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24th Working Notes of the Conference and Labs of the Evaluation Forum, CLEF-WN 2023

作者： Kim, Hyunjae Hwang, Hyeon Lee, Chaeeun Seo, Minju Yoon, Wonjin Kang, Jaewoo Department of Computer Science and Engineering Korea University Seoul02841 Korea Republic of Computational Health Informatics Program Boston Children’s Hospital BostonMA02115 United States Harvard Medical School BostonMA02115 United States AIGEN Sciences Seoul04778 Korea Republic of

Biomedical question answering (QA) plays a crucial role in assisting researchers, healthcare professionals, and even patients in accessing and retrieving accurate and up-to-date information from the vast amount of biomedical knowledge available in literature. To enhance the efficiency of knowledge discovery and information retrieval, we investigate the efficacy of various pre-processing, model training, data augmentation, and ensemble methods and evaluate a range of advanced pre-trained models such as BioLinkBERT and GPT-4. Additionally, we explore data augmentation and ensemble methods to further improve system performance. In our participation in BioASQ Task 11b-Phase B, our systems achieved a top ranking in all four batches for the yes/no type of questions, in one out of four batches for factoid questions, and in two out of four batches for list-type questions. © 2023 Copyright for this paper by its authors.

关键词： BioASQ 11b BioLinkBERT Data Augmentation Ensemble GPT-4

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JARVIS-Leaderboard:a large scale benchmark of materials design methods

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npj computational Materials 2024年第1期10卷 2280-2296页

作者： Kamal Choudhary Daniel Wines Kangming Li Kevin F.Garrity Vishu Gupta Aldo H.Romero Jaron T.Krogel Kayahan Saritas Addis Fuhr Panchapakesan Ganesh Paul R.C.Kent Keqiang Yan Yuchao Lin Shuiwang Ji Ben Blaiszik Patrick Reiser Pascal Friederich Ankit Agrawal Pratyush Tiwary Eric Beyerle Peter Minch Trevor David Rhone Ichiro Takeuchi Robert B.Wexler Arun Mannodi-Kanakkithodi Elif Ertekin Avanish Mishra Nithin Mathew Mitchell Wood Andrew Dale Rohskopf Jason Hattrick-Simpers Shih-Han Wang Luke E.K.Achenie Hongliang Xin Maureen Williams Adam J.Biacchi Francesca Tavazza Material Measurement Laboratory National Institute of Standards and TechnologyGaithersburgMD20899USA Department of Materials Science and Engineering University of Toronto27 King’s College CirTorontoONCanada Department of Electrical and Computer Engineering Northwestern UniversityEvanstonIL 60208USA Lewis-Sigler Institute for Integrative Genomics Princeton UniversityPrincetonNJ 08544USA Ludwig Institute for Cancer Research Princeton UniversityPrincetonNJ 08544USA Department of Physics and Astronomy West Virginia UniversityMorgantownWV 26506USA Materials Science and Technology Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Center for Nanophase Materials Science Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Computer Science and Engineering Texas A&M UniversityCollege StationTX 77843USA Globus University of ChicagoChicagoIL 60637USA Data Science and Learning Division Argonne National LabLemontIL 60439USA Institute of Nanotechnology Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Institute of Theoretical Informatics Karlsruhe Institute of TechnologyKaiserstraße 1276131 KarlsruheGermany Department of Chemistry and Biochemistry and Institute for Physical Science and Technology University of MarylandCollege ParkMD 20742USA Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY12180USA Department of Materials Science and Engineering University of MarylandCollege ParkMD20742USA Department of Chemistry and Institute of Materials Science and Engineering Washington University in St.LouisSt.LouisMO63130USA School of Materials Engineering Purdue UniversityWest LafayetteIN47907USA Department of Mechanical Science and Engineering University of Illinois Urbana-ChampaignUrbanaIllinois 61801USA Materials Research Laboratory University of Illinois Urbana-ChampaignUrbanaIL 61801USA Theore

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many *** science,in particular,encompasses a variety of experimental and theoretical approaches that require careful *** efforts have been developed previously to mitigate these ***,a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still *** work introduces JARVIS-Leaderboard,an open-source and community-driven platform that facilitates benchmarking and enhances *** platform allows users to set up benchmarks with customtasks and enables contributions in the form of dataset,code,and meta-data *** cover the following materials design categories:Artificial Intelligence(AI),Electronic Structure(ES).

关键词： rigorous perfect enable

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Conceptual Model of Graph-based Individual Tree and Its Utilization in Digital Twin and Metaverse of Urban Forest

Conceptual Model of Graph-based Individual Tree and Its Util...

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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Ambarwari, Agus Suwardhi, Deni Rani, Medria Shekar Husni, Emir Junaidy, Deny Willy Agirachman, Fauzan Alfi Murtiyoso, Arnadi Griess, Verena Christiane Doctoral Program of Electrical Engineering and Informatics School of Electrical Engineering and Informatics Institut Teknologi Bandung Jl. Ganesha No.10 Bandung40132 Indonesia Faculty of Earth Sciences and Technology Institut Teknologi Bandung Indonesia School of Architecture Planning and Policy Development Institut Teknologi Bandung Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Indonesia Faculty of Art and Design Institut Teknologi Bandung Indonesia Department of Environmental Systems Science Forest Resources Management Institute of Terrestrial Ecosystems Zurich8092 Switzerland

3D city models are an important cornerstone in the development of digital twin cities, allowing for various analyses and simulations. CityGML, as an open standard for 3D city models, emphasizes five main aspects: scale or level of detail (LoD), semantics, geometry, topology, and appearance. One of the important elements in CityGML 3D city models is representation of vegetation. Vegetation in CityGML, especially individual trees, is still limited in terms of detail and resolution, reducing its effectiveness for specific applications. This paper proposes a graph-based conceptual model for individual trees that conforms to the CityGML v2.0 specification. The model refers to the morphological structure of a tree consisting of roots, trunk, and crown (including branches, twigs, and leaves), and considers the five main aspects of CityGML. By enhancing semantic information and geometric details, the model aims to provide an information-rich and realistic representation of individual trees in a 3D city environment. The 3D tree model created based on this conceptual model may be applicable in the development of digital twin urban forests and virtual forest simulations that utilize immersive technologies such as the metaverse. This research opens up new opportunities in the development of solitary vegetation objects through CityGML ADE by including more detailed semantic and topological information. © Author(s) 2024.

关键词： Digital elevation model

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Properties of the mixing layer height retrieved from ceilometer measurements in Slovakia and its relationship to the air pollutant concentrations

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Environmental science and Pollution Research 2023年第54期30卷 115666-115682页

作者： Nguyen, Duy-Hieu Štefánik, Dušan Šedivá, Tereza Lin, Chitsan Ph.D. Program in Maritime Science and Technology College of Maritime National Kaohsiung University of Science and Technology Kaohsiung811213 Taiwan Slovak Hydrometeorological Institute Jeséniova 17 Bratislava833 15 Slovakia Faculty of Mathematics Physics and Informatics Comenius University in Bratislava Mlynská Dolina F1 Bratislava842 48 Slovakia Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung811213 Taiwan

Mixing layer height (MLH) is an important meteorological parameter for air quality since it significantly affects ground-level pollution in the atmosphere. This study examined the properties of the MLH on diurnal and seasonal timescales over a 3-year period (2020–2022) using high temporal resolution measurements from eight Vaisala CL31 ceilometers situated around Slovakia. Hourly averaged MLH data was retrieved from the BL-View software using merged method. The highest daily maxima for the MLH occurred mostly in summer and spring, while the lowest values occurred predominantly during winter and autumn. The average MLH daily maximum in summer was 2229 m, and just 859 m in winter. During summer, the spatial distribution of the MLH daily maxima was more uniform compared to winter, when the air masses within the individual valleys did not mix well. Correlations between ground-level pollutant concentrations and hourly mean/daily mean MLH were analyzed. The highest correlation, R≈0.6, was found for O3. For PM10, PM2.5, and NOx, the anticorrelations with MLH were found with maximum in winter (R ≈ − 0.3 for hourly data and R ≈ − 0.5 for daily mean data) but no relation in summer. Lastly, the ceilometer MLH was compared to the radiosonde retrieved MLH for various cloud covers. Our analysis is based on an extensive set of empirical data, which can improve the accuracy and effectiveness of meteorological and atmospheric chemistry models. The findings can support air pollution forecasting and warning systems, providing valuable insights for policymakers and researchers. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Air quality

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Scaled-up synthesis of defect-rich layered double hydroxide monolayers without organic species for efficient oxygen evolution reaction

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Green Energy & Environment 2022年第5期7卷 975-982页

作者： Haoyuan Chi Jingwen Dong Tian Li Sha Bai Ling Tan Jikang Wang Tianyang Shen Guihao Liu Lihong Liu Luyi Sun Yufei Zhao Yu-Fei Song State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical TechnologyBeijing100029China College of Chemistry Key Laboratory of Theoretical&Computational Photochemistry of Ministry of EducationBeijing Normal UniversityBeijing100875China Polymer Program Institute of Materials Science and Department of Chemical&Biomolecular EngineeringUniversity of ConnecticutStorrsConnecticut06269USA Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical TechnologyBeijing100029China

The scaled-up synthesis of organic-free monolayer nanomaterials is highly desirable,especially in obtaining green energy by *** this study,a method for the scaled-up synthesis of the series of monolayer layered double hydroxides(LDHs)without the addition of organic solvents is reported via the separate nucleation and aging steps *** resulting monolayer LDHs with the thicknesses of less than 1 nm showed a narrow thickness distribution.X-ray absorption fine-structure revealed that monolayer NiFe-LDH nanosheets have a number of oxygen and metal vacancies *** a practical application,monolayer NiFe-LDH nanosheets containing defects showed an enhanced electrocatalytic water oxidation activity compared with that of bulk *** functional theory calculations uncovered that excellent catalytic activity is attributed to vacancies *** proposed method is an economical and universally applicable strategy for the scaled-up production of monolayer LDHs.

关键词： process synthesis monolayer

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Carotid single- and dual-layer stents reduce the wall adhesion of platelets by influencing flow and cellular transport

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Computers in Biology and Medicine 2024年 183卷 109313-109313页

作者： Spieker, Christian J. Kern, Axelle Y. Korin, Netanel Mangin, Pierre H. Hoekstra, Alfons G. Závodszky, Gábor Computational Science Lab Informatics Institute Faculty of Science University of Amsterdam Amsterdam Netherlands INSERM EFS Grand-Est BPPS UMR-S 1255 FMTS Université de Strasbourg Strasbourg France Department of Biomedical Engineering Technion—Israel Institute of Technology Haifa Israel

An ongoing thrombosis on a ruptured atherosclerotic plaque in the carotid may cause stroke. The primary treatment for patients with tandem lesion is stenting. Dual-layer stents have been introduced as an alternative to single-layer stents for elective and emergent carotid artery stenting. While the dual-layer structure shows promise in reducing plaque prolapse through the stent struts and with it the occurrence of post-procedural embolism, there are early signs that this newer generation of stents is more thrombogenic. We investigate a single- and a dual-layer stent design to assess their influence on a set of thrombosis-related flow factors in a novel setup of combined experiments and simulations. The in vitro results reveal that both stents reduce thrombus formation by approximately 50% when human anticoagulated whole blood was perfused through macrofluidic flow chambers coated with either collagen or human atherosclerotic plaque homogenates. Simulations predict that the primary cause is reduced platelet presence in the vicinity of the wall, due to the influence of stents on flow and cellular transport. Both stents significantly alter the near-wall flow conditions, modifying shear rate, shear gradient, cell-free zones, and platelet availability. Additionally, the dual-layer stent has further increased local shear rates on the inner struts. It also displays increased stagnation zones and reduced recirculation between the outer-layer struts. Finally, the dual-layer stent shows further reduced adhesion over an atherosclerotic plaque coating. The novel approach presented here can be used to improve the design optimization process of cardiovascular stents in the future by allowing an in-depth study of the emerging flow characteristics and agonist transport. © 2024 The Authors

关键词： Struts

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Application of Neural Network in the Prediction Models of Machine Learning Based Design

Application of Neural Network in the Prediction Models of Ma...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Gehlot, Anita Ansari, Bushra Khatoon Arora, Deepika Anandaram, Harishchander Singh, Bhagwant Arias-Gonzáles, José Luis Uttaranchal University Uttaranchal Institute of Technology India Institute of Engineering and Technology AKTU Department of Civil Engineering Lucknow India Lloyd Institute of Engineering & Technology Computer Science & Engineering Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India SS Informatics Cluster School of Computer Science Uttarakhand Dehradun248007 India Pontificia Universidad Católica Del Peru Peru

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

This research examines way to pass, among the most dynamic and quickly-evolving technology fields in the telecommunication sector. Data may be sent between two locations wirelessly without the usage of digital downloads like wires and cables. Business telecom companies have advanced as a consequence and joined the big data age. Among the most potential machine learning methods for interpreting this deluge of information is computer science, which is employed in a range of business and scholarly research situations. This paper gives a broad overview of technology advancements and big data analytics, as well as possible uses for them in succeeding cellular networks. Apply sophisticated analytics to determine what mobile users' demands are, and then utilise that information to raise the effectiveness of "social wireless channels of communication."The huge, integrated, information machine educational approach it offers, which consists of extracting the features, simulation models, and forecasting enhancements, is particularly noteworthy. The key benefit of the proposed methodology is the logic of robust mathematical methods, issue conceptualization within the context of cellular networks, and reliance on a vast amount of data that represents both the bandwidth as well as other stringent user needs. and the ability to create a procedure. In a communication network, data is normally sent from the transmitter to the receiver across a distance. Using communication systems, the transmitter may be positioned somewhere within a few metres. We will examine existing strategies, their advantages and disadvantages, and potential developments in underwater sensors right now. We had discovered a fresh method of investigation. © 2022 IEEE.

关键词： Information management

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Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging

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aBIOTECH 2024年第3期5卷 281-297页

作者： Sishi Chen Xuqi Lu Hongda Fang Anand Babu Perumal Ruyue Li Lei Feng Mengcen Wang Yufei Liu College of Biosystems Engineering and Food Science Zhejiang UniversityHangzhou 310058China State Key Laboratory of Rice Biology and Breeding Zhejiang UniversityHangzhou 310058China Computational Modeling and Nanoscale Processing Unit National Institute of Food TechnologyEntrepreneurship and Management-ThanjavurMinistry of Food Processing IndustriesThanjavur 613005India College of Environmental and Resource Sciences Zhejiang UniversityHangzhou 310058China Global Education Program for AgriScience Frontiers Graduate School of AgricultureHokkaido UniversitySapporo 060-8589Japan

Bakanae disease,caused by Fusarium fujikuroi,poses a significant threat to rice production and has been observed in most rice-growing *** disease symptoms caused by different pathogens may vary,including elongated and weak stems,slender and yellow leaves,and dwarfism,as *** disease is likely to cause necrosis of diseased seedlings,and it may cause a large area of infection in the field through the transmission of ***,early disease surveillance plays a crucial role in securing rice *** monitoring methods are both time-consuming and labor-intensive and cannot be broadly *** this study,a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae *** data were obtained on the 9th,15th,and 21st day after rice infection to explore the physiological and biochemical performance,which helps to deepen the research on the disease *** data were obtained over these same periods of infection,and a deep learning model,named Rice Bakanae Disease-Visual Geometry Group(RBD-VGG),was established by leveraging hyperspectral imaging technology and deep learning *** on this model,an average accuracy of 92.2%was achieved on the 21st day of *** also achieved an accuracy of 79.4%as early as the 9th *** characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field ***,the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease,thereby providing an efficient avenue for disease prevention and control.

关键词： Bakanae disease Hyperspectral imaging Deep learning Early surveillance Disease monitoring

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UAV trajectory planning in NOMA-aided UAV-mounted RIS networks: A budgeted Multi-armed bandit approach 17

UAV trajectory planning in NOMA-aided UAV-mounted RIS networ...

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2024 17th International Conference on Computer and Electrical engineering, ICCEE 2024

作者： Hosny, Ramez Hashima, Sherief Hatano, Kohei Zaki, Rokaia M. ElHalawany, Basem M. Electrical Engineering Department Faculty of Engineering at Shoubra Benha University Cairo Egypt Computational Learning Theory Team RIKEN-AIP Fukuoka819-0395 Japan Engineering Dept Egyptian Atomic Energy Authority Cairo13759 Egypt Higher Technology Institute 10th of Ramadan Egypt Department of Informatics Kyushu University Fukuoka819-0395 Japan Higher Institute of Engineering and Technology Kafr El-Shaikh33514 Egypt Kuwait College of Science and Technology Doha District Block 4 93004 Kuwait

Reconfigurable Intelligent Surface (RIS) and Non-Orthogonal Multiple Access (NOMA) can be used as supporting technologies for driving the effective development of Unmanned Aerial Vehicles (UAV) communication systems. Herein, both technologies are combined to widen the millimeter-wave Base Stations (mmWave-BS) coverage range. In this work, we consider UAV Trajectory Planning (UTP) problem to enhance the performance of a NOMA-based UAV-mounted RIS communication network. The problem is formulated using two Budgeted Multi-Armed Bandits (BMAB) schemes. BMAB is a variant of the classical MAB algorithm that considers the random cost of selecting an arm. The UAV performs as a bandit player attempting to maximize its achievable rate (i.e., reward) via accessing different grids in its trajectory. This reward is accomplished simultaneously while minimizing the energy/cost of flying the UAV from one grid to another to preserve its battery lifetime. Hence, two variants of the Budgeted upper confidence bound (BUCB), namely the BUCB1 and BUCB2, are investigated, where the simulations show that both algorithms outperform other benchmarks. © 2024 institute of Physics Publishing. All rights reserved.

关键词： Antennas

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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