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Mode-Selective Elastic Metasurfaces
No full textThe existence of multimodes is a unique characteristic of various elastic wave systems, including elastic metasurfaces. Nevertheless, most of the previous research on elastic metasurfaces has focused on a single mode only, so the related physics is largely unknown and noise is generated by the undesired incident wave mode. Here, we present a mode-selective elastic metasurface that can tailor the target wave mode while filtering out the undesired wave mode. To this end, analytical investigation is carried out for an elastic metasurface with multimode incidence such that the metasurface can operate for both longitudinal and shear wave modes. After that, the mode-selective elastic metasurface is designed and validated numerically and experimentally. With our research, it is now possible to explicitly design an elastic metasurface for the multimode incidence case. Furthermore, since the noise caused by the undesired wave mode is barely generated with the proposed mode-selective metasurface, various elastic wave devices and physical findings are expected from the current research
Dynamic Behavior of the Domain Wall Motion in Pt/Co Multilayer with Perpendicular Magnetic Anisotropy
No full textDNA polymerase theta-mediated repair of high LET radiation-induced complex DNA double-strand breaks
No full textDNA polymerase ?? (POLQ) is a unique DNA polymerase that is able to perform microhomology-mediated end-joining as well as translesion synthesis (TLS) across an abasic (AP) site and thymine glycol (Tg). However, the biological significance of the TLS activity is currently unknown. Herein we provide evidence that the TLS activity of POLQ plays a critical role in repairing complex DNA double-strand breaks (DSBs) induced by high linear energy transfer (LET) radiation. Radiotherapy with high LET radiation such as carbon ions leads to more deleterious biological effects than corresponding doses of low LET radiation such as X-rays. High LET-induced DSBs are considered to be complex, carrying additional DNA damage such as AP site and Tg in close proximity to the DSB sites. However, it is not clearly understood how complex DSBs are processed in mammalian cells. We demonstrated that genetic disruption of POLQ results in an increase of chromatid breaks and enhanced cellular sensitivity following treatment with high LET radiation. At the biochemical level, POLQ was able to bypass an AP site and Tg during end-joining and was able to anneal two single-stranded DNA tails when DNA lesions were located outside the microhomology. This study offers evidence that POLQ is directly involved in the repair of complex DSBs
Orai1 is an entotic Ca2+ channel for non-apoptotic cell death, entosis in cancer development
No full textEntosis is a non-apoptotic cell death process that forms characteristic cell-in-cell structures in cancers, killing invading cells. Intracellular Ca2+ dynamics are essential for cellular processes, including actomyosin contractility, migration, and autophagy. However, the significance of Ca2+ and Ca2+ channels participating in entosis is unclear. Here, it is shown that intracellular Ca2+ signaling regulates entosis via SEPTIN-Orai1-Ca2+/CaM-MLCK-actomyosin axis. Intracellular Ca2+ oscillations in entotic cells show spatiotemporal variations during engulfment, mediated by Orai1 Ca2+ channels in plasma membranes. SEPTIN controlled polarized distribution of Orai1 for local MLCK activation, resulting in MLC phosphorylation and actomyosin contraction, leads to internalization of invasive cells. Ca2+ chelators and SEPTIN, Orai1, and MLCK inhibitors suppress entosis. This study identifies potential targets for treating entosis-associated tumors, showing that Orai1 is an entotic Ca2+ channel that provides essential Ca2+ signaling and sheds light on the molecular mechanism underlying entosis that involves SEPTIN filaments, Orai1, and MLCK
Quasiparticles, flat bands and the melting of hydrodynamic matter
No full textThe concept of quasiparticles-long-lived low-energy particle-like excitations-has become a cornerstone of condensed quantum matter, where it explains a variety of emergent many-body phenomena such as superfluidity and superconductivity. Here we use quasiparticles to explain the collective behaviour of a classical system of hydrodynamically interacting particles in two dimensions. In the disordered phase of this matter, measurements reveal a subpopulation of long-lived particle pairs. Modelling and simulation of the ordered crystalline phase identify the pairs as quasiparticles, emerging at the Dirac cones of the spectrum. The quasiparticles stimulate supersonic pairing avalanches, bringing about the melting of the crystal. In hexagonal crystals, where the intrinsic three-fold symmetry of the hydrodynamic interaction matches that of the crystal, the spectrum forms a flat band dense with ultra-slow, low-frequency phonons whose collective interactions induce a much sharper melting transition. Altogether, these findings demonstrate the usefulness of concepts from quantum matter theory in understanding many-body physics in classical dissipative settings. The concept of quasiparticles helps to describe various quantum phenomena in solids. It is now shown that certain properties of a classical system of hydrodynamically interacting particles can also be described by means of quasiparticles
Trace element characterization and source identification of particulate matter of different sizes in Hanoi, Vietnam
No full textParticulate matter (PM) with aerodynamic diameters of <1 mu m (PM1), 2.5 mu m (PM2.5), and 10 mu m (PM10) were investigated and analyzed for 15 trace elements in the center of Hanoi, Vietnam during April-September 2018. The mean concentrations were 40 +/- 11, 53 +/- 17, and 132 +/- 39 mu g/m3 for PM1, PM2.5, and PM10, respectively, indicating that PM pollution was severe in Hanoi. During the sampling period, the PM concentrations were little affected by local meteorological conditions. The severe PM pollution in Hanoi was significantly influenced by long-range atmo-spheric transport from northern and northeastern regions, with higher potential source areas for PM1. The total mean concentrations of 15 elements were 1417 +/- 141, 1624 +/- 159, and 2652 +/- 251 ng/m3 in PM1, PM2.5, and PM10, respectively, with Al, Zn, K, Cr, and Ni as the most abundant elements. The particle-size distribution of PM and elements showed a distinct peak in PM1. The metallurgy industry, coal combustion, traffic emission, biomass burning, and soil dust were identified as major contributors of elements in three-size PM. This study implies that PM1 pollution should gain more attention regarding its level and chemical composition
Machine learning approach for predicting anaerobic digestion performance and stability in direct interspecies electron transfer-stimulated environments
No full textDirect interspecies electron transfer (DIET) stimulation in anaerobic digestion (AD) processes by adding conductive materials has been reported to improve process stability and recovery from process imbalance during long-term continuous operations. In this study, machine learning (ML)-based models using three algorithms, namely artificial neural network, support vector machine, and random forest, were constructed to predict AD efficiency in DIET-stimulated environments. The target output variables were the chemical oxygen demand removal efficiency and methane production rate, which are two major parameters to assess AD efficiency and stability. All constructed ML-based models had high prediction efficiencies for both output variables (correlation coefficient > 0.934), because three operational time-based input parameters were used to reflect the acclimation period of microbial communities after the operating conditions were changed. The results of the random forest model showed that the time-based parameter, which was measured from the time of magnetite addition, was the most important input variables. These results suggest the potential of using ML techniques with varied time -based parameters to predict the stability of AD by stimulating DIET
Effect of dynamic friction and static friction in finite element analysis of carbon fiber preform
No full textThis paper aims to improve the accuracy of the finite element analysis for the preform shape transformation according to the difference in the type of carbon fiber and friction force. The carbon fiber type (Toray T700, Zoltek), the fiber direction (0 degrees/90 degrees, +/- 45 degrees), and the frictional force were measured, which occurred between the fiber and the mold and between the fiber and the fiber. A comparative experiment was conducted through actual preform production by designating it as a variable for element analysis. The analysis was conducted according to the difference between the static friction coefficient and the dynamic friction coefficient in the molding analysis. Within the range of the Coulomb equation, the shear angle changing was compared with the actual preform shape to show similar results
Deep-learning-based system-scale diagnosis of a nuclear power plant with multiple infrared cameras
No full textComprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic tech-nology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of ac-cident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.(c) 2022 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
PushPIN: A Pressure-Based Behavioral Biometric Authentication System for Smartwatches
No full textSmartwatches support diverse applications but suffer from security issues due to their limited resources; their small size poorly supports the rich, accurate input required for screen lock authentication. Additionally, traditional approaches to unlocking smart devices, such as Personal identification number, are highly susceptible to attacks such as guessing and video observation. Therefore, we propose PushPIN, a novel scheme that combines knowledge-based and behavioral biometric approaches to increase security. Input symbols are composed of the selection of one of four different targets with one of five different pressure levels, for a total of 20 possibilities. We complement this passcode by capturing behavioral biometric features from screen touches and wrist motion during input. We present two studies to assess the performance of PushPIN. The first assesses both usability and security against a random guessing attack. It shows acceptable usability-recall times of approximately 8 s and no errors-and strong security: equal error rates of 0.51%. The second study examines the resistance of PushPIN against a video observation attack, ultimately revealing that 36.67% of PushPINs could be cracked, performance that represents a substantial improvement over prior work on pressure-based authentication input. We conclude that pressure-based input can increase the security, while maintaining reasonable usability, of smartwatch lock systems