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Color-Tunable and Stable Copper Iodide Cluster Scintillators for Efficient X-Ray Imaging
No full textThe search for color-tunable, efficient, and robust scintillators plays a vital role in the development of modern X-ray radiography. The radioluminescence tuning of copper iodide cluster scintillators in the entire visible region by bandgap engineering is herein reported. The bandgap engineering benefits from the fact that the conduction band minimum and valence band maximum of copper iodide cluster crystals are contributed by atomic orbitals from the inorganic core and organic ligand components, respectively. In addition to high scintillation performance, the as-prepared crystalline copper iodide cluster solids exhibit remarkable resistance toward both moisture and X-ray irradiation. These features allow copper iodide cluster scintillators to show particular attractiveness for low-dose X-ray radiography with a detection limit of 55 nGy s(-1), a value approximate to 100 times lower than a standard dosage for X-ray examinations. The results suggest that optimizing both inorganic core and organic ligand for the building blocks of metal halide cluster crystals may provide new opportunities for a new generation of high-performance scintillation materials
A Ternary Neural Network Computing-in-Memory Processor with 16T1C Bitcell Architecture
No full textA highly energy-efficient Computing-in-Memory (CIM) processor for Ternary Neural Network (TNN) acceleration is proposed in this paper. Previous CIM processors for multi-bit precision neural networks showed low energy efficiency and throughput. Lightweight binary neural networks were accelerated with CIM processors for high energy efficiency but showed poor inference accuracy. In addition, most previous works suffered from poor linearity of analog computing and energy-consuming analog-to-digital conversion. To resolve the issues, we propose a Ternary-CIM (T-CIM) processor with 16T1C ternary bitcell for good linearity with compact area and a charge-based partial sum adder circuit to remove analog-to-digital conversion that consumes a large portion of the system energy. Furthermore, configurable data mapping enables execution of the whole convolution layers with smaller bitcell memory capacity. Designed with 65 nm CMOS technology, the proposed T-CIM achieves 1,316 GOPS of peak performance and 823 TOPS/W of energy efficiency
A Novel Iterative Second-Order Neural-network Learning Control Approach for Robotic Manipulators
No full textIterative Learning Control (ILC) is known as a high-accuracy control strategy for repetitive control missions of mechatronic systems. However, applying such learning controllers for robotic manipulators to result in excellent control performances is now a challenge due to unstable behaviors coming from nonlinearities, uncertainties and disturbances in the system dynamics. To tackle this challenge, in this paper, we present a novel proportional-derivative iterative second-order neural-network learning control (PDISN) method for motion-tracking control problems of robotic manipulators. The control framework is structured from time- and iterative-base control layers. First of all, the total systematic dynamics are concretely stabilized by a conventional Proportional-Derivative (PD) control signal in the time domain. The control objective is then accomplished by using an intelligent ILC decision generated in the second layer to compensate for other nonlinear uncertainties and external disturbances in the dynamics. The iterative signal is flexibly composed from various information on the iterative axis. On one hand, the previous iterative control signal is inherently reused in the current iteration but with an appropriate portion based on reliability of the current control performance. On the other hand, the iterative-based modeling deviation remaining is treated by a functional neural network that is specially activated by a second-order learning law and information synthesized from the current and previous iterations. Stabilities of the time-based nonlinear subsystem and overall system are rigorously analyzed using extended Lyapunov theories and high-order regression series criteria. Effectiveness of the proposed controller was intensively verified by the extensive comparative simulation results. Key advantages of the proposed control method are chattering-free, universal, adaptive, and robust
Machine learning algorithm for early-stage prediction of severe morbidity in COVID-19 pneumonia patients based on bio-signals
No full textBackgroundParalysis of medical systems has emerged as a major problem not only in Korea but also globally because of the COVID-19 pandemic. Therefore, early identification and treatment of COVID-19 are crucial. This study aims to develop a machine-learning algorithm based on bio-signals that predicts the infection three days in advance before it progresses from mild to severe, which may necessitate high-flow oxygen therapy or mechanical ventilation.MethodsThe study included 2758 hospitalized patients with mild severity COVID-19 between July 2020 and October 2021. Bio-signals, clinical information, and laboratory findings were retrospectively collected from the electronic medical records of patients. Machine learning methods included random forest, random forest ranger, gradient boosting machine, and support vector machine (SVM).ResultsSVM showed the best performance in terms of accuracy, kappa, sensitivity, detection rate, balanced accuracy, and run-time; the area under the receiver operating characteristic curve was also quite high at 0.96. Body temperature and SpO(2) three and four days before discharge or exacerbation were ranked high among SVM features.ConclusionsThe proposed algorithm can predict the exacerbation of severity three days in advance in patients with mild COVID-19. This prediction can help effectively manage the reallocation of appropriate medical resources in clinical settings. Therefore, this algorithm can facilitate adequate oxygen therapy and mechanical ventilator preparation, thereby improving patient prognosis, increasing the efficiency of medical systems, and mitigating the damage caused by a global pandemic
Targeted Adsorptive Removal of Nonsteroidal Anti-inflammatory Drugs for Water Purification Using Nanoporous Zr-based Metal-Organic Framework Microcubes
No full textChemically synthesized drugs used in the industry are associated with potential environmental risks. Herein, we report the targeted adsorption of nonsteroidal anti-inflammatory drugs, with and without a carboxylic acid group, using a water-stable Zr-based porphyrinic metal???organic framework (MOF), PCN???224. Nanoporous PCN???224 contains interaction sites for drugs and thus enables the removal of organic pollutants. The removal efficiency of NPX and IBP was 96 and 94%, but PCN???224 removed only 37% of SMX from water. The apparent difference in the adsorption efficiency of PCN???224 indicates that the carboxylic acid functional groups in drugs strongly interacted with the open metal sites of the PCN???224 Zr6 node via coordination bonding, resulting in a high adsorption capacity. This discovery provided a practical approach for the selective adsorptive removal of various other drugs with carboxylate groups
Predatory bacteria as potential biofilm control and eradication agents in the food industry
No full textBiofilms are a major concern within the food industry since they have the potential to reduce productivity in situ (within the field), impact food stability and storage, and cause downstream food poisoning. Within this review, predatory bacteria as potential biofilm control and eradication agents are discussed, with a particular emphasis on the intraperiplasmic Bdellovibrio-and-like organism (BALO) grouping. After providing a brief overview of predatory bacteria and their activities, focus is given to how BALOs fulfill four attributes that are essential for biocontrol agents to be successful in the food industry: (1) Broad spectrum activity against pathogens, both plant and human; (2) Activity against biofilms; (3) Safety towards humans and animals; and (4) Compatibility with food. As predatory bacteria possess all of these characteristics, they represent a novel form of biofilm biocontrol that is ripe for use within the food industry
Intense narrowband terahertz pulses produced by obliquely colliding laser pulses in helium gas
No full textA practical configuration for generating narrowband terahertz (THz) pulses based on plasma dipole oscillations (PDOs) is studied using two-dimensional particle-in-cell simulations. In this scheme, two slightly detuned laser pulses collide obliquely in a helium gas. Plasma strips are generated along the paths of the laser pulses by field ionization. The PDO created in the overlap region of the two laser pulses emits a THz pulse with a peak electric field strength of a few gigavolt per meter. An energy conversion efficiency of 0.542 x 10(-3) is achieved for laser pulse intensities 4.82 x 10(16) W=cm(2), a spot radii of 5lm, and a collision angle of 10.8 degrees. A force balance model is extended for the obliquely colliding configuration of the pulses. As the complications, such as generating plasmas separately or aligning the beams with preformed plasma, are eliminated from our new configuration, this makes a future experimental study of PDO more straightforward
Review of the Weather Hazard Research: Focused on Typhoon, Heavy Rain, Drought, Heat Wave, Cold Surge, Heavy Snow, and Strong Wind
No full textThis paper summarized the research papers on weather extremes that occurred in theRepublic of Korea, which were published in the domestic and foreign journals during 1963~2021.
Weather extreme is defined as a weather phenomenon that causes serious casualty and property loss;here, it includes typhoon, heavy rain, drought, heat wave, cold surge, heavy snow, and strong gust.
Based on the 2011~2020 statistics in Korea, above 80% of property loss due to all natural disasterswere caused by typhoons and heavy rainfalls. However, the impact of the other weather extremescan be underestimated rather than we have actually experienced; the property loss caused by theother extremes is hard to be quantitatively counted. Particularly, as global warming becomes serious,the influence of drought and heat wave has been increasing. The damages caused by coldsurges, heavy snow, and strong gust occurred over relatively local areas on short-term time scalescompared to other weather hazards. In particularly, strong gust accompanied with drought mayresult in severe forest fires over mountainous regions. We hope that the present review paper mayremind us of the importance of weather extremes that directly affect our lives
Self???Formation of a Ru/ZnO Multifunctional Bilayer for the Next???Generation Interconnect Technology via Area???Selective Atomic Layer Deposition
No full textThis study suggests a Ru/ZnO bilayer grown using area-selective atomic layer deposition (AS-ALD) as a multifunctional layer for advanced Cu metallization. As a diffusion barrier and glue layer, ZnO is selectively grown on SiO2, excluding Cu, where Ru, as a liner and seed layer, is grown on both surfaces. Dodecanethiol (DDT) is used as an inhibitor for the AS-ALD of ZnO using diethylzinc and H2O at 120 degrees C. H-2 plasma treatment removes the DDT adsorbed on Cu, forming inhibitor-free surfaces. The ALD-Ru film is then successfully deposited at 220 degrees C using tricarbonyl(trimethylenemethane)ruthenium and O-2. The Cu/bilayer/Si structural and electrical properties are investigated to determine the diffusion barrier performance of the bilayer film. Copper silicide is not formed without the conductivity degradation of the Cu/bilayer/Si structure, even after annealing at 700 degrees C. The effect of ZnO on the Ru/SiO2 structure interfacial adhesion energy is investigated using a double-cantilever-beam test and is found to increase with ZnO between Ru and SiO2. Consequently, the Ru/ZnO bilayer can be a multifunctional layer for advanced Cu interconnects. Additionally, the formation of a bottomless barrier by eliminating ZnO on the via bottom, or Cu, is expected to decrease the via resistance for the ever-shrinking Cu lines
Electrostatic Covalent Organic Frameworks as On-Demand Molecular Traps for High-Energy Li Metal Battery Electrodes
No full textRegulating electrostatic interactions between charged molecules is crucial for enabling advanced batteries with electrochemical reliability. To address this issue, herein, we present a class of electrostatic covalent organic frameworks (COFs) as on-demand molecular traps for high-energy-density Li metal batteries (LMBs). A bipyridine-based COF and its quaternized derivative are synthesized and incorporated into LiNi0.8Co0.1Mn0.1O2 (NCM811) cathodes and Li metal protective layers, respectively. These COF molecular traps are effective in chelating transition metal ions dissolved from the cathodes, enhancing Li+ desolvation, suppressing solvent decomposition, and immobilizing anions of electrolytes. The resulting LMB with the COF molecular traps fully utilizes the theoretical specific capacity of NCM811 at cathodes and allows stable Li plating/ stripping at anodes. A pouch-type LMB full cell with the COF molecular traps provides high gravimetric/volumetric energy densities (466.7 Wh k(gcell)( -1)/1370.1 Wh L-cell(-1)) under a constrained cell configuration, exceeding those of previously reported Li metal batteries based on porous crystalline frameworks