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WoS SCOPUS Document Type Document Title Abstract Authors Affiliation ResearcherID (WoS) AuthorsID (SCOPUS) Author Email(s) Journal Name JCR Abbreviation ISSN eISSN Volume Issue WoS Edition WoS Category JCR Year IF JCR (%) FWCI FWCI Update Date WoS Citation SCOPUS Citation Keywords (WoS) KeywordsPlus (WoS) Keywords (SCOPUS) KeywordsPlus (SCOPUS) Language Publication Stage Publication Year Publication Date DOI JCR Link DOI Link WOS Link SCOPUS Link
Review Recent development patterns, utilization and prospective of biofuel production: Emerging nanotechnological intervention for environmental sustainability - A review The word biofuel is here referred to as liquid or gas fuel mainly derived from biomass for the transport sector. There are many reasons why biofuels are viewed by both developed and industrialized countries as important technologies. These include reasons related to energy supply, climate, foreign exchange savings, and rural socioeconomic issues. The term modern biomass is generally used to describe the traditional use of biomass through effective and clean fuel technologies and a long-term provision of biomass resources as well as environmental and competitive fuel, heating, and power using state-of-the-art conversion technologies. For electricity and heat generation, modern biomass can be used. The most recent biomass-backed transportation fuel is bioethanol and biodiesel as well as diesel generated by synthesis from biomass Fischer-Tropsch. The petroleum additive/substitute is bioethanol. Wood, paint, and even household waste can be processed into bio-ethanol economically. The organic ethanol comes from alcoholic fermentation by hydrolysis process of the saccharides or simple sugars provided by biomass. Starch, sugar or oil-generating crops are currently the basis for the production of transport fuel. The use of vegetable oils for the production of biodiesel has been renewed because of less pollutant and sustainable nature than traditional petroleum diesel. The role of catalysts in biofuel production is highly praised as the rate of conversion and reusability are the major concerns of production economics. Biodiesel is a petroleum-based diesel renewable alternative. Bio-oil production is enabled by biomass energy conversion facilities. In the biomass thermal conversion processes, pyrolysis is the most critical method. A brief overview of the basic concepts involved in biomass fuel thermochemical conversion is reviewed with a major focus on the use of nanotechnologies for biofuel production. High potential heterogeneous nanocatalysts (zinc oxide, silver oxide, doped nanoparticles, alloy nanoparticles, titanium dioxide, magnesium dioxide, iron oxide, and others), as well as their synthesis and characterization have been considered with much emphasis on green production strategies. The share of biomass in the renewable energy sector is around 62%. The key benefit of using biomass energy is reducing greenhouse gas emissions. Manikandan, Sivasubramanian; Subbaiya, Ramasamy; Biruntha, Muniyandi; Krishnan, Radhakrishnan Yedhu; Muthusamy, Govarthanan; Karmegam, Natchimuthu Saveetha Inst Med & Tech Sci SIMATS, Saveetha Sch Engn, Dept Biotechnol, Chennai 602105, Tamil Nadu, India; Copperbelt Univ, Sch Math & Nat Sci, Dept Biol Sci, Jambo Dr,POB 21692, Kitwe, Zambia; Alagappa Univ, Dept Anim Hlth & Management, Karaikkudi 630003, Tamil Nadu, India; Amal Jyothi Coll Engn, Dept Food Technol, Kottayam 686518, Kerala, India; Kyungpook Natl Univ, Dept Environm Engn, Daegu, South Korea; Govt Arts Coll Autonomous, Dept Bot, Salem 636007, Tamil Nadu, India Muniyandi, BIRUNTHA/G-2438-2019; Muthusamy, Govarthanan/C-1491-2014; S, Manikandan/GZM-7135-2022; Krishnan, Radhakrishnan/K-7885-2016; Natchimuthu, Karmegam/J-4745-2019; Govarthanan, Muthusamy/C-1491-2014; Karmegam, Natchimuthu/J-4745-2019; Subbaiya, R/AAR-2948-2021 55213168500; 55263515700; 57203752025; 57198186588; 54881927600; 6506043230 gova.muthu@gmail.com;kanishkarmegam@gmail.com; FUEL FUEL 0016-2361 1873-7153 314 SCIE ENERGY & FUELS;ENGINEERING, CHEMICAL 2022 7.4 13.0 6.27 2025-06-25 62 82 Biofuel; Biomass; Nanoparticles; Hyperthermophilic enzymes; Biodiesel; Transesterification NANOPARTICLES; LIGNOCELLULOSE; BIODIESEL Biodiesel; Biofuel; Biomass; Hyperthermophilic enzymes; Nanoparticles; Transesterification Bioethanol; Biomass; Cerium oxide; Diesel engines; Environmental technology; Ethanol; Gas emissions; Gasoline; Greenhouse gases; II-VI semiconductors; Iron oxides; Magnesia; Nanocatalysts; Reusability; Sustainable development; Synthesis (chemical); TiO2 nanoparticles; Titanium dioxide; Zinc oxide; Bio-mass energy; Biofuel production; Developed countries; Development patterns; Environmental sustainability; Hyperthermophilic; Hyperthermophilic enzyme; Prospectives; Transesterifications; Transport sectors; Biodiesel English 2022 2022-04-15 10.1016/j.fuel.2021.122757 바로가기 바로가기 바로가기 바로가기
Article The urge of algal biomass-based fuels for environmental sustainability against a steady tide of biofuel conflict analysis: Is third-generation algal biorefinery a boon? To meet the rising demand for biofuel, food, and feed, as well as pharmaceuticals, microalgal-based biorefinery systems provide various advantages. Because of the worldwide energy crises, the future of microalgal biorefinery is attentively receiving prominence. Despite being renewable and carbon-neutral, microalgal-based technology produces net CO2 emissions. Due to poor market pricing for renewable fuels, current biomass conversion techniques are neither profitable nor long-term viable. The microalgal strain chosen is critical to the experiment's success. A comprehensive approach that considers all three aspects of environmental sustainability is required to successfully address these challenges. The process should never be jeopardized in any way that threatens its longterm viability. The issue of sustainability must therefore be addressed from the outset of every biorefinery project. It is necessary to investigate genetically altered microalgal strains with improved lipid content, light usage efficiency, pigment accumulation, and other features during the design phase of an algal-based biorefinery, among other things. This is due to the recent drop in crude oil prices, as well as the significant capital and investment costs associated with algae cultivation. Dewatering, harvesting, and lipid recovery must all be researched and developed at a low cost. To solve the problem of decreasing biomass productivities at bigger production scales, a new generation of photobioreactor designs, lighting strategies, and nutrient feed systems are required. To be successful, proponents of large-scale microalgae-based biorefineries must integrate social and sustainability sciences into their commercial plans. The current review explores the potential application of algal biomass for the production of biofuels and bio-based products. The variety of processes and pathways through which bioconversion of algal biomass can be performed are described in this review. Thanigaivel, Sundaram; Vickram, Sundaram; Dey, Nibedita; Gulothungan, Govindarajan; Subbaiya, Ramasamy; Govarthanan, Muthusamy; Karmegam, Natchimuthu; Kim, Woong Saveetha Inst Med & Tech Sci SIMATS, Saveetha Sch Engn, Dept Biotechnol, Chennai 602105, Tamil Nadu, India; Vel Tech Rangarajan Dr Sagunthala R&D Inst Sci &, Dept Elect & Commun Engn, Chennai 600062, Tamil Nadu, India; Copperbelt Univ, Sch Math & Nat Sci, Dept Biol Sci, Jambo Dr,POB 21692, Kitwe, Zambia; Kyungpook Natl Univ, Dept Environm Engn, Daegu, South Korea; Govt Arts Coll Autonomous, Dept Bot, Salem 636007, Tamil Nadu, India ; S, Thanigaivel/H-2860-2016; Muthusamy, Govarthanan/C-1491-2014; S, Vickram/ABG-9459-2020; dey, nibedita/AAG-6776-2021; Subbaiya, R/AAR-2948-2021; Karmegam, Natchimuthu/J-4745-2019; Sundaram, Thanigaivel/H-2860-2016; Govindarajan, GULOTHUNGAN/AAU-7227-2021; Govarthanan, Muthusamy/C-1491-2014; Natchimuthu, Karmegam/J-4745-2019 55624925900; 55257689800; 57216549253; 57200442414; 55263515700; 54881927600; 6506043230; 55581636400 kanishkarmegam@gmail.com;elshine@knu.ac.kr; FUEL FUEL 0016-2361 1873-7153 317 SCIE ENERGY & FUELS;ENGINEERING, CHEMICAL 2022 7.4 13.0 5.18 2025-06-25 48 66 Microalgae; Pyrolysis; Biofuel; Transesterification; Bioconversion WASTE-WATER; MICROALGAE; BIODIESEL; FEEDSTOCK; REMOVAL; ENERGY; POWER Bioconversion; Biofuel; Microalgae; Pyrolysis; Transesterification Bioconversion; Biofuels; Biomass; Costs; Economics; Energy policy; Investments; Microorganisms; Oils and fats; Refining; Strain; Sustainable development; 'current; Algal biomass; Biomass-based fuels; Carbon neutrals; CO 2 emission; Conflicts analysis; Energy crisis; Environmental sustainability; Third generation; Transesterifications; Microalgae English 2022 2022-06-01 10.1016/j.fuel.2022.123494 바로가기 바로가기 바로가기 바로가기
Article Wastewater substrates in microbial fuel cell systems for carbon-neutral bioelectricity generation: An overview Increasing environmental pollution along with fossil fuel depletion is demanding essential energy solutions. Microbial fuel cell (MFC) utilize waste resources containing biodegradable matter that plays a vital role in green "carbon-neutral" bioenergy production. MFC technology involves the multidisciplinary approach of microbiology, electrophysiology, electrochemistry, and process engineering, etc., with vast opportunities and challenges. The substrate is one of the most significant factors influencing the performance of MFC. The shift from synthetic and simple substrates towards different types of industrial wastewater with a higher level of organic matter positively impacts a crucial role in harvesting "green electricity" in MFCs. Some of the ultimate challenges and future perspectives on the energy recovery from the effluents utilized in MFCs are witnessed along with the technological progression over the years. Thulasinathan, Boobalan; Jayabalan, Tamilmani; Arumugam, Nagarajan; Kim, Woong; Kumar, Ponnuchamy; Govarthanan, Muthusamy; Alagarsamy, Arun; Kulanthaisamy, Mohan Rasu Alagappa Univ, Dept Microbiol, Bioenergy & Bioremediat Lab, Karaikkudi 630003, Tamil Nadu, India; Natl Inst Technol Andhra Pradesh, Dept Biotechnol, Tadepalligudem 534102, Andhra Pradesh, India; Indian Inst Technol Madras, Dept Biotechnol, Chennai 600036, Tamil Nadu, India; Kyungpook Natl Univ, Dept Environm Engn, Daegu 41566, South Korea; Alagappa Univ, Dept Anim Hlth & Management, Food Chem & Mol Canc Biol Lab, Sci Campus, Karaikkudi 630003, Tamil Nadu, India Arun, Alagarsamy/F-8986-2019; Govarthanan, Muthusamy/C-1491-2014; Ponnuchamy, Kumar/D-3470-2013; Arun, A./F-8986-2019; Muthusamy, Govarthanan/C-1491-2014 57207255422; 57193253131; 57200188229; 57439446800; 55581636400; 55173720800; 54881927600; 57078221100 arunalacha@gmail.com; FUEL FUEL 0016-2361 1873-7153 317 SCIE ENERGY & FUELS;ENGINEERING, CHEMICAL 2022 7.4 13.0 2.82 2025-06-25 27 36 Microbial fuel cell; Self-sustainable; Bioelectricity; Bioenergy; Wastewater treatment; Electroactive bacteria PSEUDOMONAS-CHLORORAPHIS PCL1391; COMPOSITE FOOD WASTE; ELECTRICITY-GENERATION; ELECTRON-TRANSFER; POWER-GENERATION; SIMULTANEOUS DEGRADATION; BIOHYDROGEN PRODUCTION; SULFIDE OXIDATION; LANDFILL LEACHATE; NITROGEN REMOVAL Bioelectricity; Bioenergy; Electroactive bacteria; Microbial fuel cell; Self-sustainable; Wastewater treatment Bioelectric phenomena; Carbon; Effluents; Electrophysiology; Fossil fuels; Microbial fuel cells; Substrates; Bio-energy; Carbon neutrals; Electro actives; Electroactive bacteria; Energy solutions; Environmental pollutions; Fossil-fuel depletions; Fuel cell system; Self-sustainable; Waste resources; Wastewater treatment English 2022 2022-06-01 10.1016/j.fuel.2022.123369 바로가기 바로가기 바로가기 바로가기
Article Water-mediated adhesion of oil sands on solid surfaces at low temperature Frozen oil sands adhere to the walls of mining fleets in winter, which is an undesirable phenomenon that reduces the delivery capacity of vehicles and wastes fuel for in-land transportation. For automated surface mining, an effective and scalable approach is needed to replace the current method for removing adhered oil sands by shovelling or steam cleaning. In this work, we identify ice formation at the interface of oil sands and the different substrates (steel, rubber, and bitumen/asphaltene coated steel) as the primary reason for the observed high adhesion strength of more than 1,000 kPa; in absence of ice formation, the adhesion strength of oil sand is smaller than 2 kPa. At temperature of -2.5 degrees C to -20 degrees C, the adhesion strength was measured by a force apparatus with thermal control as water content in oil sands matrix was varied from 4 to 14 wt%. The adhesion strength was found to increase linearly with water content. At a fixed water content, the adhesion strength was stronger at a lower temperature after a short freezing time of 5 min. The relationship between water content and the adhesion strength was rationalized by a theoretical model, based on the contact area between ice and the substrate. X-ray micro-computed tomography confirmed that water formed more capillary bridges with the substrate at a higher water content. To effectively reduce the adhesion of oil sands on the substrate, a method was proposed based on anti-icing by spraying a small amount of anti-freezing liquid (0.3 L/m(2)) on the substrate, which effectively reduced the adhesion strength to <2 kPa. The insights and proposed anti-freezing approach in this study may be applied to reducing adhesion of not only frozen oil sands, but wet granular materials in general. Yang, Qimeng; You, Jae Bem; Tian, Boran; Sun, Shaofeng; Daniel, Dan; Liu, Qi; Zhang, Xuehua Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 1H9, Canada; Kyungpook Natl Univ, Dept Chem Engn, Daegu 41566, South Korea; Imperial Oil, Calgary, AB T2C 4P3, Canada; ASTAR, Inst Mat Res & Engn IMRE, Singapore 138634, Singapore ; You, Jae/C-6211-2019; You, Jae Bem/C-6211-2019; Daniel, Dan/M-5386-2013; QI, LIU/IQR-4870-2023; Yang, Qimeng/IWM-6186-2023; Daniel, Dan/N-2836-2019; Zhang, Xuehua/G-1085-2010 57485287800; 55619455300; 57485287900; 57485076000; 56694017100; 57816810100; 8918489500 xuehua.zhang@ualberta.ca; FUEL FUEL 0016-2361 1873-7153 320 SCIE ENERGY & FUELS;ENGINEERING, CHEMICAL 2022 7.4 13.0 0.47 2025-06-25 6 6 Oil sands; Low temperature; Water contents; Anti-fouling ICE ADHESION; THERMAL-CONDUCTIVITY; WETTABILITY; BITUMEN; EXTRACTION; STRESS; DESIGN Anti-fouling; Low temperature; Oil sands; Water contents Adhesion; Bond strength (materials); Computerized tomography; Freezing; Ice; Oil sands; Particle size; Sand; Substrates; Anti-foulings; Anti-freezing; Delivery capacity; Effective approaches; Ice formations; Lows-temperatures; Scalable approach; Solid surface; Vehicle fuels; Waste fuels; Temperature English 2022 2022-07-15 10.1016/j.fuel.2022.123778 바로가기 바로가기 바로가기 바로가기
Article Weight interpretation of artificial neural network model for analysis of rice (Oryza sativa L.) with near-infrared spectroscopy Prediction models for major nutrients of rice were built using near-infrared (NIR) spectral data based on the artificial neural network (ANN). Scientific interpretation of the weight values was proposed and performed to understand the wavenumbers contributing to the prediction of nutrients. NIR spectra were acquired from 110 rice samples. Carbohydrate and moisture contents were predicted with values for the determination coefficient, relative root mean square error, range error ratio, and residual prediction deviation of 0.98, 0.11 %, 44, and 7.3, and 0.97, 0.80 %, 27, and 5.8, respectively. The results agreed well with ones reported in the previous studies and acquired by the conventional partial least squares (PLS)-variable importance in projection method. This study demonstrates that the combination of NIR and ANN is a powerful and accurate tool to monitor nutrients of rice and scientific interpretation of weights can be performed to overcome black box nature of the ANN. Son, Seungwoo; Kim, Donghwi; Choi, Myoung Choul; Lee, Joonhee; Kim, Byungjoo; Choi, Chang Min; Kim, Sunghwan Kyungpook Natl Univ, Dept Chem, Daegu 41566, South Korea; Korea Inst Ocean Sci & Technol, Oil & POPs Res Grp, Geoje 53201, South Korea; Korea Basic Sci Inst, Ctr Sci Instrumentat, Cheongju 28119, South Korea; Korea Res Inst Stand & Sci, Div Chem & Biol Metrol, Organ Metrol Grp, Daejeon 34113, South Korea; Mass Spectrometry Convergence Res Ctr, Daegu 41566, South Korea; Green Nano Mat Res Ctr, Daegu 41566, South Korea ; Choi, Chang Min/HPE-9467-2023; Kim, Sunghwan/HKN-9812-2023 57206473214; 58839420600; 25633658300; 55689961300; 55543775200; 57278941000; 57203772967 sunghwank@knu.ac.kr; FOOD CHEMISTRY-X FOOD CHEM X 2590-1575 15 SCIE CHEMISTRY, APPLIED 2022 6.1 13.0 1.1 2025-06-25 16 14 Artificial neural network; Prediction model; Rice; Nutrients; Near-infrared spectroscopy; Partial least squares NIRS Artificial neural network; Near-infrared spectroscopy; Nutrients; Partial least squares; Prediction model; Rice Forecasting; Infrared devices; Least squares approximations; Mean square error; Near infrared spectroscopy; Neural networks; carbohydrate; Artificial neural network modeling; Near infrared spectra; Near infrared spectral; Partial least-squares; Prediction modelling; Rice; Rice (Oryza sativa L.); Spectral data; Wave numbers; Weight values; Article; artificial neural network; carbohydrate analysis; fat content; feed forward neural network; moisture; near infrared spectroscopy; nutrient; partial least squares regression; predictive model; protein content; rice; root mean squared error; weight; Nutrients English 2022 2022-10-30 10.1016/j.fochx.2022.100430 바로가기 바로가기 바로가기 바로가기
Article Dark-Mode Human-Machine Communication Realized by Persistent Luminescence and Deep Learning Increasing ubiquitous collaborative intelligence between humans and machines requires human-machine communication (HMC) that is more human and less machine-like to accomplish given tasks. Although speech signals are considered the best modes of communication in HMC, background noise often interferes with these signals. Therefore, research focused on integrating lip-reading technology into HMC has gained significant attention. However, lip-reading functions effectively only in well-lit environments. In contrast, HMC may occur daily in dark environments owing to potential energy shortages, increased exploration in darkness, nighttime emergencies, etc. Herein, a possible method for HMC in the dark mode is presented, which is realized based on deep learning motion patterns of persistent luminescence (PL) of the skin surrounding the lips. An ultrasoft PL-polymer composite patch is used to record the motion pattern of the skin during speech in the dark. It is found that visual geometric group network (VGGNET-5) and residual neural network (ResNet-34) could predict spoken words in darkness with test accuracies of 98.5% and 98.75%, respectively. Furthermore, these models could effectively distinguish similar-sounding words such as "around" and "ground." Dark-mode communication can allow a wide range of people, including disabled people with limited dexterity and voice tremors, to communicate with artificial intelligence machines. Timilsina, Suman; Shin, Ho Geun; Sohn, Kee-Sun; Kim, Ji Sik Kyungpook Natl Univ, Sch Nano & Adv Mat Engn, 2559 Gyeongsang Daero, Sangju Si 37224, Gyeongsangbuk D, South Korea; Kyungpook Natl Univ, Dept Adv Sci & Technol Convergence, 2559 Gyeongsang Daero, Sangju Si 37224, Gyeongsangbuk D, South Korea; Sejong Univ, Nanotechnol & Adv Mat Engn, 209 Neungdong Ro, Seoul 143747, South Korea Timilisina, Suman/GLT-8029-2022 sumantimilsina87@knu.ac.kr;jisikkim@knu.ac.kr; ADVANCED INTELLIGENT SYSTEMS ADV INTELL SYST-GER 2640-4567 4 7 SCIE AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ROBOTICS 2022 7.4 13.1 8 dark-mode human-machine communication; deep learning; human-machine communication; luminescence; visual speech recognition ARTIFICIAL-INTELLIGENCE; PHOSPHOR English 2022 2022-07 10.1002/aisy.202200036 바로가기 바로가기 바로가기
Article Highly Reliable Synaptic Cell Array Based on Organic-Inorganic Hybrid Bilayer Stack toward Precise Offline Learning As the use of artificial intelligence (AI) soars, the development of novel neuromorphic computing is demanding because of the disadvantages of the von Neumann architecture. Furthermore, extensive research on electrochemical metallization (ECM) memristors as synaptic cells have been carried out toward a linear conductance update for online learning applications. In most cases, however, a conductance distribution change over time has not been studied as a major issue, giving less consideration to inference-only computing accelerators based on offline learning. Herein, organic-inorganic bilayer stacking for synaptic unit cells using poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3) and Al2O3 thin films is suggested, showing highly enhanced reliability for offline learning. The bilayer structure achieves better reliability and control of the analog resistive switching and synaptic functions, respectively, through the guided formation of conductive filaments via tip-enhanced electric fields. In addition, 5-bit multilevel states achieve long-term stability (>10(4) s) following an in-depth study on conductance-level stability. Finally, a device-to-system-level simulation is performed by building a convolutional neural network (CNN) based on the hybrid devices. This highlighted the significance of multilevel states in fully connected layers. It is believed that the study provides a practical approach to using ECM-based memristors for inference-only neural network accelerators. Cha, Jun-Hwe; Jang, Byung Chul; Oh, Jungyeop; Lee, Changhyeon; Yang, Sang Yoon; Park, Hamin; Im, Sung Gap; Choi, Sung-Yool Korea Adv Inst Sci & Technol KAIST, Ctr Adv Mat Discovery 3D Displays, Graphene 2D Mat Res Ctr, Sch Elect Engn, 291 Daehak Ro, Daejeon 34141, South Korea; Kyungpook Natl Univ, Sch Elect Engn, 80 Daehakro, Daegu 41566, South Korea; Kyungpook Natl Univ, Sch Elect & Elect Engn, 80 Daehakro, Daegu 41566, South Korea; Korea Adv Inst Sci & Technol KAIST, Dept Chem & Biomol Engn, 291 Daehak Ro, Daejeon 34141, South Korea; Kwangwoon Univ, Dept Elect Engn, 20 Gwangun Ro, Seoul 01897, South Korea Im, Sung/C-1823-2011; Jang, Byung Chul/GYV-0656-2022; Choi, Sung-Yool/B-3383-2012 sungyool.choi@kaist.ac.kr; ADVANCED INTELLIGENT SYSTEMS ADV INTELL SYST-GER 2640-4567 4 6 SCIE AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ROBOTICS 2022 7.4 13.1 9 5-bit multilevel retention; conductive-bridging random-access memory (CBRAM); fine-tuning; neuromorphic computing; offline learning MEMRISTOR DEVICE; BEHAVIORS; OXIDATION; ENDURANCE; LAYER English 2022 2022-06 10.1002/aisy.202200018 바로가기 바로가기 바로가기
Article Memristor-Based Security Primitives Robust to Malicious Attacks for Highly Secure Neuromorphic Systems Internet-of-things (IoT) edge devices with a memristive neuromorphic system can more effectively enhance daily lives. However, cyberattacks remain critical concerns for smart IoT edge devices that process a vast body of information via networks. Herein, a highly secure neuromorphic system is reported, which can be implemented using a physically unclonable function (PUF) that exploits the high entropy achieved via the stochastic switching of a poly(1,3,5-trivinyl-1,3,5-trimethyl cyclotrisiloxane) (pV3D3)-based memristor. The excellent insulating property of pV3D3 enhances the stochasticity of the tunneling distance for randomly ruptured Cu filaments. The pV3D3 memristor-based PUF (pV3D3-PUF) achieves near-ideal 50% averages for uniformity and uniqueness, excellent reliability under conditions of mechanical stress and water immersion, and reconfigurability-bolstering security without additional hardware. Using stochastic in-memory computing, the pV3D3-PUF shows resilience to machine learning attacks. Furthermore, a cryptography protocol is demonstrated, which enables artificial intelligence service implementation without security issues for PUF-integrated pV3D3 memristor-based neuromorphic systems. Oh, Jungyeop; Kim, Sungkyu; Choi, Junhwan; Cha, Jun-Hwe; Im, Sung Gap; Jang, Byung Chul; Choi, Sung-Yool Korea Adv Inst Sci & Technol KAIST, Graphene 2D Mat Res Ctr, Sch Elect Engn, 291 Daehak Ro, Daejeon 34141, South Korea; Sejong Univ, Dept Nanotechnol & Adv Mat Engn, 209 Neungdong Ro, Seoul 05006, South Korea; Korea Adv Inst Sci & Technol KAIST, Graphene 2D Mat Res Ctr, Dept Chem & Biomol Engn, 291 Daehak Ro, Daejeon 34141, South Korea; Kyungpook Natl Univ, Sch Elect Engn, 80 Daehakro, Daegu 41566, South Korea; Kyungpook Natl Univ, Sch Elect & Elect Engn, 80 Daehakro, Daegu 41566, South Korea Jang, Byung Chul/GYV-0656-2022; Im, Sung/C-1823-2011; Kim, Sung Kyu/GSI-4036-2022; Choi, Sung-Yool/B-3383-2012 bc.jang@knu.ac.kr;sungyool.choi@kaist.ac.kr; ADVANCED INTELLIGENT SYSTEMS ADV INTELL SYST-GER 2640-4567 4 11 SCIE AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ROBOTICS 2022 7.4 13.1 5 cryptography; machine learning attacks; memristors; neuromorphic systems; physical unclonable functions MEMORY ARRAY English 2022 2022-11 10.1002/aisy.202200177 바로가기 바로가기 바로가기
Article Systematic Engineering of Metal Ion Injection in Memristors for Complex Neuromorphic Computing with High Energy Efficiency Neuromorphic electronics attract significant attention as a new computing architecture. Despite much effort for achieving practical neuromorphic systems, it is still challenging to construct a synapse array ideal for complex neural networks. Herein, a novel strategy for developing a highly integrated crossbar array of a one-selector-one-memory (1S-1R) synapse by systematically engineering ion injection is demonstrated. In the proposed synapse, an electrochemical metallization (ECM) memristor consisting of unstable filaments and a typical ECM device with stable filaments act as a selector with a low leakage current and a stable memory device, respectively. To overcome the voltage-matching issues in constructing the 1S-1R synapse with high integration density, ion injection related with the electrical properties is optimized in the ECM devices via the distribution of active metal nanoparticles at the interface. The developed synapse possesses a high on/off ratio, superior selectivity, low operating current, and stable multilevel conductance, compared to the previously reported devices. High feasibility for complex neuromorphic systems is demonstrated, and the neural network based on the developed synapse array exhibits reliable parallel computation with high energy efficiency. This promising concept of realizing complex neuromorphic electronics is a fundamental building block for the practical artificial intelligence. Kim, Seong Eun; Kim, Min-Hwi; Jang, Jisu; Kim, Hyungjin; Kim, Sungjun; Jang, Jaewon; Bae, Jin-Hyuk; Kang, In Man; Lee, Sin-Hyung Kyungpook Natl Univ, Sch Elect Engn, 80 Daehak Ro, Daegu 702701, South Korea; Kyungpook Natl Univ, Sch Elect & Elect Engn, 80 Daehak Ro, Daegu 702701, South Korea; Seoul Natl Univ, Sch Elect & Comp Engn, 1 Gwanak Ro, Seoul 08826, South Korea; Yonsei Univ, Dept Mat Sci & Engn, Seoul 03722, South Korea; Dongguk Univ, Div Elect & Elect Engn, Seoul 04620, South Korea Lee, Sin-Hyung/ABD-6425-2022 sinhlee@knu.ac.kr; ADVANCED INTELLIGENT SYSTEMS ADV INTELL SYST-GER 2640-4567 4 9 SCIE AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ROBOTICS 2022 7.4 13.1 25 artificial synapses; memristors; neural networks; one selector-one memory; parallel computation BIPOLAR SELECTOR; SYNAPSE English 2022 2022-09 10.1002/aisy.202200110 바로가기 바로가기 바로가기
Article Beam-spin asymmetry Σ for Σ⁻ hyperon photoproduction off the neutron We report a new measurement of the beam-spin asymmetry, Sigma, for the (gamma) over right arrown -> K+Sigma(-) reaction using quasi-free neutrons in a liquid-deuterium target. The new dataset includes data at previously unmeasured photon energy and angular ranges, thereby providing new constraints on partial wave analyses used to extract properties of the excited nucleon states. The experimental data were obtained using the CEBAF Large Acceptance Spectrometer (CLAS), housed in Hall B of the Thomas Jefferson National Accelerator Facility (JLab). The CLAS detector measured reaction products from a liquid-deuterium target produced by an energy-tagged, linearly polarised photon beam with energies in the range 1.1 to 2.3 GeV. Predictions from an isobar model indicate strong sensitivity to N(1720)3/2(+), Delta(1900)1/2(-), and N(1895)1/2(-), which corroborates results from a recent combined analysis of all K Sigma channels. When our data are incorporated in the fits of partial-wave analyses, one observes significant changes in gamma-n couplings of resonances which have small branching ratios to the pi N channel. (c) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP(3). Zachariou, N.; Munevar, E.; Berman, B. L.; Bydzovsky, P.; Cieply, A.; Feldman, G.; Ilieva, Y.; Nadel-Turonski, P.; Skoupil, D.; Sarantsev, A., V; Watts, D. P.; Amaryan, M. J.; Angelini, G.; Armstrong, W. R.; Atac, H.; Avakian, H.; Barion, L.; Bashkanov, M.; Battaglieri, M.; Bedlinskiy, I; Benmokhtar, F.; Bianconi, A.; Biondo, L.; Biselli, A. S.; Bondi, M.; Bossu, F.; Boiarinov, S.; Briscoe, W. J.; Brooks, W. K.; Bulumulla, D.; Burkert, V. D.; Carman, D. S.; Carvajal, J. C.; Celentano, A.; Chatagnon, P.; Chetry, T.; Ciullo, G.; Clark, L.; Cole, P. L.; Contalbrigo, M.; Costantini, G.; Crede, V; D'Angelo, A.; Dashyan, N.; De Vita, R.; Defurne, M.; Deur, A.; Diehl, S.; Djalali, C.; Dupre, R.; Dugger, M.; Egiyan, H.; Ehrhart, M.; El Alaoui, A.; El Fassi, L.; Eugenio, P.; Fedotov, G.; Fegan, S.; Filippi, A.; Fradi, A.; Gavalian, G.; Gilfoyle, G. P.; Girod, F. X.; Gleason, C.; Golubenko, A. A.; Gothe, R. W.; Griffioen, K. A.; Guidal, M.; Hafidi, K.; Hakobyan, H.; Hattawy, M.; Hayward, T. B.; Heddle, D.; Hicks, K.; Hobart, A.; Holtrop, M.; Ireland, D. G.; Isupov, E. L.; Jenkins, D.; Jo, H. S.; Joo, K.; Keller, D.; Khanal, A.; Khandaker, M.; Kim, A.; Klein, F. J.; Kripko, A.; Kubarovsky, V; Lanza, L.; Leali, M.; Livingston, K.; MacGregor, I. J. D.; Marchand, D.; Markov, N.; Marsicano, L.; Mascagna, V; McKinnon, B.; Migliorati, S.; Mineeva, T.; Mirazita, M.; Mokeev, V; Camacho, C. Munoz; Neupane, K.; Niccolai, S.; Niculescu, G.; O'Connell, T. R.; Osipenko, M.; Ostrovidov, A., I; Pandey, P.; Paolone, M.; Pappalardo, L. L.; Paremuzyan, R.; Pasyuk, E.; Phelps, W.; Pogorelko, O.; Price, J. W.; Prok, Y.; Raue, B. A.; Ripani, M.; Ritman, J.; Rizzo, A.; Rosner, G.; Rowley, J.; Sabatie, F.; Salgado, C.; Schmidt, A.; Schumacher, R. A.; Sharabian, Y. G.; Shirokov, E., V; Shrestha, U.; Sokhan, D.; Soto, O.; Sparveris, N.; Stepanyan, S.; Stoler, P.; Strakovsky, I. I.; Strauch, S.; Tyson, R.; Ungaro, M.; Venturelli, L.; Voskanyan, H.; Vossen, A.; Voutier, E.; Wei, K.; Wei, X.; Wishart, R.; Wood, M. H.; Yale, B.; Zhang, J.; Zhao, Z. W. Argonne Natl Lab, Argonne, IL 60439 USA; Arizona State Univ, Tempe, AZ 85287 USA; Calif State Univ Dominguez Hills, Carson, CA 90747 USA; Canisius Coll, Buffalo, NY 14208 USA; Carnegie Mellon Univ, Pittsburgh, PA 15213 USA; Catholic Univ Amer, Washington, DC 20064 USA; Univ Paris Saclay, CEA, IRFU, F-91191 Gif Sur Yvette, France; SUNY Stony Brook, CFNS, Stony Brook, NY 11794 USA; Christopher Newport Univ, Newport News, VA 23606 USA; Duke Univ, Durham, NC 27708 USA; Duquesne Univ, 600 Forbes Ave, Pittsburgh, PA 15282 USA; Fairfield Univ, Fairfield, CT 06824 USA; Florida Int Univ, Miami, FL 33199 USA; Florida State Univ, Tallahassee, FL 32306 USA; George Washington Univ, Washington, DC 20052 USA; Univ Bonn, Helmholtz Inst Strahlen & Kernphys, D-53115 Bonn, Germany; Ist Nazl Fis Nucl, Sez Catania, I-95123 Catania, Italy; Ist Nazl Fis Nucl, Sez Ferrara, I-44100 Ferrara, Italy; Ist Nazl Fis Nucl, Lab Nazl Frascati, I-00044 Frascati, Italy; Ist Nazl Fis Nucl, Sez Genova, I-16146 Genoa, Italy; Ist Nazl Fis Nucl, Sez Roma Tor Vergata, I-00133 Rome, Italy; Ist Nazl Fis Nucl, Sez Torino, I-10125 Turin, Italy; Ist Nazl Fis Nucl, Sez Pavia, I-27100 Pavia, Italy; Univ Paris Saclay, IJCLab, CNRS IN2P3, F-91405 Orsay, France; Inst Kernphys Juelich, Julich, Germany; James Madison Univ, Harrisonburg, VA 22807 USA; Kyungpook Natl Univ, Daegu 41566, South Korea; Lamar Univ, 4400 MLK Blvd,POB 10046, Beaumont, TX 77710 USA; Mississippi State Univ, Mississippi State, MS 39762 USA; Natl Res Ctr Kurchatov Inst ITEP, Moscow 117259, Russia; Czech Acad Sci, Nucl Phys Inst, Rez 25068, Czech Republic; Univ New Hampshire, Durham, NH 03824 USA; New Mexico State Univ, POB 30001, Las Cruces, NM 88003 USA; Norfolk State Univ, Norfolk, VA 23504 USA; Ohio Univ, Athens, OH 45701 USA; Old Dominion Univ, Norfolk, VA 23529 USA; Univ Giessen, Phys Inst 2, D-35392 Giessen, Germany; Rensselaer Polytech Inst, Troy, NY 12180 USA; Lomonosov Moscow State Univ, Skobeltsyn Inst Nucl Phys, Moscow 119234, Russia; Temple Univ, Philadelphia, PA 19122 USA; Thomas Jefferson Natl Accelerator Facil, Newport News, VA 23606 USA; Union Coll, Schenectady, NY 12308 USA; Univ Connecticut, Storrs, CT 06269 USA; Univ Dist Francisco Jose de Caldas, Bogota, Colombia; Univ Tecn Federico Santa Maria, Casilla 110-V, Valparaiso, Chile; Univ Ferrara, I-44121 Ferrara, Italy; Univ Brescia, I-25123 Brescia, Italy; Univ Insubria, I-22100 Como, Italy; Univ Messina, I-98166 Messina, Italy; Univ Roma Tor Vergata, I-00133 Rome, Italy; Univ Gabes, Gabes 6072, Tunisia; Univ Glasgow, Glasgow G12 8QQ, Lanark, Scotland; Univ Richmond, Richmond, VA 23173 USA; Univ South Carolina, Columbia, SC 29208 USA; Univ York, York YO10 5DD, N Yorkshire, England; Virginia Tech, Blacksburg, VA 24061 USA; Univ Virginia, Charlottesville, VA 22901 USA; Coll William & Mary, Williamsburg, VA 23187 USA; Yerevan Phys Inst, Yerevan 375036, Armenia; Idaho State Univ, Pocatello, ID 83209 USA ; Tyson, Richard/LRC-4125-2024; Schumacher, Reinhard/K-6455-2013; Sparveris, Nikolaos/C-4751-2008; Zhao, Zhi-Wen/HZI-5398-2023; Hyde, Charles/W-9190-2018; Holtrop, Maurik/A-9017-2010; Filippi, Alessandra/AAE-9322-2020; Deur, Alexandre/H-9778-2019; Bashkanov, Mikhail/R-1333-2018; MacGregor, Ian/D-4072-2011; Battaglieri, Marco/I-6262-2018; Isupov, Evgeny/J-2976-2012; Lanza, Lucilla/E-6479-2017; Costantini, Giulio/F-3829-2018; Sarantsev, Andrey/R-6057-2016; Brooks, William/C-8636-2013; Ireland, David/E-8618-2010; McKinnon, Bryan/J-2928-2018; Bozzi, Giuseppe/H-7283-2017; Celentano, Andrea/JFJ-2728-2023; D'Angelo, Annalisa/A-2439-2012; Burkert, Volker/AAF-7395-2020; Adikaram, Dasuni/D-1539-2016; Mineeva, Taisiya/MDT-1592-2025; Sabatie, Franck/K-9066-2015; Dugger, Michael/AAR-5206-2021; Bydzovsky, Petr/G-8600-2014; Osipenko, Mikhail/N-8292-2015; Cieply, Ales/H-2970-2014; Marsicano, Luca/KPB-4594-2024; Khanal, Aaditya/ABI-5610-2020; Alaoui, Ahmed/B-4638-2015; Filippi, Alessandra/I-9530-2012; Mascagna, Valerio/HLQ-1103-2023; Zhang, Jixie/A-1461-2016; Pappalardo, Luciano/AAB-2380-2021; Jo, Hyon-Suk/HGC-7070-2022; Hakobyan, Hayk/JUF-6461-2023 36836386600; 16241887900; 16018872700; 8367533200; 55905698900; 36004100800; 35227424100; 6603294089; 55550315900; 59113982100; 7201539565; 35277104000; 57193121212; 57221249616; 57210826461; 7006613415; 23033257000; 6506107717; 7004520678; 35277104100; 57218527298; 7102358422; 57226649056; 35227021700; 54398256600; 35725064700; 35277071300; 7005532059; 35400106000; 57218357843; 7004440244; 7005853901; 57214364802; 54392656300; 57202987431; 57189889203; 6603765308; 36022213600; 35227101500; 7003468594; 57221147612; 6602900241; 55828029600; 6507987909; 59345445900; 56272524200; 6604025441; 57217562965; 35374416600; 35069234100; 35227159100; 35227171500; 57193421717; 26535686800; 14041647600; 9845148400; 57215092344; 56978985100; 8695796100; 35276927000; 35227304900; 6603686320; 35227280900; 42661299500; 57210932456; 7102183142; 57217000308; 7003432409; 34570410000; 57208726428; 56115055200; 57206656408; 7003821864; 22966851900; 57214681432; 7005060869; 57209456981; 35227460400; 57237808500; 35227429400; 57202638465; 57216594895; 57208691543; 13405022500; 36604596000; 57214026240; 57205462419; 6701392158; 56047689500; 6507646370; 9278396500; 7006040977; 7004889588; 35227617100; 57193833269; 22135531000; 35227669300; 57224626542; 26023453000; 35227656900; 7004546205; 12244632700; 57212715831; 35227746500; 35227763200; 57197510744; 6701825145; 6603112367; 57226647977; 22986163400; 7004207376; 36085149700; 35227791700; 56362788600; 8903140900; 55329126900; 35227871000; 7004527121; 7003515879; 6701495633; 56589489300; 7102538331; 57214597279; 35227896300; 35228024200; 57212416423; 7201653195; 35227996900; 15030349100; 57213706614; 16065283100; 55787422600; 6507906118; 7004491103; 8331193400; 22969481600; 7004321986; 57222078258; 35228099400; 22136651400; 6504161736; 36934412800; 6603350317; 57221067333; 13204321200; 57222271505; 57201559118; 57204955185; 57215210642; 57216598335 nicholas@jlab.org; PHYSICS LETTERS B PHYS LETT B 0370-2693 1873-2445 827 SCIE ASTRONOMY & ASTROPHYSICS;PHYSICS, NUCLEAR;PHYSICS, PARTICLES & FIELDS 2022 4.3 13.2 0.91 2025-06-25 9 10 QUARK-MODEL; OBSERVABLES; BARYONS English 2022 2022-04-10 10.1016/j.physletb.2022.136985 바로가기 바로가기 바로가기 바로가기
Article Composite Guidance for Impact Time Control Under Physical Constraints This article presents a composite guidance law comprising a three-stage proportional navigation (PN) structure for the simultaneous arrival of multiple missiles. Motivated by the fact that the selection of the navigation constant considerably influences the trajectory shape, the proposed composite law is designed to have different navigation constants at each stage to achieve the desired impact time. The three stages of the proposed law are configured to sequentially increase, maintain, and decrease the magnitude of the look angle by switching only the navigation constant, through which the seeker's field-of-view constraint, which is mainly determined by the look angle, can be satisfied without additional considerations. Furthermore, the simple PN-based structure of the proposed law makes it possible to obtain an exact closed-loop solution, providing various useful information for practical implementation, such as the expected trajectory, required guidance command, and range of achievable impact time. In addition, the relevant investigations based on the theoretical analysis as well as the demonstration results obtained through the numerical simulation are presented herein. Kim, Hyeong-Geun; Lee, Hyeonbeom Incheon Natl Univ, Dept Mech Engn, Incheon 22012, South Korea; Kyungpook Natl Univ, Dept Elect Engn, Daegu 41566, South Korea 56029058100; 55556473400 hgkim@inu.ac.kr;hbeomlee@knu.ac.kr; IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS IEEE T AERO ELEC SYS 0018-9251 1557-9603 58 2 SCIE ENGINEERING, AEROSPACE;ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS 2022 4.4 13.2 1.06 2025-06-25 11 11 Missiles; Navigation; Trajectory; Switches; Aerodynamics; Estimation; Shape; Closed-loop analysis; impact time control; physical constraint; proportional navigation (PN) guidance LAW; ANGLE; PURE Closed-loop analysis; impact time control; physical constraint; proportional navigation (PN) guidance Electronic guidance systems; Closed-loop analysis; Guidance laws; Impact time; Impact time control; Multiple missiles; Navigation structures; Physical constraints; Proportional navigation; Time control; Trajectory shapes; Navigation English 2022 2022-04 10.1109/taes.2021.3119759 바로가기 바로가기 바로가기 바로가기
Article Data-driven sequence labeling methods incorporating the long-range spatial variation of geological data for lithofacies sequence estimation The use of geophysical well-log data for interpreting the stratigraphic lithofacies sequence is cost effective. In this study, several data-driven lithofacies sequence estimation models are developed, where long- and short-term memory (LSTM) and bidirectional LSTM (BLSTM) are applied to efficiently complement the long-range spatial variation of successive well-log and lithofacies measurements. During the development, the models using the autoregressive (AR) input variables of lithofacies are designed to incorporate the lithofacies sequence pattern into the estimation. The performances of the proposed models are comparatively validated with an artificial deep neural network (DNN)-based model that does not consider long-range variation. Accordingly, a total of six estimation models are examined: DNN, AR-DNN, LSTM, AR-LSTM, BLSTM, and AR-BLSTM. For model implementation, synthetic data and actual data acquired from the Satyr-5 well in Western Australia are used. For the synthetic data, the results indicate that the incorporation of nonstationary statistical information improves the performance of BLSTM-based models. In addition, AR-input information is effective with respect to the estimation of the vertical thickness of lithofacies. The advantage of using AR inputs can also be observed for actual data, where AR-based models perform significantly better than the other models. Quantitatively speaking, the fitness of DNN-, LSTM-, and BLSTM-based models is 81.79 %, 84.53 %, and 85.08 %, respectively, whereas that of AR-DNN-, AR-LSTM-, and AR-BLSTM-based models is 85.43 %, 85.72 %, and 86.56 %, respectively. The proposed models are expected to be useful with respect to interpreting the heterogeneity of lithofacies distribution in a cost-effective and computationally efficient way. Particularly, BLSTM-based models are widely applicable because they perform well regardless the spatial statistics of lithofacies sequences. Park, Gyeong-Tae; Jeong, Jina; Emelyanova, Irina; Pervukhina, Marina; Esteban, Lionel; Yun, Seong-Taek Kyungpook Natl Univ, Dept Geol, Daegu, South Korea; CSIRO, Energy, Perth, WA, Australia; Korea Univ, Earth & Environm Sci, Seoul, South Korea Emelyanova, Irina/F-7743-2011 57226594931; 55488558800; 23501352100; 24339119300; 16039005100; 57221362072 jeong.j@knu.ac.kr; JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING J PETROL SCI ENG 0920-4105 1873-4715 208 SCIE ENERGY & FUELS;ENGINEERING, PETROLEUM 2022 4.4 13.2 0.24 2025-06-25 4 3 Lithofacies sequence estimation; Geophysical well-log data; Long-and short-term memory; Bidirectional long- and short-term memory; A long-range spatial information; Autoregressive lithofacies WELL LOG A long-range spatial information; Autoregressive lithofacies; Bidirectional long- and short-term memory; Geophysical well-log data; Lithofacies sequence estimation; Long- and short-term memory Australia; Western Australia; Cost effectiveness; Deep neural networks; Long short-term memory; Stratigraphy; Well logging; A long-range spatial information; Auto-regressive; Autoregressive lithofacies; Bidirectional long-term and short-term memory; Geophysical well-log data; Lithofacies; Lithofacies sequence estimation; Long and short term memory; Long-term and short-term memory; Neural-networks; artificial neural network; comparative study; estimation method; lithofacies; performance assessment; sequence stratigraphy; spatial variation; well logging; Brain English 2022 2022-01 10.1016/j.petrol.2021.109345 바로가기 바로가기 바로가기 바로가기
Article Electrochemical oxidation of sodium dodecylbenzenesulfonate in Pt anodes with Y2O3 particles The electrochemical oxidation process has been widely studied in the field of wastewater treatment for the decomposition of organic materials through oxidation using center dot OH generated on the anode. Pt anode electrodes with high durability and long-term operability have a low oxygen evolution potential, making them unsuitable for electrochemical oxidation processes. Therefore, to apply Pt electrodes that are suitable for long-term operation and large-scale processes, it is necessary to develop a new method for improving the decomposition rate of organic materials. This study introduces a method to improve the decomposition rate of organic materials when using a Pt anode electrode in the electrochemical oxidation process for the treatment of organic decontamination liquid waste. Electrochemical decom-position tests were performed using sodium dodecylbenzenesulfonate (SDBS) as a representative organic material and a Pt mesh as the anode electrode. Y2O3 particles were introduced into the electrolytic cell to improve the decomposition rate. The decomposition rate significantly improved from 21% to 99%, and the current efficiency also improved. These results can be applied to the electrochemical oxidation process without additional system modification to enhance the decomposition rate and current efficiency. (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/). Choi, Jung-Hoon; Lee, Byeonggwan; Lee, Ki-Rak; Kang, Hyun Woo; Eom, Hyeon Jin; Shin, Seong-Sik; Kim, Ga-Yeong; Park, Geun-Il; Park, Hwan-Seo Korea Atom Energy Res Inst, Radioact Waste Treatment Res Team, 111 Daedeok-daero, 989, Daejeon, South Korea; Kyungpook Natl Univ, Dept Hydrogen & Renewable Energy, 80 Daehak ro, Daegu 41566, South Korea; Chungnam Natl Univ, Dept Environm Engn, 99 Daehak ro, Daejeon 34134, South Korea Lee, Byeonggwan/HPD-2363-2023; Kim, Ga-yeong/HPE-4623-2023 56227488100; 57201269846; 14420116600; 55128039500; 57222569272; 57765349600; 57766427400; 7403041382; 56175751900 mrchoijh@kaeri.re.kr; NUCLEAR ENGINEERING AND TECHNOLOGY NUCL ENG TECHNOL 1738-5733 54 12 SCIE NUCLEAR SCIENCE & TECHNOLOGY 2022 2.7 13.2 0.32 2025-06-25 4 3 Electrochemical oxidation process; Decomposition; Organic material; Wastewater treatment; Organic decontamination; Liquid waste; Y-2 O-3 particle; Sodium dodecylbenzenesulfonate WASTE-WATER TREATMENT; ANIONIC SURFACTANT; ELECTRODES; DEGRADATION Decomposition; Electrochemical oxidation process; Liquid waste; Organic decontamination; Organic material; Sodium dodecylbenzenesulfonate; Wastewater treatment; Y<sub>2</sub>O<sub>3</sub> particle English 2022 2022-12 10.1016/j.net.2022.08.007 바로가기 바로가기 바로가기 바로가기
Article Evidence for WW/WZ vector boson scattering in the decay channel lνqq produced in association with two jets in proton-proton collisions at √s=13TeV Evidence is reported for electroweak (EW) vector boson scattering in the decay channel l nu qq of two weak vector bosons WV(V = Wor Z), produced in association with two parton jets. The search uses a data set of proton-proton collisions at 13TeVcollected with the CMS detector during 2016-2018 with an integrated luminosity of 138fb(-1). Events are selected requiring one lepton (electron or muon), moderate missing transverse momentum, two jets with a large pseudorapidity separation and a large dijet invariant mass, and a signature consistent with the hadronic decay of a W/Zboson. The cross section is computed in a fiducial phase space defined at parton level requiring all parton transverse momenta p(T)> 10 GeVand at least one pair of outgoing partons with invariant mass mqq> 100 GeV. The measured and expected EW WVproduction cross sections are 1.90(-0.46)(+0.5)3 pb and 2.23(-0.11)(+0.08)(scale) +/- 0.05(PDF) pb, respectively, where PDF is the parton distribution function. The observed EW signal strength is mu EW= 0.85 +/- 0.12 (stat)(-0.17)(+0.19)(syst), corresponding to a signal significance of 4.4 standard deviations with 5.1 expected, and it is measured keeping the quantum chromodynamics (QCD) associated diboson production fixed to the standard model prediction. This is the first evidence of vector boson scattering in the l nu qq decay channel at LHC. The simultaneous measurement of the EW and QCD associated diboson production agrees with the standard model prediction. (C) 2022 The Author(s). Published by Elsevier B.V. Tumasyan, A.; Adam, W.; Andrejkovic, J. W.; Bergauer, T.; Chatterjee, S.; Dragicevic, M.; Del Valle, A. Escalante; Fruhwirth, R.; Jeitler, M.; Krammer, N.; Lechner, L.; Liko, D.; Mikulec, I; Paulitsch, P.; Pitters, F. M.; Schieck, J.; Schofbeck, R.; Schwarz, D.; Templ, S.; Waltenberger, W.; Wulz, C-E; Chekhovsky, V; Litomin, A.; Makarenko, V; Darwish, M. R.; De Wolf, E. A.; Janssen, T.; Kello, T.; Lelek, A.; Sfar, H. Rejeb; Van Mechelen, P.; Van Putte, S.; Van Remortel, N.; Blekman, F.; Bols, E. S.; D'Hondt, J.; Delcourt, M.; El Faham, H.; Lowette, S.; Moortgat, S.; Morton, A.; Muller, D.; Sahasransu, A. R.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Stylianou, N.; Beghin, D.; Bilin, B.; Clerbaux, B.; De Lentdecker, G.; Favart, L.; Grebenyuk, A.; Kalsi, A. K.; Lee, K.; Mahdavikhorrami, M.; Makarenko, I; Moureaux, L.; Petre, L.; Popov, A.; Postiau, N.; Starling, E.; Thomas, L.; Vanden Bemden, M.; Vander Velde, C.; Vanlaer, P.; Wezenbeek, L.; Gao, X.; Cornelis, T.; Dobur, D.; Knolle, J.; Lambrecht, L.; Mestdach, G.; Niedziela, M.; Roskas, C.; Samalan, A.; Skovpen, K.; Tytgat, M.; Vermassen, B.; Vit, M.; Benecke, A.; Bethani, A.; Bruno, G.; Bury, F.; Caputo, C.; David, P.; Delaere, C.; Donertas, I. S.; Giammanco, A.; Jaffel, K.; Jain, Sa; Lemaitre, V; Mondal, K.; Prisciandaro, J.; Taliercio, A.; Teklishyn, M.; Tran, T. T.; Vischia, P.; Wertz, S.; Alves, G. A.; Hensel, C.; Moraes, A.; Alda Junior, W. L.; Gallo Pereira, M. Alves; Ferreira Filho, M. Barroso; Malbouisson, H. Brandao; Carvalho, W.; Chinellato, J.; Da Costa, E. M.; Da Silveira, G. G.; Damiao, D. De Jesus; De Souza, S. Fonseca; Figueiredo, D. Matos; Herrera, C. Mora; Amarilo, K. Mota; Mundim, L.; Nogima, H.; Teles, P. Rebello; Santoro, A.; Silva Do Amaral, S. M.; Sznajder, A.; Thiel, M.; Da Silva De Araujo, F. Torres; Pereira, A. Vilela; Bernardes, C. A.; Calligaris, L.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Lemos, D. S.; Mercadante, P. G.; Novaes, S. F.; Padula, Sandra S.; Aleksandrov, A.; Antchev, G.; Hadjiiska, R.; Iaydjiev, P.; Misheva, M.; Rodozov, M.; Shopova, M.; Sultanov, G.; Dimitrov, A.; Ivanov, T.; Litov, L.; Pavlov, B.; Petkov, P.; Petrov, A.; Cheng, T.; Javaid, T.; Mittal, M.; Yuan, L.; Ahmad, M.; Bauer, G.; Dozen, C.; Hu, Z.; Martins, J.; Wang, Y.; Yi, K.; Chapon, E.; Chen, G. M.; Chen, H. S.; Chen, M.; Iemmi, F.; Kapoor, A.; Leggat, D.; Liao, H.; Liu, Z-A; Milosevic, V; Monti, F.; Sharma, R.; Tao, J.; Thomas-Wilsker, J.; Wang, J.; Zhang, H.; Zhao, J.; Agapitos, A.; An, Y.; Ban, Y.; Chen, C.; Levin, A.; Li, Q.; Lyu, X.; Mao, Y.; Qian, S. J.; Wang, D.; Wang, Q.; Xiao, J.; Lu, M.; You, Z.; Okawa, H.; Lin, Z.; Xiao, M.; Avila, C.; Cabrera, A.; Florez, C.; Fraga, J.; Mejia Guisao, J.; Ramirez, F.; Ruiz Alvarez, J. D.; Salazar Gonzalez, C. A.; Giljanovic, D.; Godinovic, N.; Lelas, D.; Puljak, I; Antunovic, Z.; Kovac, M.; Sculac, T.; Brigljevic, V; Ferencek, D.; Majumder, D.; Roguljic, M.; Starodumov, A.; Susa, T.; Attikis, A.; Christoforou, K.; Erodotou, E.; Ioannou, A.; Kole, G.; Kolosova, M.; Konstantinou, S.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Saka, H.; Finger, M.; Finger Jr, M.; Kveton, A.; Ayala, E.; Carrera Jarrin, E.; Abdalla, H.; Khalil, S.; Lotfy, A.; Mahmoud, M. A.; Bhowmik, S.; Dewanjee, R. K.; Ehataht, K.; Kadastik, M.; Nandan, S.; Nielsen, C.; Pata, J.; Raidal, M.; Tani, L.; Veelken, C.; Eerola, P.; Forthomme, L.; Kirschenmann, H.; Osterberg, K.; Voutilainen, M.; Bharthuar, S.; Brucken, E.; Garcia, F.; Havukainen, J.; Kim, M. 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Univ Pavia, Pavia, Italy; Ist Nazl Fis Nucl, Sez Perugia, Perugia, Italy; Univ Perugia, Perugia, Italy; Ist Nazl Fis Nucl, Sez Pisa, Pisa, Italy; Univ Pisa, Pisa, Italy; Scuola Normale Super Pisa, Pisa, Italy; Univ Siena, Siena, Italy; Ist Nazl Fis Nucl, Sez Roma, Rome, Italy; Sapienza Univ Roma, Rome, Italy; Ist Nazl Fis Nucl, Sez Torino, Turin, Italy; Univ Torino, Turin, Italy; Univ Piemonte Orientale, Novara, Italy; Ist Nazl Fis Nucl, Sez Trieste, Trieste, Italy; Univ Trieste, Trieste, Italy; Kyungpook Natl Univ, Daegu, South Korea; Chonnam Natl Univ, Inst Universe & Elementary Particles, Kwangju, South Korea; Hanyang Univ, Seoul, South Korea; Korea Univ, Seoul, South Korea; Kyung Hee Univ, Dept Phys, Seoul, South Korea; Sejong Univ, Seoul, South Korea; Seoul Natl Univ, Seoul, South Korea; Univ Seoul, Seoul, South Korea; Yonsei Univ, Dept Phys, Seoul, South Korea; Sungkyunkwan Univ, Suwon, South Korea; Amer Univ Middle East AUM, Coll Engn & Technol, Dasman, Egaila, Kuwait; Riga Tech Univ, Riga, Latvia; 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Cukurova Univ, Sci & Art Fac, Phys Dept, Adana, Turkey; Middle East Tech Univ, Phys Dept, Ankara, Turkey; Bogazici Univ, Istanbul, Turkey; Istanbul Tech Univ, Istanbul, Turkey; Istanbul Univ, Istanbul, Turkey; Natl Acad Sci Ukraine, Inst Scintillat Mat, Kharkov, Ukraine; Natl Sci Ctr, Kharkov Inst Phys & Technol, Kharkov, Ukraine; Univ Bristol, Bristol, Avon, England; Rutherford Appleton Lab, Didcot, Oxon, England; Imperial Coll, London, England; Brunel Univ, Uxbridge, Middx, England; Baylor Univ, Waco, TX 76798 USA; Catholic Univ Amer, Washington, DC 20064 USA; Univ Alabama, Tuscaloosa, AL USA; Boston Univ, Boston, MA 02215 USA; Brown Univ, Providence, RI 02912 USA; Univ Calif Davis, Davis, CA 95616 USA; Univ Calif Los Angeles, Los Angeles, CA USA; Univ Calif Riverside, Riverside, CA 92521 USA; Univ Calif San Diego, La Jolla, CA 92093 USA; Univ Calif Santa Barbara, Dept Phys, Santa Barbara, CA 93106 USA; CALTECH, Pasadena, CA USA; Carnegie Mellon Univ, Pittsburgh, PA 15213 USA; Univ Colorado, Boulder, CO 80309 USA; Cornell Univ, Ithaca, NY USA; Fermilab Natl Accelerator Lab, Batavia, IL USA; Univ Florida, Gainesville, FL USA; Florida State Univ, Tallahassee, FL 32306 USA; Florida Inst Technol, Melbourne, FL 32901 USA; Univ Illinois Chicago UIC, Chicago, IL USA; Univ Iowa, Iowa City, IA USA; Johns Hopkins Univ, Baltimore, MD USA; Univ Kansas, Lawrence, KS 66045 USA; Kansas State Univ, Manhattan, KS 66506 USA; Lawrence Livermore Natl Lab, Livermore, CA 94550 USA; Univ Maryland, College Pk, MD 20742 USA; MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA; Univ Minnesota, Minneapolis, MN USA; Univ Nebraska Lincoln, Lincoln, NE USA; SUNY Buffalo, Buffalo, NY USA; Northeastern Univ, Boston, MA 02115 USA; Northwestern Univ, Evanston, IL USA; Univ Notre Dame, Notre Dame, IN 46556 USA; Ohio State Univ, Columbus, OH 43210 USA; Princeton Univ, Princeton, NJ 08544 USA; Univ Puerto Rico, Mayaguez, PR USA; Purdue Univ, W Lafayette, PA USA; Purdue Univ Northwest, Hammond, IA USA; Rice Univ, Houston, TX USA; Univ Rochester, Rochester, NY USA; Rutgers State Univ, Piscataway, NJ USA; Univ Tennessee, Knoxville, TN USA; Texas A&M Univ, College Stn, TX USA; Texas Tech Univ, Lubbock, TX 79409 USA; Vanderbilt Univ, 221 Kirkland Hall, Nashville, TN 37235 USA; Univ Virginia, Charlottesville, VA USA; Wayne State Univ, Detroit, MI USA; Univ Wisconsin, Madison, WI USA; TU Wien, Vienna, Austria; Arab Acad Sci Technol & Maritime Transport, Fac Engn, Inst Basic & Appl Sci, Alexandria, Egypt; Univ Estadual Campinas, Campinas, SP, Brazil; Univ Fed Rio Grande do Sul, Porto Alegre, RS, Brazil; Univ Chinese Acad Sci, Beijing, Peoples R China; UFMS, Nova Andradina, Brazil; Nanjing Normal Univ, Dept Phys, Nanjing, Peoples R China; Cairo Univ, Cairo, Egypt; Zewail City Sci & Technol, Zewail, Egypt; Univ Haute Alsace, Mulhouse, France; Tbilisi State Univ, Tbilisi, Georgia; Erzincan Binali Yildirim Univ, Erzincan, Turkey; Brandenburg Tech Univ Cottbus, Cottbus, Germany; Forschungszentrum Julich, Julich, Germany; Assiut Univ, Fac Sci, Phys Dept, Assiut, Egypt; IIT Bhubaneswar, Bhubaneswar, India; Inst Phys, Bhubaneswar, India; GHG Khalsa Coll, Ludhiana, Punjab, India; Shoolini Univ, Solan, India; Univ Hyderabad, Hyderabad, India; Univ Visva Bharati, Santini Ketan, W Bengal, India; Indian Inst Technol IIT, Mumbai, Maharashtra, India; Sharif Univ Technol, Tehran, Iran; Univ Sci & Technol Mazandaran, Dept Phys, Behshahr, Iran; Italian Natl Agcy New Technol Energy & Sustainabl, Bologna, Italy; Ctr Siciliano Fis Nucl & Struttura Mat, Catania, Italy; CNR, Ist Officina Mat, Perugia, Italy; Consejo Nacl Ciencia & Technol, Mexico City, DF, Mexico; Uzbek Acad Sci, Inst Nucl Phys, Tashkent, Uzbekistan; St Petersburg Polytech Univ, St Petersburg, Russia; Budker Inst Nucl Phys, Novosibirsk, Russia; Eastern Univ, Trincomalee Campus, Nilaveli, Sri Lanka; Ecole Polytech Fed Lausanne, Lausanne, Switzerland; Stefan Meyer Inst Subatom Phys, Vienna, Austria; IN2P3 CNRS, Lab Annecy Le Vieux Phys Particules, Annecy Le Vieux, France; Sirnak Univ, Sirnak, Turkey; Near East Univ, Res Ctr Expt Hlth Sci, Nicosia, Turkey; Konya Tech Univ, Konya, Turkey; Istanbul Univ Cerrahpasa, Fac Engn, Istanbul, Turkey; Piri Reis Univ, Istanbul, Turkey; Adiyaman Univ, Adiyaman, Turkey; Ozyegin Univ, Istanbul, Turkey; Izmir Inst Technol, Izmir, Turkey; Necmettin Erbakan Univ, Konya, Turkey; Bozok Univ Rektorlugu, Yozgat, Turkey; Marmara Univ, Istanbul, Turkey; Milli Savunma Univ, Istanbul, Turkey; Kafkas Univ, Kars, Turkey; Istanbul Bilgi Univ, Istanbul, Turkey; Hacettepe Univ, Ankara, Turkey; Univ Southampton, Sch Phys & Astron, Southampton, England; Univ Durham, IPPP, Durham, England; Monash Univ, Fac Sci, Clayton, Vic, Australia; Bethel Univ, Minneapolis, MN USA; Karamanoglu Mehmetbey Univ, Karaman, Turkey; Ain Shams Univ, Cairo, Egypt; Bingol Univ, Bingol, Turkey; Sinop Univ, Sinop, Turkey; Erciyes Univ, Kayseri, Turkey; Texas A&M Univ Qatar, Doha, Qatar Zhang, Wenyu/KIJ-2417-2024; Rolandi, Luigi (Gigi)/E-8563-2013; Saygin, Kadir/AAX-5235-2021; Biino, Cristina/AAF-1585-2020; LECOQ, Paul/S-7246-2019; Lokhtin, Igor/D-7004-2012; 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57220064466; 57908413800; 57908457500; 57220064460; 57219647487; 55603522400; 57216997315; 57220064173; 57220064313; 57220064097; 57220064533; 57202522373; 57219648360; 57216596346; 35227365100; 57220064314; 57217841082; 57221685212; 57220064375; 36675437100; 57220064223; 23480738300; 57220064351; 57199996011; 57207983072; 22996761700; 55951354400; 57201370087; 56681988200; 57147084600; 56401004600; 55868435500; 57210751238; 35308730700; 8984617000; 57189693933; 7006404363; 57210312265; 7003879901; 57210321748; 57215670509; 36639963700; 56501874000; 36123824900; 57217021019; 35227990900; 57193206437; 56648200300; 57216948352 PHYSICS LETTERS B PHYS LETT B 0370-2693 1873-2445 834 SCIE ASTRONOMY & ASTROPHYSICS;PHYSICS, NUCLEAR;PHYSICS, PARTICLES & FIELDS 2022 4.3 13.2 1.37 2025-06-25 12 15 CMS; Vector boson scattering PP COLLISIONS; EVENTS; PHYSICS CMS; Vector boson scattering English 2022 2022-11-10 10.1016/j.physletb.2022.137438 바로가기 바로가기 바로가기 바로가기
Article Fragmentation of jets containing a prompt J/ψ meson in PbPb and pp collisions at √sNN=5.02 TeV Jets containing a prompt J/psi meson are studied in lead-lead collisions at a nucleon-nucleon center-of mass energy of 5.02 TeV, using the CMS detector at the LHC. Jets are selected to be in the transverse momentum range of 30 <= pT <= 40 GeV. The J/psi yield in these jets is evaluated as a function of the jet fragmentation variable z, the ratio of the J/psi T-p to the jet p(T). The nuclear modification factor, R-AA, is then derived by comparing the yield in lead-lead collisions to the corresponding expectation based on proton-proton data, at the same nucleon-nucleon center-of-mass energy. The suppression of the J/psi yield shows a dependence on z, indicating that the interaction of the J/psi with the quark-gluon plasma formed in heavy ion collisions depends on the fragmentation that gives rise to the J/psi meson. (C) 2021 The Author(s). Published by Elsevier B.V. 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Elkafrawy, Tamer/Q-8786-2016; Golubev, Nikolay/L-4131-2017; Re, Virginia/F-6403-2013; Sarica, Ulascan/JXY-2829-2024; Puerta Pelayo, Jesus/L-1632-2014; Lokhtin, Igor/D-7004-2012; yagil, avi/I-3278-2018; de Souza Lemos, Dener/X-1931-2018; Rodrigo, Teresa/F-6183-2018; Albert, Arianne/AAG-7518-2019; Sculac, Toni/AAE-4141-2019; Květoň, Antonín/AAA-9178-2020; li, jing/KHY-5337-2024; Amendola, Chiara/KCK-3814-2024; Shah, Aashaq/GYI-8983-2022; Venditti, Rosamaria/I-1030-2014; Selvaggi, Michele/CAJ-2129-2022; Bonacorsi, Daniele/F-1505-2014; Morovic, Srecko/T-9019-2019; Padhi, Saswat/LTF-2060-2024; Chistov, Ruslan/B-4893-2014; Majumder, Gobinda/MTB-7852-2025; Janssen, Xavier/E-1915-2013; Calderon, Dr. Alicia/K-3658-2014; Grynyov, Borys/D-1714-2010; Garutti, Erika/NRY-4813-2025; Petrolini, Alessandro/H-3782-2011; Habibullah, Redwan/AAK-9617-2020; Everaerts, Pieter/KIE-3686-2024; Menichelli, Mauro/AAQ-4026-2020; Azarkin, Maxim/N-2578-2015; Babaev, Anton/U-5457-2019; Abbrescia, Marcello/HTN-5367-2023; Tsirou, Andromachi/HHC-6869-2022; Errico, Filippo/AAD-7755-2019; Sen, S./C-6473-2014; Malbouisson, H./N-6733-2015; Stahl, Achim/E-8846-2011; Cardini, Andrea/KUF-0984-2024; Kaveh, Hessamoddin/AAI-7230-2021; Gonzalez, Arely/I-1034-2015; Navarria, Francesco Luigi/ACP-7823-2022; Steggemann, Jan/AAL-5700-2020; kangal, evrim/L-8978-2013; Mohammadi Najafabadi, Mohammad/GLR-0213-2022; Soffi, Livia/HSC-0774-2023; Hlushchenko, Olena/AAC-8972-2022; Hernandez Calama, Jose Maria/H-9127-2015; Kalsi, Amandeep Kaur/JNR-9607-2023; Calligaris, Luigi/K-2018-2017; Gelmi, Amy/D-4456-2012; Moon, Chang-Seong/J-3619-2014; Spagnolo, Paolo/G-3401-2017; Pavlov, Borislav/O-8491-2018; Menasce, Dario/A-2168-2016; Rolandi, Luigi (Gigi)/E-8563-2013; Varela, Joao/K-4829-2016; Melo da Costa, Eliza/AAX-2394-2021; Lista, Luca/M-2912-2019; Arcidiacono, Roberta/ABF-3918-2020; Brandt, Steven/AGV-6975-2022; Dutta, Ipsita/JDW-8207-2023; Duarte, Javier/AAA-5414-2020; Saka, Halil/GPF-8892-2022; Battilana, Carlo/AAA-7345-2020; Fernandez Perez Tomei, Thiago Rafael/E-7091-2012; Van Remortel, Nick/AGQ-7045-2022; Caspart, Rene/LNQ-1560-2024; Zhang, heyang/LBH-8236-2024; Gülmez, Erhan/P-9518-2015; Gonzalez, Barbara/ABG-7021-2020; Rolandi, Luigi/E-8563-2013; Singh, J B/IXD-2130-2023; Shoaib, Muhammad/V-1862-2019; Pálinkás, József/AAB-9640-2019; Mohammadi Najafabadi, Mojtaba/HLH-5741-2023; Lange, Torben/HOH-4787-2023; Alison, Jamie/AAD-5013-2022; Gozalez-Lopez, Oscar/AAH-3533-2019; Kim, Mee/E-7234-2015; Verwilligen, Piet/AAA-8819-2019; Bunkowski, Karol/KOD-1567-2024; Donato, Silvio/GPK-2262-2022; Doğangün, Oktay/AAC-1579-2022; Kim, Victor/L-1648-2013; Kim, Soon Hee/GXF-6736-2022; Govoni, Pietro/K-9619-2016; Chen, Fang/JZE-4446-2024; Bermudez Martinez, Armando/HHN-4625-2022; Salvini, Paola/G-1670-2018; Akgun, Bora/AAM-8311-2021; Zhu, Ren-Yuan/V-8966-2019; ciocci, maria agnese/I-2153-2015; Mulders, Martijn/AGK-9612-2022; Wuchterl, Sebastian/LOU-0041-2024; Ferro, Fabrizio/HMP-5516-2023; Luukka, Panja-Riina/S-8956-2018; Wang, Dayong/LRC-9881-2024; Kirpichnikov, Dmitry/U-9605-2019; Calderon, Alicia/K-3658-2014; Dewanjee, Ram Krishna/AFE-3468-2022; Fernandez Menendez, Javier/B-6550-2014; Lau, Kok-Tee/AAX-5361-2020; Juodagalvis, Andrius/F-6952-2018; Zalewski, Piotr/H-7335-2013; Forthomme, Laurent/AFS-8908-2022; Sun, Mingkang/NGS-8788-2025; Venturi, Andrea/J-1877-2012; Krücker, Dirk/X-1844-2019; Da Silveira, Gustavo Gil/N-7279-2014; Fedi, Giacomo/IRZ-8694-2023; Missiroli, Marino/AAA-9072-2021; Obertino, Maria/ABH-1662-2020; Buchanan, James/ABB-6906-2021; Lee, Seung Eun/ABG-1607-2021; Martinez Rivero, Celso/V-6916-2017; Piperov, Stefan/Q-1980-2017; Dai, Yuxiang/JGC-6867-2023; Pinna, Daniele/AAF-4490-2021; Terkulov, Adel/M-8581-2015; Hebbeker, Thomas/L-4073-2013; Boimska, Bozena/Z-6088-2019; Gomez, Jaime/P-5242-2018; Perez-Calero Yzquierdo, Antonio/F-2235-2013; Azzi, Patrizia/H-5404-2012; Sonnadara, Upul/E-6359-2010; Presilla, Matteo/MGW-3532-2025; Roguljic, Matej/NMJ-5801-2025; Kyberd, Paul/P-1092-2014; Primavera, Federica/KUD-4067-2024; de guio, federico/B-8553-2009; KHAN, Masood/AAA-7384-2020; Su, Hang/Z-1713-2019; De Iorio, Agostino/HJH-7155-2023; Della Ricca, Giuseppe/B-6826-2013; , Luo/AAP-8401-2020; Marin, Jose/K-6412-2014; Camporesi, Tiziano/F-8307-2013; Rossi, Biagio/F-4137-2018; Fiori, Francesco/H-1454-2018; Chang, Philip/AAN-3350-2021; Li, Qiang/AGK-6990-2022; Paulini, Manfred/N-7794-2014; Swain, Subrat/IXW-8971-2023; Mora Herrera, Maria Clemencia/AAD-6466-2021; de Jesus Damiao, Dilson/G-6218-2012; Benaglia, Andrea/AGF-5495-2022; Gregores, Eduardo/F-8702-2012; Gütay, Levent/C-5352-2009; Tytgat, Michael/F-3732-2018; SEVINC KAYA, OZLEM/AAE-8069-2020; Duarte-Campderrós, Jordi/F-5025-2018; Bharthuar, Shudhashil/GQB-2619-2022; Sagir, Sinan/A-5219-2018; Saoulidou, Niki/AAA-2891-2020; Kim, Tae Jeong/P-7848-2015; Novaes, Sergio/D-3532-2012; Grandi, Claudio/B-5654-2015; Triossi, Andrea/K-2813-2017; Sanchez, Federico/F-5809-2012; Milosevic, Jelena/ABG-9850-2020; Vischia, Pietro/AAB-7811-2021; Skovpen, Kirill/HKM-3974-2023; Godinovic, Nikola/G-6124-2017; Skoro, Goran/F-3642-2010; Moscatelli, Francesco/AEL-1878-2022; Watson, Ian/IAM-9344-2023; Reis, Tomas/JHS-4868-2023; Lezki, Samet/AAG-8277-2019; Zhang, Hong-Hao/F-3737-2011; Gu, Chenghong/AAH-1540-2019; Bilican, Fuat/AGD-3089-2022; Kim, Dongwon/KHV-7759-2024; Nandan, Saswati/JVY-9235-2024; Kumar, Vijay/G-4290-2011; wang, long/IZE-1764-2023; Gandrajula, Reddy/AFR-4403-2022; Zhou, Shiyuan/IYJ-7387-2023; Barnyakov, Alexander/AAZ-8825-2021; Androsov, Konstantin/HJA-2332-2022; Zorbilmez, Caglar/ABH-1905-2020; Aguilar-Benitez, Manuel/L-4277-2017; Fouz Iglesias, Maria Cruz/AAF-1105-2019; Gavrilov, Gennadii/GRO-6222-2022; WANG, SHIHAO/KHC-8263-2024; Viliani, Lorenzo/AAU-3462-2021; zhao, jize/AAS-1102-2021; Cipriani, Marco/MHU-3780-2025; Aruta, Caterina/NRX-9970-2025; Radics, Balint/AAN-2721-2020; Pérez-Calero Yzquierdo, Antonio/F-2235-2013; Csanad, Mate/D-5960-2012; Ivanchenko, Vladimir/L-5254-2017; Ćirković, Predrag/G-8059-2012; MAESTRE, JUAN/I-5763-2015; Saygin, Kadir/AAX-5235-2021; Borg, Jacqueline/AFS-8793-2022; Kim, Hyun-Jong/X-3662-2019; Gupta, Rajat/GVS-7964-2022; Pásztor, Gabriella/D-9716-2016; Ragazzi, Stefano/D-2463-2009; staiano, amedeo/KVB-8364-2024; Malagalage, Kithsiri/F-7629-2018; Ma, Xiaodong/JAN-7473-2023; Flore, Carlo/HZJ-6377-2023; Malik, Sudhir/D-6621-2018; Paramatti, Riccardo/IXW-6363-2023; Behera, Prafulla/AAK-8686-2020; Lu, Rui/KCJ-8212-2024; Kaur, Amandeep/IYJ-2622-2023; Wadud, Dr. Mohammad A./AAR-2288-2020; Giacomelli, Paolo/B-8076-2009; Focardi, Ettore/E-7376-2012; Nguyen, Vu/AAQ-5062-2020; Gennai, Simone/P-2880-2015; Haddad, Yacine/GRX-5022-2022; Zaleski, Stéphane/N-8879-2017; Shmatov, Sergei/J-5621-2014; ZHANG, JIANWEN/JMQ-9363-2023; Vala, Martin/HZL-1003-2023; Mazza, Giovanni/ABI-2143-2020; Popov, Vitaliy/C-9925-2016; Colaleo, Anna/HSG-1637-2023; Anampa, Kenyi/GPX-8479-2022; Traczyk, Piotr/ABA-8929-2020; Giommi, Luca/AGV-7154-2022; Flix, Josep/G-5414-2012; Martinelli, Maurizio/AAC-8282-2021; Bertacchi, Valerio/KFQ-6990-2024; Paganoni, Marco/A-4235-2016; Kaspar, Jan/LPP-7624-2024; Dordevic, Milos/HSH-8083-2023; Salerno, Roberto/A-7509-2010; Smirnov, Vitaly/B-5001-2017; Yuldashev, Bekhzod/AAA-5876-2021; Katkov, Igor/E-2627-2012; Duarte-Campderros, Jordi/F-5025-2018; ciocci, maria/I-2153-2015; Mignerey, Alice/D-6623-2011; Choudhury, Seema/LEM-7962-2024; Marlow, Daniel/C-9132-2014; González Caballero, Isidro/E-7350-2010; Gleyzer, Sergei/AAE-6726-2020; Castilla-Valdez, Heriberto/F-8906-2019; tosi, mia/J-5777-2012; Lee, Suhyun/AAA-3368-2022; Boimska, Bożena/Z-6088-2019; Josa Mutuberria, Isabel/K-5184-2014; Wittich, Peter/HOH-5761-2023; Mejia Guisao, Jhovanny/ABG-3627-2021; SAHOO, DILLIP/AAA-4724-2022; Lychkovskaya, Natalia/F-8341-2017; bai, yu/KHU-2608-2024; Bedoya, Cristina/K-8066-2014; Zolkapli, Zukhaimira/AAA-4039-2019; KAYİS TOPAKSU, Aysel/B-8910-2018; Belyaev, Alexander/F-6637-2015; Malakhov, Alexander/D-5702-2016; Bossini, Edoardo/AAA-4972-2020; Ranieri, Antonio/E-9545-2015; Franci, Daniele/LDE-8961-2024; Hall-Wilton, Richard/U-6918-2019; Chen, Yang/KHD-8849-2024; Sert, H/ABA-5645-2020; Petkov, Peicho/M-2080-2016; Amoroso, Simone/AAW-4334-2021; LEITON, ANA/ABH-9538-2020; Sandro, Fonseca de Souza/M-8731-2014; Reyes-Almanza, Rogelio/GQJ-8236-2022; Palladino, Vito/AAA-5328-2020; Raidal, Martti/F-4436-2012; Lee, Jason/B-9701-2014; ASILAR, Ece/KEI-3075-2024; Doroba, krzysztof/AAA-3419-2021; Voevodina, Elena/G-7314-2016; Lutz, Benjamin/U-4283-2017; Pinna Angioni, Gian Luca/AAZ-6834-2021; Yazgan, Efe/C-4521-2014; Padula, Sandra/AAE-4304-2020; Kharlamova, Tatyana/AAQ-5430-2020; Ruiz Jimeno, Alberto/E-4473-2011; Venditti, Rosamaria/IVH-5947-2023; Meijers, Femke/IUM-7952-2023; ABDULLAH, WAN/B-5439-2010; Titov, Maxim/JAC-6742-2023; Bainbridge, Robert/JRW-6150-2023; Dewanjee, Dr. Ram Krishna/AFE-3468-2022; Mercadante, Pedro/K-1918-2012; Kim, Junghwan/AAQ-9204-2021; Hildreth, Michael/LWK-8035-2024; Maravin, Yurii/N-9259-2018; kasem, Ashraf/GMX-4925-2022; Atef, Ashraf/AAC-9697-2021; Orlando, Nicola/AAL-1723-2021; Fouz, Maria Cruz/AAF-1105-2019; Dozen, Candan/IQS-4597-2023; Heredia De La Cruz, Ivan/L-2610-2018; Neumeister, Norbert/JXN-1675-2024; Ciangottini, Diego/HJH-5914-2023; Guler, Yalcin/AAG-7833-2019; Jo, Hyon-Suk/HGC-7070-2022; HAJAHMAD (Wael HAJ AHMAD), Vael/E-6738-2016; De La Cruz Burelo, Eduard/Q-6021-2019; Goh, Junghwan/Q-3720-2016; D'Alessandro, Raffaello/F-5897-2015; Fanò, Livio/K-2460-2015; Ogul, Hasan/S-7951-2016; Salvatico, Riccardo/JAO-1069-2023; Cadamuro, Luca/AAO-8637-2020; Güler, Ali Murat/ABB-3382-2020; Fallavollita, Francesco/AAL-8850-2020; Singha, Soumya/AAS-5837-2021; Karancsi, János/A-9710-2013; Dhingra, Nitish/ABA-9507-2020; Tcherniaev, Evgueni/G-3453-2016; Biino, Cristina/AAF-1585-2020; Verdini, Piero/J-2839-2012; Lokos, Sandor/A-4798-2019; Sen, Sercan/C-6473-2014; Puljak, Ivica/D-8917-2017; Leonidov, Andrey/P-3197-2014; Chernyavskaya, Nadezda/F-2161-2015; Go, Yeonju/HSH-6030-2023; Ferencek, Dinko/G-6411-2015; Wang, Shaohui/HKO-6774-2023; Jain, Shikha/ADG-6788-2022; ASILAR, Ece/ABC-4577-2020; Gonzalez Caballero, Isidro/E-7350-2010; Osherson, Marc/LWZ-8687-2024; Kim, Geuk-Nam/IAO-4628-2023; Mastrapasqua, Vincenzo/KZU-0967-2024; Fienga, Francesco/HKE-1204-2023; Gulmez, Erhan/P-9518-2015; Ievgeniia, Makarenko/ABF-9828-2021; Naimuddin, Md/JXL-7104-2024; ozok, ferhat/A-6803-2018; Elkafrawy, Tamer/HPF-5873-2023; Savina, Maria/HDO-6544-2022; Navarria, Francesco/ACP-7823-2022; KÖMÜRCÜ, YILDIRAY/AAC-6166-2020; Meridiani, Paolo/ABH-1655-2020; Theofilatos, Konstantinos/AAL-9162-2021; Bastos, Daniel/GZG-4209-2022; Marinelli, Nancy/MCJ-3716-2025; Finco, Linda/AAH-3618-2019; Sarkar, Debojit/GNW-6585-2022; Kozhuharov, Venelin/AAL-1658-2021; Vilela Pereira, Antonio/L-4142-2016; Arneodo, Michele/ABF-7197-2020; Amapane, Nicola/HDN-9630-2022; Snoeys, Walter/K-8259-2015; Kim, Jeehoon/K-3763-2012; Maselli, Silvia/J-1599-2012; Dharmaratna, Welathantri/F-6745-2018; Cavallo, Nicola/F-8913-2012; Gil Da Silveira, Gustavo/N-7279-2014; Mrenna, Stephen/KIL-6081-2024; Aushev, Tagir/AAN-9735-2020; Chadeeva, Marina/M-9644-2019; Loukas, Dimitris/ABF-1478-2020; Orimoto, Toyoko/D-4759-2016; Pompili, Alexis/ISU-8813-2023; Trocino, Daniele/AGI-2155-2022; Troshin, Sergey/AAR-8556-2020; Gonzalez, Jesus David/IAN-5526-2023; Najafabadi, Mojtaba/AAF-7564-2019; Ovtin, Ivan/AAM-8892-2021; tsamalaidze, zviadi/U-6949-2019; Ahmad, Ashfaq/ABJ-8366-2022; Blekman, Freya/ABD-7916-2020; boccali, tommaso/ACT-7600-2022; Garg, Rocky/AAV-9845-2021; Waltenberger, Wolfgang/H-9330-2018; Li, Kun/JLL-6505-2023; Mousa, Jehad/I-8019-2019; Bourilkov, Dimitri/AAO-4908-2020; ALCARAZ MAESTRE, JUAN/I-5763-2015; Asawatangtrakuldee, Chayanit/GLS-1465-2022; Belyaev, Andrey/E-1540-2012; Hobson, Peter/C-8919-2016; wei, li/JWP-9848-2024; Seidel, Marcus/AAA-4799-2022; Grzanka, Leszek/M-9052-2018; Palencia Cortezon, Jose Enrique/F-1059-2017; Yalvac, Metin/LYP-1776-2024; Vardanyan, Irina/K-7981-2012; Tinoco Mendes, Andre David/D-4314-2011; BARRILLON, Stephanie/ABA-6873-2021; siviero, federico/LXA-5818-2024; Hernández Calama, José María/AAW-6394-2021; SHARMA, RAM/MIO-2784-2025; Cassese, Antonio/R-1713-2016; Eskut, Eda/B-8905-2018; Goy Lopez, Silvia/K-9200-2017; Okhotnikov, Vitalii/O-4293-2017; Rossi, Alessandro/I-8230-2017; Canelli, Florencia/AAI-1616-2021; Cerri, Olmo/Z-3579-2019; Sunar Cerci, Deniz/AHE-1706-2022; Demiragli, Zeynep/IZE-1919-2023; Benussi, Luigi/O-9684-2014; Xie, Si/O-6830-2016; Mora Herrera, Clemencia/AAD-6466-2021; Asavapibhop, Burin/X-1477-2019; Bartosik, Nazar/ABF-2326-2020; Simsek, Ali Eren/AFO-3050-2022; Petrucciani, Giovanni/AAM-8482-2020; Kratochwil, Nicolaus/ACR-4384-2022; Calvo Alamillo, Enrique/L-1203-2014; Konecki, Marcin/G-4164-2015; GULER, E/AAH-4748-2019; Chenarani, Shirin/AAO-8918-2021; SHIRAZI, SHARIF/G-4823-2010; Sen, F. Aydogmus/I-4879-2014; Ozkorucuklu, Suat/AAE-7535-2020; liu, yang/JMB-9083-2023; Cepeda, María/HNO-9314-2023; Fernandez Bedoya, Cristina/K-8066-2014; De Filippis, Nicola/AAD-6280-2019; Di Mattia, Alessandro/HNQ-0365-2023; Fasanella, Daniele/ABD-9759-2020; Strologas, John/GWR-2036-2022; de Souza Sandro, Fonseca/ABB-8505-2020; Polatoz, Ayse/AAF-6722-2021; Van Haevermaet, Hans/HOI-0668-2023; Gokbulut, Gul/G-2141-2018; Organtini, Giovanni/D-3990-2009; Montagna, Paolo Maria/AAY-8669-2021; Leonardo, Nuno/M-6940-2016; Rivero, Celso/V-6916-2017; Ryutin, Roman/P-3615-2015; Popov, Andrey/E-1052-2012; MantillaSuarez, Cristina/LWK-8416-2024; Maier, Benedikt/AAN-1929-2021; Ganjour, Serguei/D-8853-2011; Di Florio, Adriano/ABA-3631-2020; Legger, Federica/HNC-2568-2023; Dremin, Igor/K-8053-2015; LIU, Yandong/KVZ-1066-2024; Navarro-Tobar, Álvaro/K-7864-2014; KARA, Ozgun/MCY-5689-2025; Dallavalle, Gaetano Marco/AFW-4654-2022; Hall, Jeter/F-6108-2013; Iaydjiev, Plamen/AEB-8785-2022; Safronov, Grigory/F-8494-2017; Ivanov, Andrew/A-7982-2013; Clements, Emma/HSG-3424-2023; Malvezzi, Sandra/B-8524-2009; Sacchi, Roberto/AGS-7202-2022; Lander, Roddy/KQU-7736-2024; Iorio, Alberto Orso Maria/GXA-0233-2022; Wu, Wenjie/KVA-7436-2024; Veckalns, Viesturs/AAZ-3105-2020; Hoorani, Hafeez/D-1791-2013; Ruhlmann-Kleider, Vanina/AGJ-4460-2022; Moraes, Arthur/F-6478-2010; Corte-Leon, Paula/K-9094-2017; Delgado Peris, Antonio/AAA-5165-2019; Wimpenny, Stephen/K-8848-2013; SIMSEK, Ali Eren/AFO-3050-2022; Gerosa, Raffaele/AGL-6079-2022; My, Salvatore/HTM-1335-2023; Pasztor, Gabriella/D-9716-2016; Malawski, Maciej/H-9119-2012; Schwarz, Dennis/GYA-4479-2022; Quijada, Javier/AAS-4234-2021; Mondal, Santu/GSE-1742-2022; Fiorina, Davide/HKE-5979-2023; Philippov, Dmitry/H-6800-2016; Tomei, Thiago/E-7091-2012; Markowitz, Pete/AAC-3382-2020; Heredia de la Cruz, Ivan/L-2610-2018; Piedra, Jonatan/F-3247-2018; García-García, Francisco/B-1929-2014; Kapoor, Aakanksha Rajiv/KAM-5986-2024; Kardapoltsev, Leonid/AAQ-4047-2021; Cankocak, Kerem/A-1507-2018; Kadastik, Mario/B-7559-2008; Cimmino, Anna/AAA-1673-2020; Xu, Qiao/AAM-9242-2020; Teles, Patricia/ABF-9723-2021; Lannon, Kevin/HGU-5755-2022; Sznajder, Andre/L-1621-2016; Hong, Byungsik/O-8359-2018; Boos, Eduard/D-9748-2012; Lu, Meng/GQZ-7036-2022; Scodellaro, Luca/K-9091-2014; Roy Chowdhury, Suvankar/IXN-7230-2023; Rossi, Andrea/AGH-4546-2022; Bortignon, Pierluigi/AAF-6561-2020; Heller, Richard/I-6605-2012; Hoh, Siewyan/AAM-9562-2021; TUVE', Cristina/P-3933-2015; Dogra, Sunil/B-5330-2013; Novak, Tibor/JGE-0651-2023; Kovac, Marko/D-5817-2017; Moscatelli, Francesco/N-6333-2014; Wulz, Claudia-Elisabeth/H-5657-2011; LECOQ, Paul/S-7246-2019; Aimè, Chiara/GXG-4131-2022; candelise, vieri/H-2195-2015; Tiras, Emrah/ABG-2354-2020; Mishra, Trilochan/A-7947-2010; buotempo, salvatore/B-5210-2012; Isildak, Bora/D-8829-2015; zhang, jin/IXD-9872-2023; Hegeman, Jeroen/HJA-1005-2022; Mundim, Luiz/A-1291-2012; Khvedelidze, Arsen/AAF-5025-2020; Bagliesi, Giuseppe/C-2230-2013; Ciulli, Vitaliano/ABC-5440-2020; 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35227367700; 57202847289; 51663846700; 56681723400; 57204005081; 55795646500; 55203009000; 56352741400; 12758816400; 57217797874; 57190728397; 35227512400; 56681986300; 57205307086; 57199997802; 55095942200; 56682022400; 9276392600; 36171331300; 57220737619; 7101904491; 24376473600; 57220064478; 55134092500; 7005485775; 36170873500; 57219647487; 55603522400; 57216997315; 7102931559; 37051309800; 16240181000; 7004188390; 57220064533; 55538110700; 57219648360; 57216596346; 35227365100; 57220064314; 57217841082; 57221685212; 57220064375; 57220064223; 57220064351; 57220064259; 57203514331; 57199996011; 57206353679; 57207983072; 22996761700; 16226119300; 55951354400; 57201370087; 56681988200; 57147084600; 55868435500; 57210751238; 35308730700; 8984617000; 7006404363; 57210312265; 7003879901; 57210321748; 57215670509; 56501874000; 56681978600; 36123824900; 57217021019; 35227990900; 57193206437; 56648200300; 57216948352 PHYSICS LETTERS B PHYS LETT B 0370-2693 1873-2445 825 SCIE ASTRONOMY & ASTROPHYSICS;PHYSICS, NUCLEAR;PHYSICS, PARTICLES & FIELDS 2022 4.3 13.2 1.28 2025-06-25 14 14 CMS; Heavy ions; Quarkonia; Jets SUPPRESSION CMS; Heavy ions; Jets; Quarkonia English 2022 2022-02-10 10.1016/j.physletb.2021.136842 바로가기 바로가기 바로가기 바로가기
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논문 데이터 용어 설명

용어 설명
WoS Web of Science. Clarivate Analytics에서 제공하는 학술 데이터베이스입니다. 해당 논문이 WoS에 수록되어 있는지 여부를 표시합니다 (○: 수록됨).
SCOPUS Elsevier에서 제공하는 세계 최대 규모의 초록 및 인용 데이터베이스입니다. 해당 논문이 SCOPUS에 수록되어 있는지 여부를 표시합니다 (○: 수록됨).
Document Type 문헌의 유형을 나타냅니다. Article(원저), Review(리뷰), Proceeding Paper(학회논문), Editorial Material(편집자료), Letter(레터) 등으로 분류됩니다.
Title 논문의 제목입니다.
Abstract 논문의 초록(요약)입니다. 연구의 목적, 방법, 결과, 결론을 간략히 요약한 내용입니다.
Authors 논문의 저자 목록입니다. 공동 저자가 여러 명인 경우 세미콜론(;)으로 구분됩니다.
Affiliation 저자들의 소속 기관 정보입니다. 대학, 연구소, 기업 등 저자가 소속된 기관명이 표시됩니다.
ResearcherID (WoS) Web of Science의 고유 연구자 식별번호입니다. 동명이인을 구분하고 연구자의 업적을 정확하게 추적할 수 있습니다.
AuthorsID (SCOPUS) SCOPUS의 고유 저자 식별번호입니다. 연구자의 모든 출판물을 추적하고 관리하는 데 사용됩니다.
Journal 논문이 게재된 학술지의 정식 명칭입니다.
JCR Abbreviation Journal Citation Reports에서 사용하는 저널의 공식 약어입니다. 저널을 간략하게 표기할 때 사용됩니다.
ISSN International Standard Serial Number. 국제표준연속간행물번호로, 인쇄본 저널에 부여되는 고유 식별번호입니다.
eISSN Electronic ISSN. 전자 버전 저널에 부여되는 고유 식별번호입니다.
Volume 저널의 권(Volume) 번호입니다. 보통 연도별로 하나의 권이 부여됩니다.
Issue 저널의 호(Issue) 번호입니다. 한 권 내에서 여러 호로 나누어 출판되는 경우가 많습니다.
WoS Edition Web of Science의 에디션입니다. SCIE(Science Citation Index Expanded), SSCI(Social Sciences Citation Index), AHCI(Arts & Humanities Citation Index) 등으로 구분됩니다.
WoS Category Web of Science의 주제 분류 카테고리입니다. 저널과 논문이 속한 학문 분야를 나타냅니다.
JCR Year 해당 저널의 JCR(Journal Citation Reports) 지표가 산출된 연도입니다.
IF (Impact Factor) 저널 영향력 지수. 최근 2년간 발표된 논문이 해당 연도에 평균적으로 인용된 횟수를 나타냅니다. 저널의 학술적 영향력을 나타내는 대표적인 지표입니다.
JCR (%) 해당 카테고리에서 저널이 위치하는 상위 백분율입니다. 값이 낮을수록 우수한 저널임을 의미합니다 (예: 5%는 상위 5%를 의미).
FWCI Field-Weighted Citation Impact. 분야별 가중 인용 영향력 지수입니다. 논문이 받은 인용을 동일 분야, 동일 연도, 동일 문헌 유형의 평균과 비교한 값입니다. 1.0이 평균이며, 1.0보다 높으면 평균 이상의 인용을 받았음을 의미합니다.
FWCI UpdateDate FWCI 값이 마지막으로 업데이트된 날짜입니다. FWCI는 인용이 누적됨에 따라 주기적으로 업데이트됩니다.
WOS Citation Web of Science에서 집계된 해당 논문의 총 인용 횟수입니다.
SCOPUS Citation SCOPUS에서 집계된 해당 논문의 총 인용 횟수입니다.
Keywords (WoS) 저자가 논문에서 직접 지정한 키워드입니다. Web of Science에 등록된 저자 키워드 목록입니다.
KeywordsPlus (WoS) Web of Science에서 자동으로 추출한 추가 키워드입니다. 논문의 참고문헌 제목에서 자주 등장하는 단어들로 생성됩니다.
Keywords (SCOPUS) 저자가 논문에서 직접 지정한 키워드입니다. SCOPUS에 등록된 저자 키워드 목록입니다.
KeywordsPlus (SCOPUS) SCOPUS에서 자동으로 추출하거나 추가한 색인 키워드입니다.
Language 논문이 작성된 언어입니다. 대부분 English이며, 그 외 다양한 언어로 작성된 논문이 포함될 수 있습니다.
Publication Year 논문이 출판된 연도입니다.
Publication Date 논문의 정확한 출판 날짜입니다 (년-월-일 형식).
DOI Digital Object Identifier. 디지털 객체 식별자로, 논문을 고유하게 식별하는 영구적인 식별번호입니다. 이를 통해 논문의 온라인 위치를 찾을 수 있습니다.