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Measurement of the and tH production rates in the H → decay channel using proton-proton collision data at = 13 TeV

Journal of High Energy Physics 2025年第2期2025卷 1-75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. Breugelmans, N. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Heyen, F. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Bilin, B. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Shahzad, M. A. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. Samalan, A. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, I. S. Giammanco, A. Guzel, A. O. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Alves Gallo Pereira, M. Coelho, E. Correia Silva, G. Hensel, C. Menezes De Oliveira, T. Moraes, A. Rebello Teles, P. Soeiro, M. Vilela Pereira, A. Aldá Júnior, W. L. 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. Macedo, M. Martins, J. Mora Herrera, C. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Maietto Silverio, I. 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. Liang, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. 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 Paulista Universidade 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 U.S.A. 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 Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou 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

An analysis of the production of a Higgs boson (H) in association with a top quark-antiquark pair ( $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ ) or a single top quark (tH) is presented. The Higgs boson decay into a bottom quark-antiquark pair (H → $$ \textrm{b}\overline{\textrm{b}} $$ ) is targeted, and three different final states of the top quark decays are considered, defined by the number of leptons (electrons or muons) in the event. The analysis utilises proton-proton collision data collected at the CERN LHC with the CMS experiment at $$ \sqrt{s} $$ = 13 TeV in 2016–2018, which correspond to an integrated luminosity of 138 fb−1. The observed $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ production rate relative to the standard model expectation is 0.33 ± 0.26 = 0.33 ± 0.17(stat) ± 0.21(syst). Additionally, the $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ production rate is determined in intervals of Higgs boson transverse momentum. An upper limit at 95% confidence level is set on the tH production rate of 14.6 times the standard model prediction, with an expectation of $$ {19.3}_{-6.0}^{+9.2} $$ . Finally, constraints are derived on the strength and structure of the coupling between the Higgs boson and the top quark from simultaneous extraction of the $$ \textrm{t}\overline{\textrm{t}}\textrm{H} $$ and tH production rates, and the results are combined with those obtained in other Higgs boson decay channels.

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A Multi-node Overlap Sensor Ratio Based Coverage Enhancing Algorithm in Wireless Multi-media Sensor Networks

A Multi-node Overlap Sensor Ratio Based Coverage Enhancing A...

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第30届中国控制与决策会议

作者： Ji Peng Chu Hao Yu Xiaosheng Si Pengju Faculty of Robot Science and Engineering Northeastern University

The coverage problem is one of the key technologies of Wireless Multi-media Sensor Networks(WMSNs). The covering effect has a great influence on the monitoring ability, quality of service, network lifetime of WMSNs. Therefore, a new Multi-node Overlap Sensor Ratio Based Coverage Enhancing Algorithm(M-OSRCEA) is proposed in this paper to achieve the high QoS of WNSNs. Since the 3 D sensing models are more complex but closer to the real situation, a complex 3 D directional sensing model is established in this paper, and the key parameters are calculated precisely. Different from the current 3 D distributed coverage enhancing algorithm, the shock characteristic during coverage enhancing is considered as the key parameter. A multi-node based dynamic shock eliminating mechanism is designed and calculating the overlap ratio of single node, an error reducing method based on size coefficient is presented. In order to evaluate the performance of the proposed algorithm that the simulation experiments are designed. The results show the proposed algorithm is better in coverage-enhancing compared with other distributed coverage enhancing algorithms.

关键词： Wireless Multi-media Sensor Networks(WMSNs) Coverage-enhancing algorithm 3D directional sensing model overlap sensor ratio dynamic shock eliminating

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Observation of the decay

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The European Physical Journal C 2024年第10期84卷 1-24页

作者： Hayrapetyan, A. Tumasyan, A. Adam, W. Andrejkovic, J. W. Bergauer, T. Chatterjee, S. Damanakis, K. Dragicevic, M. Valle, A. Escalante Del Hussain, P. S. Jeitler, M. Krammer, N. Liko, D. Mikulec, I. Schieck, J. Schöfbeck, R. Schwarz, D. Sonawane, M. Templ, S. Waltenberger, W. Wulz, C.-E. Darwish, M. R. Janssen, T. Mechelen, P. Van Bols, E. S. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. El Faham, H. Lowette, S. Makarenko, I. Müller, D. Sahasransu, A. R. Tavernier, S. Tytgat, M. Van Putte, S. Vannerom, D. Clerbaux, B. De Lentdecker, G. Favart, L. Hohov, D. Jaramillo, J. Khalilzadeh, A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Pétré, L. Postiau, N. Thomas, L. Bemden, M. Vanden Velde, C. Vander Vanlaer, P. De Coen, M. Dobur, D. Hong, Y. Knolle, J. Lambrecht, L. Mestdach, G. Rendón, C. Samalan, A. Skovpen, K. Bossche, N. Van Den Wezenbeek, L. Benecke, A. Bruno, G. Caputo, C. Delaere, C. Donertas, I. S. Giammanco, A. Jaffel, K. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Mondal, K. Tran, T. T. Wertz, S. Alves, G. A. Coelho, E. Hensel, C. De Oliveira, T. Menezes Moraes, A. Teles, P. Rebello Soeiro, M. Júnior, W. L. Aldá Pereira, M. Alves Gallo Filho, M. Barroso Ferreira Malbouisson, H. Brandao Carvalho, W. Chinellato, J. Da Costa, E. M. Da Silveira, G. G. De Jesus Damiao, D. De Souza, S. Fonseca Martins, J. Herrera, C. Mora Amarilo, K. Mota Mundim, L. Nogima, H. Santoro, A. Sznajder, A. Thiel, M. Pereira, A. Vilela Bernardes, C. A. Calligaris, L. Tomei, T. R. Fernandez Perez 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. Ivanov, T. Litov, L. Pavlov, B. Petkov, P. Petrov, A. Shumka, E. Keshri, S. Thakur, S. Cheng, T. Guo, Q. Javaid, T. Mittal, M. Yuan, L. Bauer, G. 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. Monti, F. Shahzad, M. A. Sharma, R. Song, J. N. Tao, J. Wang, C. Wang, J. Wang, Z. Zhang, H. Aga 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 Paulista Universidade 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 Now at Henan Normal University Xinxiang China Now at The University of Iowa Iowa City 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 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 Mec

Using proton–proton collision data corresponding to an integrated luminosity of $$140\hbox { fb}^{-1}$$ collected by the CMS experiment at $$\sqrt{s}= 13\,\text {Te}\hspace{-.08em}\text {V} $$ , the $${{{\Lambda }} _{\text {b}}^{{0}}} \rightarrow {{\text {J}/\uppsi }} {{{\Xi }} ^{{-}}} {{\text {K}} ^{{+}}} $$ decay is observed for the first time, with a statistical significance exceeding 5 standard deviations. The relative branching fraction, with respect to the $${{{\Lambda }} _{\text {b}}^{{0}}} \rightarrow {{{\uppsi }} ({2\textrm{S}})} {{\Lambda }} $$ decay, is measured to be $$\mathcal {B}({{{\Lambda }} _{\text {b}}^{{0}}} \rightarrow {{\text {J}/\uppsi }} {{{\Xi }} ^{{-}}} {{\text {K}} ^{{+}}} )/\mathcal {B}({{{\Lambda }} _{\text {b}}^{{0}}} \rightarrow {{{\uppsi }} ({2\textrm{S}})} {{\Lambda }} ) = [3.38\pm 1.02\pm 0.61\pm 0.03]\%$$ , where the first uncertainty is statistical, the second is systematic, and the third is related to the uncertainties in $$\mathcal {B}({{{\uppsi }} ({2\textrm{S}})} \rightarrow {{\text {J}/\uppsi }} {{{\uppi }} ^{{+}}} {{{\uppi }} ^{{-}}} )$$ and $$\mathcal {B}({{{\Xi }} ^{{-}}} \rightarrow {{\Lambda }} {{{\uppi }} ^{{-}}} )$$ .

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Game Scene Construction for Lower Limb Rehabilitation robot Based on Virtual Reality

Game Scene Construction for Lower Limb Rehabilitation Robot ...

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International Conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Yongfei Feng Di Jin Qianjun Shao Jianye Niu Luige Vladareanu Hongbo Wang Faculty of Mechanical Engineering & Mechanics Ningbo University Ningbo China Hebei Key Laboratory of Robot Sensing and Human-Robot Interaction Hebei University of Technology Tianjin China Robotics and Mechatronic Department Institute of Solid Mechanics of the Romanian Academy Bucharest Romania Parallel Robot and Mechatronic System Laboratory of Hebei Province and Key Laboratory of Advanced Forging & Stamping Technology and Science of Ministry of Education Yanshan University Qinhuangdao China

ISBN: (数字)9781728199283

ISBN: (纸本)9781728199290

At present, the number of patients with lower extremity dysfunction is increasing year by year. However, traditional rehabilitation training cannot meet the needs of patients for rehabilitation training. Rehabilitation robot is a good solution to train patients instead of the rehabilitation physician. Based on clinical practice experience, a lower limb rehabilitation training system based on virtual reality (VR) is designed, which combined the VR technology and rehabilitation evaluation program to improve the rehabilitation efficiency of patients. According to the functional requirements of the lower limb rehabilitation robot (LLR-RO), the construction of rehabilitation robot control system and sensor system is completed. Based on the Unity3D development environment, a VR scenes of space shooting is planned and created for rehabilitation training VR technology which enables patients to complete the training process while maintaining a pleasant mood. Through the software and hardware debugging, the feasibility, data transmission capability of VR-based remote rehabilitation training system and the synchronization performance of VR scenes and rehabilitation robots are verified. In the future, more virtual reality scenes will be studied to adapt to patients of different genders and ages.

关键词： Games Training Rehabilitation robotics Space vehicles Legged locomotion Aircraft Aerospace electronics

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Measurements of inclusive and differential cross sections for top quark production in association with a Z boson in proton-proton collisions at = 13 TeV

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Journal of High Energy Physics 2025年第2期2025卷 1-53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. Waltenberger, W. Wulz, C.-E. Janssen, T. Van Laer, T. Van Mechelen, P. Breugelmans, N. D’Hondt, J. Dansana, S. De Moor, A. Delcourt, M. Heyen, F. Lowette, S. Makarenko, I. Müller, D. Tavernier, S. Tytgat, M. Van Onsem, G. P. Van Putte, S. Vannerom, D. Bilin, B. Clerbaux, B. Das, A. K. De Lentdecker, G. Evard, H. Favart, L. Gianneios, P. Jaramillo, J. Khalilzadeh, A. Khan, F. A. Lee, K. Mahdavikhorrami, M. Malara, A. Paredes, S. Shahzad, M. A. Thomas, L. Vanden Bemden, M. Vander Velde, C. Vanlaer, P. De Coen, M. Dobur, D. Gokbulut, G. Hong, Y. Knolle, J. Lambrecht, L. Marckx, D. Mota Amarilo, K. Skovpen, K. Van Den Bossche, N. van der Linden, J. Wezenbeek, L. Benecke, A. Bethani, A. Bruno, G. Caputo, C. De Favereau De Jeneret, J. Delaere, C. Donertas, I. S. Giammanco, A. Guzel, A. O. Jain, Sa. Lemaitre, V. Lidrych, J. Mastrapasqua, P. Tran, T. T. Wertz, S. Alves, G. A. Alves Gallo Pereira, M. Coelho, E. Correia Silva, G. Hensel, C. Menezes De Oliveira, T. Mora Herrera, C. Moraes, A. Rebello Teles, P. Soeiro, M. Vilela Pereira, A. Aldá Júnior, W. L. 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. Laux Kuhn, T. Macedo, M. Martins, J. Mundim, L. Nogima, H. Pinheiro, J. P. Santoro, A. Sznajder, A. Thiel, M. Bernardes, C. A. Calligaris, L. Fernandez Perez Tomei, T. R. Gregores, E. M. Maietto Silverio, I. 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. Laroze, D. Thakur, S. Cheng, T. Javaid, T. Yuan, L. Hu, Z. Liang, Z. Liu, J. Yi, K. Chen, G. M. Chen, H. S. Chen, M. Iemmi, F. Jiang, C. H. Yerevan Physics Institute Yerevan Armenia Yerevan State University Yerevan Armenia Institut für Hochenergiephysik Vienna Austria TU Wien Vienna Austria Universiteit Antwerpen Antwerpen Belgium 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 Paulista Universidade 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 U.S.A. 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 Guangdong Provincial Key Laboratory of Nuclear Science and Guangdong-Hong Kong Joint Laboratory of Quantum Matter South China Normal University Guangzhou 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 Engine

Measurements are presented of inclusive and differential cross sections for Z boson associated production of top quark pairs ( $$ \textrm{t}\overline{\textrm{t}}\textrm{Z} $$ ) and single top quarks (tZq or tWZ). The data were recorded in proton-proton collisions at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb−1. Events with three or more leptons, electrons or muons, are selected and a multiclass deep neural network is used to separate three event categories, the $$ \textrm{t}\overline{\textrm{t}}\textrm{Z} $$ and tWZ processes, the tZq process, and the backgrounds. A profile likelihood approach is used to unfold the differential cross sections, to account for systematic uncertainties, and to determine the correlations between the two signal categories in one global fit. The inclusive cross sections for a dilepton invariant mass between 70 and 110 GeV are measured to be 1.14 ± 0.07 pb for the sum of $$ \textrm{t}\overline{\textrm{t}}\textrm{Z} $$ and tWZ, and 0.81 ± 0.10 pb for tZq, in good agreement with theoretical predictions.

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Standing Assistance Control based on Voluntary Body Movement within Safety Tolerance

Standing Assistance Control based on Voluntary Body Movement...

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International Workshop on Advanced Motion Control (AMC)

作者： Daisuke Chugo Masahiro Yokota Satoshi Muramatsu Sho Yokota Jin-Hua She Hiroshi Hashimoto Takahiro Katayama Yasuhide Mizuta Atsushi Koujina Graduate School of Science and Technology Kwansei Gakuin University Sanda Hyogo Japan School of Science and Information and Telecommunication Engineering Tokai University Hiratsuka Kanagawa Japan Faculty of Science and Engineering Toyo University Kawagoe Saitama Japan School of Engineering Tokyo University of Technology Hachioji Tokyo Japan Master Program of Innovation for Design and Engineering Advanced Institute of Industrial Technology Shinagawa Tokyo Japan Service Robot Division RT.WORKS CO. LTD Osaka Japan

ISBN: (数字)9781728131894

ISBN: (纸本)9781728131900

This paper proposes a novel standing assistance robot, which considers the posture tolerance of its user. In previous studies, conventional assistive robots did not require patients to use their own physical strength to stand, which leads to decreased strength in the elderly. Therefore, an assistive robot that allows patients to maximally use the physical strength they possess is required. To realize this objective, it is important that a robot assists patients according to their body movement and by their intentions. However, in previous studies, general assistive robots helped patients by using a fixed motion reference pathway in spite of their original intention, and as a result, these robots failed to use the physical strength of the patients. In our previous study, we investigated the posture tolerance during a standing motion. In this tolerance, patients could stand by own muscle strength with stable posture. Thus, as the next step, this paper proposes assistance force control scheme which considers the investigated posture tolerance. Our robot generated assistance force, which lead the voluntary movement with their remaining physical strength within investigated range, by combining position control and force control. A prototype assistive robot, based on the proposed idea was fabricated to help patients stand up safely using the maximum of their remaining physical strength.

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Operations & Supply Chain Management: Principles and Practice

arXiv

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arXiv 2025年

作者： Petropoulos, Fotios Akkermans, Henk Aksin, O. Zeynep Ali, Imran Babai, Mohamed Zied Barbosa-Povoa, Ana Battaïa, Olga Besiou, Maria Boysen, Nils Brammer, Stephen Brandon-Jones, Alistair Briskorn, Dirk Browning, Tyson R. Buijs, Paul Centobelli, Piera Chiarini, Andrea Cousins, Paul Cudney, Elizabeth A. Davies, Andrew Day, Steven J. de Koster, René Dekker, Rommert Denicol, Juliano Despeisse, Mélanie Disney, Stephen M. Dolgui, Alexandre Duong, Linh El-Qallali, Malek Fahimnia, Behnam Fakhredin, Fatemeh Gershwin, Stanley B. Ghamat, Salar Giannikas, Vaggelis Glock, Christoph H. Godsell, Janet Govindan, Kannan Hannibal, Claire Haug, Anders Hernaus, Tomislav Hsuan, Juliana Ivanov, Dmitry Jahre, Marianne Johansson, Björn Kalidoss, Madan Shankar Kanellopoulos, Argyris Kannan, Devika Karul, Elif Katsikopoulos, Konstantinos V. Kilic-Ararat, Ayse Begüm Kolisch, Rainer Koppenberg, Maximilian Kumar, Maneesh Kuo, Yong-Hong Kusiak, Andrew Lewis, Michael A. Lim, Stanley Frederick W.T. Limère, Veronique Liu, Jiyin Maghazei, Omid Marić, Matija Meissner, Joern Meuwissen, Miranda Micheli, Pietro Nanayakkara, Samudaya Özdemir, Bengü Nur Papadopoulos, Thanos Pavelin, Stephen Perera, Srinath Phillips, Wendy Prak, Dennis Pun, Hubert Qazi, Sharfah Ahmad Ramanathan, Usha Reiner, Gerald Reitsma, Ewout Roehrich, Jens K. Sanders, Nada R. Sarkis, Joseph Schmid, Nico André Schmidt, Christoph G. Schroeder, Andreas Selviaridis, Kostas Seuring, Stefan Shi, Chuan Son, Byung-Gak Spring, Martin Squire, Brian van der Valk, Wendy van Donk, Dirk Pieter van Houtum, Geert-Jan Wilhelm, Miriam Wynstra, Finn Zheng, Ting School of Management University of Bath Bath United Kingdom Department of Information Systems & Operations Management Tilburg School of Economics and Management Tilburg University Netherlands College of Administrative Sciences and Economics Koc University Turkey School of Business and Law Central Queensland University Australia Kedge Business School France Center for Management Studies Instituto Superior Técnico University of Lisbon Portugal Kühne Logistics University Hamburg Germany Friedrich-Schiller-Universität Jena Lehrstuhl für Operations Management Germany Bergische Universität Wuppertal Germany Neeley School of Business Texas Christian University Fort WorthTX United States Department of Operations University of Groningen Netherlands Department of Industrial Engineering University of Naples "Federico II" Italy Department of Management University of Verona Italy Management School University of Liverpool United Kingdom John E. Simon School of Business Maryville University of Saint Louis Missouri United States Science Policy Research Unit University of Sussex Business School Brighton United Kingdom School of Management Zhejiang University China Rotterdam School of Management Erasmus University Netherlands Erasmus School of Economics Erasmus University Netherlands Bartlett School of Sustainable Construction The Bartlett Faculty of the Built Environment University College London United Kingdom Department of Industrial and Material Sciences Chalmers University of Technology Sweden Center for Simulation Analytics and Modelling University of Exeter Business School United Kingdom IMT Atlantique LS2N-CNRS Nantes France College of Business and Law University of the West of England United Kingdom Rabdan Academy United Arab Emirates Institute of Transport and Logistics Studies The University of Sydney Business School Australia Kühne Logistics University Germany Department of Mechanical Engineering Massachusetts Institute of Technology Massachusetts United S

Operations and Supply Chain Management (OSCM) has continually evolved, incorporating a broad array of strategies, frameworks, and technologies to address complex challenges across industries. This encyclopedic article provides a comprehensive overview of contemporary strategies, tools, methods, principles, and best practices that define the field’s cutting-edge advancements. It also explores the diverse environments where OSCM principles have been effectively implemented. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. © 2025, CC BY.

关键词： Supply chain management

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Computer Aided Diagnosis for Diabetic Retinopathy based on Fundus Image

Computer Aided Diagnosis for Diabetic Retinopathy based on F...

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第37届中国控制会议

作者： Wei Zhou Chengdong Wu Xiaosheng Yu College of Information Science and Engineering Northeastern University Faculty of Robot Science and Engineering Northeastern University

Diabetic Retinopathy（DR） is an eye abnormality caused by long term diabetes, which can lead to vision defects or even blindness. Performing retinal screening examinations on all diabetic patients is a hard work due to the limited number of specialists cannot keep up with the increasing prevalence of diabetes, and hence there are many undiagnosed and untreated cases of DR. Computer aided diagnosis（CAD） is a good way to save the patient’s vision and to help the ophthalmologists in mass screening of diabetes sufferers. The main purpose of the proposed study is to design an automated grading approach for DR screening, using a publicly available database of retinal images, which can evaluate the fundus images like human experts while achieving a high sensitivity for the detection of DR. This paper gives a summary of the results obtained by our previous studies. Together with these results and the DR grading schemes, we provide an automatic analysis approach for the risk of macular edema and the retinopathy grade. Experimental results indicate that the performance of our approach on this database is comparable to that of human experts.

关键词： Diabetic Retinopathy Exudates Optic disc Macula Pattern Recognition

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Recombined Convolutional Neural Network for Recognition of Macular Disorders in SD-OCT Images

Recombined Convolutional Neural Network for Recognition of M...

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第37届中国控制会议

作者： Tianqi Meng Chengdong Wu Tong Jia Yang Jiang Zixi Jia College of Information Science and Engineering Northeastern University Faculty of Robot Science and Engineering Northeastern University

Macular disorders, such as age-related macular degeneration（AMD） and diabetic macular edema（DME）, have plagued humans for many years, yet the traditional recognition methods are often complicated, time-consuming and laborious. Therefore, this paper proposes an effective recombined residual convolutional neural network（CNN） with less computation to recognize AMD, DME and the normal of SD-OCT images. This paper adopts the18-layer Residual CNN as the basis and removes some of convolutional layers in the convolutional groups which extract low-level or high-level features, while add them to the middle part of the CNN, to enhance the ability of extracting mid-level features. This paper also adjusts the kernel size to change the visual receptive fields during convolution calculations. Experiments show that the recombined residual CNN with the kernel size of 3 x3 shows better performance than the original one and recently proposed methods on the same dataset, with the highest overall accuracy up to 90%. Moreover, in terms of feature extraction complexity and time consumption, the recombined residual CNN is also more dominant.

关键词： Macular disorders recombined residual CNN deep learning SD-OCT

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Markov Chain-Based Feature Extraction for Anomaly Detection in Time Series and Its Industrial Application

Markov Chain-Based Feature Extraction for Anomaly Detection ...

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第30届中国控制与决策会议

作者： Dong Zang Jinhai Liu Huaizhen Wang School of Information Science and Engineering Northeastern University Faculty of Robot Science and Engineering Northeastern University

Different time series often represent different meanings,especially when time series are anomalous,carrying out the research of time series is of great *** traditional anomaly detection methods,we propose a special feature extraction method based on transfer probability of Markov chain,so as to deep mining information of the raw time ***,time series is transformed into forms of Markov chain by a simple ***,the transfer probability matrix of Markov chain can be calculated,we name it as Markov ***,key parameters occurred during process of features extraction will be *** then,taking the extracted Markov feature and two simple statistical features(mean and variance) as the feature ***,to verify effectiveness of the proposed method,we use four machine learning methods(i.e.k-NN,RF,DT and SVM) and oil pipeline pressure data in experiment ***,comparatives between the proposed features extraction with other three methods are *** results show that the proposed anomaly detection method is more accurate and effective.

关键词： time series anomaly detection Markov chain oil pipeline

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