检索结果-内蒙古大学图书馆

Energy and exergy assessment of photovoltaic-thermal system using tungsten trioxide nanofluid: An experimental study

引用

International Journal of Thermofluids 2022年 16卷

作者： Alktranee, Mohammed Shehab, Mohammed Ahmed Németh, Zoltán Bencs, Péter Hernadi, Klara Koós, Tamás Department of Fluid and Heat Engineering Faculty of Mechanical Engineering and Informatics University of Miskolc HU Miskolc3515 Hungary Department of Mechanical Techniques Technical Institute of Basra Southern Technical University Basrah Iraq Faculty of Materials and Chemical Engineering University of Miskolc HU Miskolc3515 Hungary Polymers and Petrochemicals Engineering Department Basrah University for Oil and Gas Basrah61004 Iraq Advanced Materials and Intelligent Technologies Higher Education and Industrial Cooperation Centre University of Miskolc HU Miskolc3515 Hungary Institute of Physical Metallurgy Metal Forming and Nanotechnology University of Miskolc HU Miskolc3515 Hungary Institute of Energy and Quality Affairs University of Miskolc MiskolcH-3515 Hungary

The temperature rising of the photovoltaic (PV) modules is a major issue leading to a reduction in their conversion efficiency and low output power. Therefore, removing heat from the PV module is necessary to permanently work and improve the electrical power output. Serpentine pipes and a thermal absorber plate has proposed with fluid circulation to reduce the PV cell temperature and improve their efficiency. The current work experimentally investigates the effect of using tungsten trioxide (WO3) nanofluids on the PVT system's energy and exergy efficiency at different volume concentrations of 0. 5 vol%, 0. 75 vol%, and 1 vol%. The results indicated an increment in the electrical power output by 11.15 W due to reducing the PV temperature by 21.4% and enhancing the PVT overall efficiency by 29.6% compared with the cooling by Deionized (DI) water. Increasing the volume concentration of nanoparticles increased the Reynolds number, and the Nusselt number enhanced the heat transfer coefficient and increased the pressure drop. Improving the thermal properties of the nanofluid has increased the electrical exergy efficiency and lowered the thermal exergy efficiency due to the reduction in thermal exergy quality. Compared to DI water cooling, exergy losses and entropy generation were reduced by 10% and 34.8%, respectively. © 2022

关键词： Deionized water

来源：评论

学校读者我要写书评

暂无评论

Architecture-aware design and implementation of CNN algorithms for embedded inference: the ALOHA project

Architecture-aware design and implementation of CNN algorith...

引用

International Conference on Microelectronics, ICM

作者： Paolo Meloni Daniela Loi Gianfranco Deriu Andy D. Pimentel Dolly Saprat Maura Pintort Battista Biggio Oscar Ripolles David Solans Francesco Conti Luca Benini Todor Stefanov Svetlana Minakova Bernhard Moser Natalia Shepeleva Michael Masin Francesca Palumbo Nikos Fragoulis Ilias Theodorakopoulos Department of Electrical and Electronic Engineering University of Cagliari Italy Institute of Informatics University of Amsterdam The Netherlands Pluribus One Italy CA Technologies Spain Integrated Systems Laboratory ETH Zurich Switzerland Institute of Advanced Computer Science Leiden University The Netherlands Software Competence Center Hagenberg Austria IBM Research - Haifa Israel IDEA Lab University of Sassari Italy IRIDA Labs Computer Vision Systems Greece

ISBN: (纸本)9781538681688;9781538681671

The use of Deep Learning (DL) algorithms is increasingly evolving in many application domains. Despite the rapid growing of algorithm size and complexity, performing DL inference at the edge is becoming a clear trend to cope with low latency, privacy and bandwidth constraints. Nevertheless, traditional implementation on low-energy computing nodes often requires experience-based manual intervention and trial-and-error iterations to get to a functional and effective solution. This work presents a computer-aided design (CAD) support for effective implementation of DL algorithms on embedded systems, aiming at automating different design steps and reducing cost. The proposed tool flow comprises capabilities to consider architecture-and hardware-related variables at very early stages of the development process, from pre-training hyperparameter optimization and algorithm configuration to deployment, and to adequately address security, power efficiency and adaptivity requirements. This paper also presents some preliminary results obtained by the first implementation of the optimization techniques supported by the tool flow.

关键词： Tools Inference algorithms Engines Computer architecture Computational modeling Microelectronics Training

来源：评论

学校读者我要写书评

暂无评论

First Measurement of A=4 Hypernuclei and Antihypernuclei at the LHC

引用

Physical Review Letters 2025年第16期134卷 162301-162301页

作者： S. Acharya A. Agarwal G. Aglieri Rinella L. Aglietta M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov V. Akishina M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt A. R. Altamura I. Altsybeev J. R. Alvarado C. O. R. Alvarez M. N. Anaam C. Andrei N. Andreou A. Andronic E. Andronov V. Anguelov F. Antinori P. Antonioli N. Apadula L. Aphecetche H. Appelshäuser C. Arata S. Arcelli R. Arnaldi J. G. M. C. A. Arneiro I. C. Arsene M. Arslandok A. Augustinus R. Averbeck D. Averyanov M. D. Azmi H. Baba A. Badalà J. Bae Y. W. Baek X. Bai R. Bailhache Y. Bailung R. Bala A. Balbino A. Baldisseri B. Balis Z. Banoo V. Barbasova F. Barile L. Barioglio M. Barlou B. Barman G. G. Barnaföldi L. S. Barnby E. Barreau V. Barret L. Barreto C. Bartels K. Barth E. Bartsch N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova L. G. E. Beltran Y. A. V. Beltran G. Bencedi A. Bensaoula S. Beole Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin B. Bhattacharjee L. Bianchi J. Bielčík J. Bielčíková A. P. Bigot A. Bilandzic G. Biro S. Biswas N. Bize J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume G. Boca F. Bock T. Bodova J. Bok L. Boldizsár M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov A. G. Borquez Carcamo E. Botta Y. E. M. Bouziani L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno V. D. Buchakchiev M. D. Buckland D. Budnikov H. Buesching S. Bufalino P. Buhler N. Burmasov Z. Buthelezi A. Bylinkin S. A. Bysiak J. C. Cabanillas Noris M. F. T. Cabrera M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho P. Camerini F. D. M. Canedo S. L. Cantway M. Carabas A. A. Carballo F. Carnesecchi R. Caron L. A. D. Carvalho J. Castillo Castellanos M. Castoldi F. Catalano S. Cattaruzzi R. Cerri I. Chakaberia P. Chakraborty S. Chandra S. Cha Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento di Fisica dell’Università and Sezione INFN Turin Italy Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovak Republic Affiliated with an institute covered by a cooperation agreement with CERN Frankfurt Institute for Advanced Studies Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico University of Houston Houston Texas USA Sungkyunkwan University Suwon City Republic of Korea Department of Physics and Technology University of Bergen Bergen Norway INFN Sezione di Cagliari Cagliari Italy Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany INFN Sezione di Bari Bari Italy Physik Department Technische Universität München Munich Germany High Energy Physics Group Universidad Autónoma de Puebla Puebla Mexico Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania University of Derby Derby United Kingdom Universität Münster Institut für Kernphysik Münster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy INFN Sezione di Bologna Bologna Italy Lawrence Berkeley National Laboratory Berkeley California USA SUBATECH I

In this Letter, the first evidence of the He¯Λ¯4 antihypernucleus is presented, along with the first measurement at the LHC of the production of (anti)hypernuclei with mass number A=4, specifically (anti)HΛ4 and (anti)HeΛ4. In addition, the antiparticle-to-particle ratios for both hypernuclei (H¯Λ¯4/HΛ4 and He¯Λ¯4/HeΛ4) are shown, which are sensitive to the baryochemical potential of the strongly interacting matter created in heavy-ion collisions. The results are obtained from a data sample of central Pb-Pb collisions, collected during the 2018 LHC data taking at a center-of-mass energy per nucleon pair of sNN=5.02 TeV. The yields measured for the average of the charge-conjugated states are found to be [0.78±0.19(stat)±0.17(syst)]×10−6 for the (anti)HΛ4 and [1.08±0.34(stat)±0.20(syst)]×10−6 for the (anti)HeΛ4, and the measured antiparticle-to-particle ratios are in agreement with unity. The presence of (anti)HΛ4 and (anti)HeΛ4 excited states is expected to strongly enhance the production yield of these hypernuclei. The yield values exhibit a combined deviation of 3.3σ from the theoretical ground-state-only expectation, while the inclusion of the excited states in the calculations leads to an agreement within 0.6σ with the present measurements. Additionally, the measured (anti)HΛ4 and (anti)HeΛ4 masses are compatible with the world-average values within the uncertainties.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A few-shot learning graph multi-trajectory evolution network for forecasting multimodal baby connectivity development from a baseline timepoint

arXiv

引用

arXiv 2021年

作者： Bessadok, Alaa Nebli, Ahmed Mahjoub, Mohamed Ali Li, Gang Lin, Weili Shen, Dinggang Rekik, Islem BASIRA Lab Faculty of Computer and Informatics Istanbul Technical University Istanbul Turkey Higher Institute of Informatics and Communication Technologies University of Sousse 4011 Tunisia National Engineering School of Sousse University of Sousse LATIS- Laboratory of Advanced Technology and Intelligent Systems Sousse4023 Tunisia Mannouba Tunisia Department of Radiology and BRIC University of North Carolina Chapel HillNC United States School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development United Imaging Intelligence Co. Ltd. Shanghai China

Charting the baby connectome evolution trajectory during the first year after birth plays a vital role in understanding dynamic connectivity development of baby brains. Such analysis requires acquisition of longitudinal connectomic datasets. However, both neonatal and postnatal scans are rarely acquired due to various difficulties. A small body of works has focused on predicting baby brain evolution trajectory from a neonatal brain connectome derived from a single modality. Although promising, large training datasets are essential to boost model learning and to generalize to a multi-trajectory prediction from different modalities (i.e., functional and morphological connectomes). Here, we unprecedentedly explore the question: "Can we design a few-shot learning-based framework for predicting brain graph trajectories across different modalities?" To this aim, we propose a Graph Multi-Trajectory Evolution Network (GmTE-Net), which adopts a teacher-student paradigm where the teacher network learns on pure neonatal brain graphs and the student network learns on simulated brain graphs given a set of different timepoints. To the best of our knowledge, this is the first teacher-student architecture tailored for brain graph multi-trajectory growth prediction that is based on few-shot learning and generalized to graph neural networks (GNNs). To boost the performance of the student network, we introduce a local topology-aware distillation loss that forces the predicted graph topology of the student network to be consistent with the teacher network. Experimental results demonstrate substantial performance gains over benchmark methods. Hence, our GmTE-Net can be leveraged to predict atypical brain connectivity trajectory evolution across various modalities. Our code is available at https://***/basiralab/GmTE-Net. © 2021, CC BY-NC-ND.

关键词： Trajectories

来源：评论

学校读者我要写书评

暂无评论

CsPb(I_(x)Br_(1-x))_(3) solar cells

引用

Science Bulletin 2019年第20期64卷 1532-1539页

作者： Xue Jia Chuantian Zuo Shuxia Tao Kuan Sun Yixin Zhao Shangfeng Yang Ming Cheng Mingkui Wang Yongbo Yuan Junliang Yang Feng Gao Guichuan Xing Zhanhua Wei Lijun Zhang Hin-Lap Yip Mingzhen Liu Qing Shen Longwei Yin Liyuan Han Shengzhong Liu Lianzhou Wang Jingshan Luo Hairen Tan Zhiwen Jin Liming Ding Center for Excellence in Nanoscience(CAS) Key Laboratory of Nanosystem and Hierarchical Fabrication(CAS)National Center for Nanoscience and TechnologyBeijing100190China University of Chinese Academy of Sciences Beijing100049China CSIRO Manufacturing Bag10Clayton SouthVictoria3169Australia Center for Computational Energy Research Department of Applied PhysicsEindhoven University of Technology5600MB EindhovenThe Netherlands MOE Key Laboratory of Low-grade Energy Utilization Technologies and Systems Chongqing UniversityChongqing400044China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Department of Materials Science and Engineering University of Science and Technology of ChinaHefei230026China Institute for Energy Research Jiangsu UniversityZhenjiang212013China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan430074China School of Physics and Electronics Central South UniversityChangsha410083China Department of Physics Chemistry and BiologyLinkoping UniversitySE-58183 LinkopingSweden Institute of Applied Physics and Materials Engineering University of MacaoMacao999078China College of Materials Science&Engineering Huaqiao UniversityXiamen361021China College of Materials Science and Engineering Jilin UniversityChangchun130012China State Key Laboratory of Luminescent Materials and Devices South China University of TechnologyGuangzhou510640China Center for Applied Chemistry School of Materials and EnergyUniversity of Electronic Science and Technology of ChinaChengdu611731China Faculty of Informatics and Engineering The University of Electro-CommunicationsTokyo182-8585Japan School of Materials Science and Engineering Shandong UniversityJinan250061China State Key Laboratory of Metal Matrix Composites School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai200240China Shaanxi Key Laboratory for Advanced Energy Devices Shaanxi Normal UniversityXi’an710119China

Owing to its nice performance, low cost, and simple solution-processing, organic-inorganic hybrid perovskite solar cell(PSC) becomes a promising candidate for next-generation high-efficiency solar *** power conversion efficiency(PCE) has boosted from 3.8% to 25.2% over the past ten years. Despite the rapid progress in PCE, the device stability is a key issue that impedes the commercialization of PSCs. Recently, all-inorganic cesium lead halide perovskites have attracted much attention due to their better stability compared with their organic-inorganic counterpart. In this progress report, we summarize the properties of CsPb(IxBr1-x)3 and their applications in solar cells. The current challenges and corresponding solutions are discussed. Finally, we share our perspectives on CsPb(IxBr1-x)3 solar cells and outline possible directions to further improve the device performance.

关键词： Perovskite solar cells Cesium lead halide perovskites Power conversion efficiency Stability

来源：评论

学校读者我要写书评

暂无评论

Antimicrobial nitric oxide releasing compounds and scaffolds

Antimicrobial nitric oxide releasing compounds and scaffolds

引用

2019 Symposium on Antimicrobial Combination Devices

作者： McEntire, Bryan J. Sonny Bal, B. Pezzotti, Giuseppe SINTX Technologies Inc. 1885 W. 2100 S. Salt Lake CityUT84119 United States Ceramic Physics Laboratory Kyoto Institute of Technology Sakyo-ku Matsugasaki Kyoto606-8585 Japan Dept. of Orthopedic Surgery Tokyo Medical University 6-7-1 Nishi Shinjuku Shinjuku-ku Tokyo160-023 Japan Center for Advanced Medial Engineering and Informatics Osaka University 2-2 Yamadaoka Suita Osaka565-0854 Japan Dept. of Immunology Graduate School of Medical Science Kyoto Prefectural University of Medicine Kamigyo-ku 465 Kajii-cho Kyoto602-8566 Japan

ISBN: (纸本)9780803177062

Perioperative and latent infections are leading causes for the failure of orthopedic and dental implants. Their incidence is expected to increase unless innovative antimicrobial therapies and biomaterials are developed. One approach that has attracted interest is the utilization of nitric oxide (NO)-releasing biomaterials. NO is an endogenously produced lipophilic gas. It is an essential signaling molecule in many homeostatic processes and an important immunological defense against pathogens. The antimicrobial properties of NO rely, in part, on its combination with superoxide radicals to form various reactive nitrogen species. Collectively, these radicals exert nitrosative, oxidative, and osmotic stress in prokaryotic cells. Exogenous NO effectively enhances this bacteriolytic effect. NO is an ideal elutable molecule for biomedical implants because bacteriolysis occurs at the implant's surface. To date, a number of NO-releasing compounds and scaffolds have been developed, including nitrates/nitrites, nitrosyls, S-nitrosothiols, N-diazeniumdiolates, nanoparticles, biosynthetic polymers, and liposomes. Unfortunately, their clinical application has been limited due to inadequate NO payloads, uncontrolled release, and potential mammalian cell toxicity. Silicon nitride (Si3N4), a nonoxide bioceramic, is a new NO donor and scaffold that may overcome these limitations. Si3N4 modulates the release of NO in response to changes in the biological milieu, and it has the potential for extended effectiveness against pathogens. As a structural ceramic, Si3N4 has shown antimicrobial efficacy against several orthopedic and oral bacteria, and in powder form it can be incorporated into metallic and polymer composites with similar antipathogenic properties. This review addresses antimicrobial NO-releasing compounds and scaffolds, with special emphasis on silicon nitride. Copyright © 2020 by ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959.

关键词： Silicon nitride

来源：评论

学校读者我要写书评

暂无评论

Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

引用

Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Temperature and Electron Concentration Dependences of 1/f Noise in Hg1-xCdxTe - Evidence for a Mobility Fluctuations Mechanism

arXiv

引用

arXiv 2024年

作者： Rehman, Adil Petriakov, Volodymyr Yahniuk, Ivan Kazakov, Aleksandr Rogalska, Iwona Grendysa, Jakub Marchewka, Michal Haras, Maciej Wojtowicz, Tomasz Cywiński, Grzegorz Knap, Wojciech Rumyantsev, Sergey CENTERA Laboratories Institute of High Pressure Physics Polish Academy of Sciences Warsaw01-142 Poland Terahertz Center University of Regensburg Regensburg93040 Germany International Research Centre MagTop Institute of Physics Polish Academy of Sciences Warsaw02-668 Poland University of Rzeszów Institute of Materials Engineering Center for Microelectronics and Nanotechnology Rzeszów35-959 Poland Centre for Advanced Materials and Technologies CEZAMAT Warsaw University of Technology Warsaw02-822 Poland Gdańsk University of Technology Faculty of Electronics Telecommunications and Informatics Advanced Materials Center Gdańsk80-233 Poland

Hg1-xCdxTe is a unique material with the band-gap tunable by the temperature, pressure, and cadmium content in a wide range, from 1.6 eV to inverted band-gap of -0.3 eV. This makes Hg1-xCdxTe one of the key materials for infrared and terahertz detectors, whose characteristics largely depend on the material noise properties. In this work, we investigated the low-frequency 1/f noise in a thick (800 nm) HgCdTe layer and in a field effect transistor (FET) with an 8 nm wide HgTe quantum well. Both structures exhibited a small contribution from contact noise and showed weak noise dependences on temperature. Investigation of the 1/f noise in HgTe quantum well FET as a function of gate voltage revealed that the noise also depends weakly on electron concentration. These findings indicate that the noise properties of Hg1-xCdxTe are similar to those of graphene, where mobility fluctuations were found to be the dominant mechanism of the 1/f noise. © 2024, CC BY.

关键词： Nanocrystals

来源：评论

学校读者我要写书评

暂无评论

Measurement of the inclusive cross sections for W and Z boson production in proton-proton collisions at = 5.02 and 13 TeV

引用

Journal of High Energy Physics 2025年第4期2025卷 1-52页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Hussain, P. S. Jeitler, M. Krammer, N. Li, A. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M. R. Janssen, T. Van Mechelen, P. Bols, E. S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Hohov, D. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Mota Amarilo, K. Rendón, C. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I. S. Giammanco, A. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Coelho, E. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Aldá Júnior, W. L. Alves Gallo Pereira, M. Barroso Ferreira Filho, M. Brandao Malbouisson, H. Carvalho, W. Chinellato, J. Da Costa, E. M. Da Silveira, G. G. De Jesus Damiao, D. Fonseca De Souza, S. Gomes De Souza, R. Martins, J. Mora Herrera, C. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Vilela Pereira, A. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Mercadante, P. G. Novaes, S. F. Orzari, B. Padula, Sandra S. Aleksandrov, A. Antchev, G. Hadjiiska, R. Iaydjiev, P. Misheva, M. Shopova, M. Sultanov, G. Dimitrov, A. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Kapoor, A. Liao, H. Liu, Z.-A. Sharma, R. Song, J. N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Agapitos, A. Ban, Y. Levin, A. Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium Institute of Basic and Applied Sciences Faculty of Engineering Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Vrije Universiteit Brussel Brussel Belgium Ghent University Ghent Belgium Université Libre de Bruxelles Bruxelles Belgium Université Catholique de Louvain Louvain-la-Neuve Belgium Centro Brasileiro de Pesquisas Fisicas Rio de Janeiro Brazil Universidade do Estado do Rio de Janeiro Rio de Janeiro Brazil Universidade Estadual de Campinas Campinas Brazil Federal University of Rio Grande do Sul Porto Alegre Brazil UFMS Nova Andradina Brazil Universidade Estadual PaulistaUniversidade Federal do ABC São Paulo Brazil Institute for Nuclear Research and Nuclear Energy Bulgarian Academy of Sciences Sofia Bulgaria University of Sofia Sofia Bulgaria Instituto De Alta Investigación Universidad de Tarapacá Arica Chile Beihang University Beijing China Department of Physics Tsinghua University Beijing China Nanjing Normal University Nanjing China The University of Iowa Iowa City Iowa USA Institute of High Energy Physics Beijing China University of Chinese Academy of Sciences Beijing China China Center of Advanced Science and Technology Beijing China China Spallation Neutron Source Guangdong China State Key Laboratory of Nuclear Physics and Technology Peking University Beijing China Sun Yat-Sen University Guangzhou China University of Science and Technology of China Hefei China Henan Normal University Xinxiang China Institute of Modern Physics and Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) - Fudan University Shanghai China Zhejiang University Hangzhou China Universidad de Los Andes Bogota Colombia Universidad de Antioquia Medellin Colombia University of Split Faculty of Electrical Engineering Mechanical

Measurements of fiducial and total inclusive cross sections for W and Z boson production are presented in proton-proton collisions at $$ \sqrt{s} $$ = 5.02 and 13 TeV. Electron and muon decay modes (ℓ = e or μ) are studied in the data collected with the CMS detector in 2017, in dedicated runs with reduced instantaneous luminosity. The data sets correspond to integrated luminosities of 298 ± 6 pb−1 at 5.02 TeV and 206 ± 5 pb−1 at 13 TeV. Measured values of the products of the total inclusive cross sections and the branching fractions at 5.02 TeV are σ(pp → W + X) $$ \mathcal{B} $$ (W → ℓν) = 7300 ± 10 (stat) ± 60 (syst) ± 140 (lumi) pb, and σ(pp → Z+X) $$ \mathcal{B} $$ (Z → ℓ+ℓ−) = 669 ± 2 (stat) ± 6 (syst) ± 13 (lumi) pb for the dilepton invariant mass in the range of 60–120 GeV. The corresponding results at 13 TeV are 20480 ± 10 (stat) ± 170 (syst) ± 470 (lumi) pb and 1952 ± 4 (stat) ± 18 (syst) ± 45 (lumi) pb. The measured values agree with cross section calculations at next-to-next-to-leading-order in perturbative quantum chromodynamics. Fiducial and total inclusive cross sections, ratios of cross sections of W+ and W− production as well as inclusive W and Z boson production, and ratios of these measurements at 5.02 and 13 TeV are reported.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

引用

The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：