target occlusion and targets being concealed by the presence of background clutter. Thus, the true tracks that follow the desired targets are often lost due to the occlusion of uncertain measurements detected by a sensor, such as a motion capture (mocap) sensor. In addition, sensor measurement noise, process noise and clutter measurements degrade the system performance. To avoid track loss, we use the Markov-chain-two (MC2) model that allows the propagation of target existence through the occlusion region. We utilized the MC2 model in linear multi-target tracking based on the integrated probabilistic data association (LMIPDA) and proposed a modified integrated algorithm referred to here as LMIPDA-MC2. We consider a three-dimensional surveillance for tracking occluded targets, such as unmanned aerial vehicles (UAVs) and other autonomous vehicles at low altitude in clutters. We compared the results of the proposed method with existing Markov-chain model based algorithms using Monte Carlo simulations and practical experiments. We also provide track retention and false-track discrimination (FTD) statistics to explain the significance of the LMIPDA-MC2 algorithm
Deep-learning approach for predicting crystalline phase distribution of femtosecond laser-processed silicon
No full textIn this study, two deep-learning models are presented to predict the crystalline phases of femtosecond laser-processed silicon. To obtain the datasets, single-crystal silicon was processed by a femtosecond laser using 49 different combinations of laser fluence and scanning speed, and for each specimen, Raman spectra were measured at 22,500 locations inside a square domain. The first model was trained to classify the Raman spectra of silicon into six silicon phases. By applying the model to the entire surface of a silicon specimen in batches, the silicon phase distribution was visualized in RGB color values, with each color representing a particular silicon phase. Using the classification results of the 49 specimens obtained by the first model, the second model was developed to predict the silicon phase distribution image from the inputs of the laser fluence and scanning speed. The average prediction accuracy of the second model was 86.60%.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)