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38th International Cosmic Ray Conference, ICRC 2023

作者： Oshima, H. Fujita, K. Ogio, S. Sako, T. Abbasi, R.U. Abe, Y. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cheon, B.G. Chikawa, M. Fedynitch, A. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Furlich, G. Gerber, Z. Globus, N. Hanlon, W. Hayashida, N. He, H. Hibi, R. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawakami, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, S.W. Kimura, Y. Komae, I. Komori, K. Kusumori, Y. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lee, M.J. Lubsandorzhiev, B. Lundquist, J.P. Matsuyama, T. Matthews, J.A. Matthews, J.N. Mayta, R. Miyashita, K. Mizuno, K. Mori, M. Murakami, M. Myers, I. Nagataki, S. Nakai, K. Nakamura, T. Nishio, E. Nonaka, T. Ogio, S. Ohoka, H. Okazaki, N. Oku, Y. Okuda, T. Omura, Y. Onishi, M. Ono, M. Oshima, A. Oshima, H. Ozawa, S. Park, I.H. Park, K.Y. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rott, C. Rubtsov, G.I. Ryu, D. Sagawa, H. Saito, R. Sakaki, N. Sako, T. Sakurai, N. Sato, D. Sato, K. Sato, S. Sekino, K. Shah, P.D. Shibata, N. Shibata, T. Shikita, J. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, K. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, K. Tanaka, M. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Udo, S. Urban, F. Vaiman, I.A. Warren, D. Wong, T. Yamazaki, K. Yashiro, K. Yoshida, F. Zhezher, Y. Zundel, Z. Institute for Cosmic Ray Research The University of Tokyo 5-1-5 Kashiwa-no-ha Chiba Kashiwa Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano380-8554 Japan High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT84112-0830 United States Graduate School of Science Osaka Metropolitan University Sugimoto Sumiyoshi Osaka558-8585 Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul426-791 Korea Republic of Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Institute of Physics Academia Sinica Taipei City115201 Taiwan Nambu Yoichiro Institute of Theoretical and Experimental Physics Osaka Metropolitan University Sugimoto Sumiyoshi Osaka558-8585 Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako351-0198 Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama221-8686 Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu400-8511 Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama338-8570 Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon16419 Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo158-8557 Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow117312 Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo337-8570 Japan Graduate School of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka572-8530 Japan Service de Physique Théorique Université Libre de Bruxelles Brussels Belgium Department of Physics Yonsei University Seodaemun-gu Seoul120-749 Korea Republic of Center for Astrophysics and Cosmology Universi

The TA Low-energy Extension (TALE) experiment extends the low-energy side of the TA experiment below 1015 eV. A main objective of TALE is to study the transition from galactic to extragalactic cosmic rays. The TALE detector is a hybrid observatory composed of fluorescence telescopes and a surface detector array of scintillation counters. The surface detectors are arranged with inter-counter spacing of 400 and 600 meters, suitable for hybrid energy spectrum measurements in the low-energy region. We measured the energy spectrum using data collected during 429 hours of observation by the TALE hybrid detector. This energy spectrum measurement will play an important role in understanding the transition from cosmic rays of galactic origin to those of extragalactic origin. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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Software Project management Effectiveness Evaluation Tool 20

Software Project Management Effectiveness Evaluation Tool

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Proceedings of the 9th International Conference on Software and Information engineering

作者： Mohamed Ellithey Barghoth Akram Salah Manal A Ismail Master in Software Engineering Program Egyptian E-Learning University Giza Egypt Cairo University Giza Egypt Department of Computer Engineering and Systems Helwan University Helwan Egypt

ISBN: (纸本)9781450377218

Project management is important for the success of a software project. Enhancing software project management effectiveness measurements should lead the project manager to advance practices that contribute to the successful development of software projects. This study aims to enhance project management effectiveness measurements in the direction of increase the likelihood of software project success. An enhanced evaluation model of software project management effectiveness has been proposed in this study. Correspondingly, the model fed into a developed software project management effectiveness evaluation tool. This measurement tool helps software development managers to evaluate, monitor and improve project management effectiveness in software projects. The developed software project management effectiveness evaluation tool has been validated by a feedback survey. Feedback survey participants have been demonstrated the importance of the developed project management effectiveness measurement tool.

关键词： Software project management model Measurements of software project management effectiveness Software project management effectiveness Software project success

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FaSTrack:A Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

FaSTrack:A Modular Framework for Real-Time Motion Planning a...

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作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime Fernandez Bansal, Somil Han, Soojean Tomlin, Claire J. School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada Department of Mechanical and Aerospace Engineering University of California San Diego San DiegoCA92122 United States Department of Electrical Engineering Princeton University PrincetonNJ08536 United States Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94704 United States Control and Dynamical Systems Program California Institute of Technology Los AngelesCA90019 United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. © 1963-2012 IEEE.

关键词： Real time systems

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Mining and predicting micro-process patterns of issue resolution for open source software projects 32

Mining and predicting micro-process patterns of issue resolu...

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32nd International Conference on Software engineering and Knowledge engineering, SEKE 2020

作者： Wang, Yiran Cao, Jian Lo, David Department of Computer Science and Engineering Shanghai Jiaotong University Shanghai200240 China School of Information Systems Singapore Management University 178902 Singapore

ISBN: (纸本)1891706500

Addressing issue reports is an integral part of open source software (OSS) projects. Although several studies have attempted to discover the factors that affect issue resolution, few pay attention to the underlying micro-process patterns of resolution processes. Discovering these micro-patterns will help us understand the dynamics of issue resolution processes so that we can manage and improve them in better ways. Of the various types of issues, those relating to corrective maintenance account for nearly half hence resolving these issues efficiently is critical for the success of OSS projects. Therefore, we apply process mining techniques to discover the micro-patterns of resolution processes for issues relating to corrective maintenance. Four and five typical patterns are found for the identification stage and solving stage of the resolution processes respectively. Furthermore, it is shown that the consequent patterns can be predicted with a certain degree of accuracy by selecting the appropriate features and models. Furthermore, we make use of the pattern information predicted to forecast the issue lifetime and the results show that this information can also improve the accuracy in the earlier observation points. At the same time, pattern predictions provide good interpretability to the forecast of issue lifetime. © 2020 Knowledge systems Institute graduate School. All rights reserved.

关键词： Forecasting

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Monocular and Hybrid Analysis for TA×4 Fluorescence Detectors 38

Monocular and Hybrid Analysis for TA×4 Fluorescence Detecto...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Kusumori, Yuki Tameda, Yuichiro Ogio, Shoichi Fujita, Keitaro Tsunesada, Yoshiki Sako, Takashi Sato, Shoma Takagi, Yutaro Komori, Kohei Iwagami, Hirai Abbasi, R.U. Abe, Y. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cheon, B.G. Chikawa, M. Fedynitch, A. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Furlich, G. Gerber, Z. Globus, N. Hanlon, W. Hayashida, N. He, H. Hibi, R. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawakami, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, S.W. Kimura, Y. Komae, I. Komori, K. Kusumori, Y. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lee, M.J. Lubsandorzhiev, B. Lundquist, J.P. Matsuyama, T. Matthews, J.A. Matthews, J.N. Mayta, R. Miyashita, K. Mizuno, K. Mori, M. Murakami, M. Myers, I. Nagataki, S. Nakai, K. Nakamura, T. Nishio, E. Nonaka, T. Ogio, S. Ohoka, H. Okazaki, N. Oku, Y. Okuda, T. Omura, Y. Onishi, M. Ono, M. Oshima, A. Oshima, H. Ozawa, S. Park, I.H. Park, K.Y. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rott, C. Rubtsov, G.I. Ryu, D. Sagawa, H. Saito, R. Sakaki, N. Sako, T. Sakurai, N. Sato, D. Sato, K. Sato, S. Sekino, K. Shah, P.D. Shibata, N. Shibata, T. Shikita, J. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, K. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, K. Tanaka, M. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Udo, S. Urban, F. Vaiman, I.A. Warren, D. Wong, T. Yamazaki, K. Yashiro, K. Yoshida, F. Zhezher, Y. Zundel, Z. Graduate School of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka572-8530 Japan Institute for Cosmic Ray Research The University of Tokyo Kashiwa-no-ha 5-1-5 Kashiwa city Japan Osaka Metropolitan University Sugimoto-ku Osaka558-8585 Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano380-8554 Japan High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT84112-0830 United States Graduate School of Science Osaka Metropolitan University Sugimoto Sumiyoshi Osaka558-8585 Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul426-791 Korea Republic of Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Institute of Physics Academia Sinica Taipei City115201 Taiwan Nambu Yoichiro Institute of Theoretical and Experimental Physics Osaka Metropolitan University Sugimoto Sumiyoshi Osaka558-8585 Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako351-0198 Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama221-8686 Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu400-8511 Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama338-8570 Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon16419 Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo158-8557 Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow117312 Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo337-8570 Japan Service de Physique Théorique Université Libre de Bruxelles Brussels1050 Belgium Department of Physics Yonsei University Seodaemun-gu Seoul120-74

The TA×4 project is an extension of the Telescope Array (TA) experiment, aimed at clarifying the origin of the highest energy cosmic rays. It has deployed 4 fluorescence detectors (FDs) and 130 surface detectors (SDs) at the northeast lobe of the original TA array and 8 FDs and 127 SDs at the southeast lobe of the original TA array, expanding the detection area about four times larger than the TA experiment. This expansion enables us to sample larger data. The TA×4 has been collecting data to obtain solid evidence of the excess of events in the arrival direction distribution, known as the TA hotspot, reported in 2014 by the TA experiment. The north and south observations began in 2018 and 2019, respectively, and are ongoing except for a hiatus from February to June 2020 due to the COVID-19 pandemic. In this presentation, we will report the details of TA×4 FD monocular analysis. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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Measurement of the cosmic ray energy spectrum with the TA×4 SD array 38

Measurement of the cosmic ray energy spectrum with the TA×4...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Fujisue, Kozo Abbasi, R.U. Abe, Y. Abu-Zayyad, T. Allen, M. Arai, Y. Arimura, R. Barcikowski, E. Belz, J.W. Bergman, D.R. Blake, S.A. Buckland, I. Cheon, B.G. Chikawa, M. Fedynitch, A. Fujii, T. Fujisue, K. Fujita, K. Fujiwara, R. Fukushima, M. Furlich, G. Gerber, Z. Globus, N. Hanlon, W. Hayashida, N. He, H. Hibi, R. Hibino, K. Higuchi, R. Honda, K. Ikeda, D. Inoue, N. Ishii, T. Ito, H. Ivanov, D. Iwasaki, A. Jeong, H.M. Jeong, S. Jui, C.C.H. Kadota, K. Kakimoto, F. Kalashev, O. Kasahara, K. Kasami, S. Kawakami, S. Kawata, K. Kharuk, I. Kido, E. Kim, H.B. Kim, J.H. Kim, J.H. Kim, S.W. Kimura, Y. Komae, I. Komori, K. Kusumori, Y. Kuznetsov, M. Kwon, Y.J. Lee, K.H. Lee, M.J. Lubsandorzhiev, B. Lundquist, J.P. Matsuyama, T. Matthews, J.A. Matthews, J.N. Mayta, R. Miyashita, K. Mizuno, K. Mori, M. Murakami, M. Myers, I. Nagataki, S. Nakai, K. Nakamura, T. Nishio, E. Nonaka, T. Ogio, S. Ohoka, H. Okazaki, N. Oku, Y. Okuda, T. Omura, Y. Onishi, M. Ono, M. Oshima, A. Oshima, H. Ozawa, S. Park, I.H. Park, K.Y. Potts, M. Pshirkov, M.S. Remington, J. Rodriguez, D.C. Rott, C. Rubtsov, G.I. Ryu, D. Sagawa, H. Saito, R. Sakaki, N. Sako, T. Sakurai, N. Sato, D. Sato, K. Sato, S. Sekino, K. Shah, P.D. Shibata, N. Shibata, T. Shikita, J. Shimodaira, H. Shin, B.K. Shin, H.S. Shinto, D. Smith, J.D. Sokolsky, P. Stokes, B.T. Stroman, T.A. Takagi, Y. Takahashi, K. Takamura, M. Takeda, M. Takeishi, R. Taketa, A. Takita, M. Tameda, Y. Tanaka, K. Tanaka, M. Thomas, S.B. Thomson, G.B. Tinyakov, P. Tkachev, I. Tokuno, H. Tomida, T. Troitsky, S. Tsuda, R. Tsunesada, Y. Udo, S. Urban, F. Vaiman, I.A. Warren, D. Wong, T. Yamazaki, K. Yashiro, K. Yoshida, F. Zhezher, Y. Zundel, Z. Institute for Cosmic Ray Research Kashiwa-no-ha 5-1-5 Kashiwa city Japan Department of Physics Loyola University Chicago ChicagoIL60660 United States Academic Assembly School of Science and Technology Institute of Engineering Shinshu University Nagano Nagano380-8554 Japan High Energy Astrophysics Institute Department of Physics and Astronomy University of Utah Salt Lake CityUT84112-0830 United States Graduate School of Science Osaka Metropolitan University Sugimoto Sumiyoshi Osaka558-8585 Japan Department of Physics The Research Institute of Natural Science Hanyang University Seongdong-gu Seoul426-791 Korea Republic of Institute for Cosmic Ray Research University of Tokyo Chiba Kashiwa277-8582 Japan Institute of Physics Academia Sinica Taipei City115201 Taiwan Nambu Yoichiro Institute of Theoretical and Experimental Physics Osaka Metropolitan University Sugimoto Sumiyoshi Osaka558-8585 Japan Astrophysical Big Bang Laboratory RIKEN Saitama Wako351-0198 Japan Faculty of Engineering Kanagawa University Kanagawa Yokohama221-8686 Japan Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi Yamanashi Kofu400-8511 Japan The Graduate School of Science and Engineering Saitama University Saitama Saitama338-8570 Japan Department of Physics SungKyunKwan University Jang-an-gu Suwon16419 Korea Republic of Department of Physics Tokyo City University Setagaya-ku Tokyo158-8557 Japan Institute for Nuclear Research The Russian Academy of Sciences Moscow117312 Russia Faculty of Systems Engineering and Science Shibaura Institute of Technology Minato-ku Tokyo337-8570 Japan Graduate School of Engineering Osaka Electro-Communication University Neyagawa-shi Osaka572-8530 Japan Service de Physique Théorique Université Libre de Bruxelles Brussels1050 Belgium Department of Physics Yonsei University Seodaemun-gu Seoul120-749 Korea Republic of Center for Astrophysics and Cosmology University of Nova Gorica Nov

The TA×4 experiment aims to better understand the origin and nature of ultra-high energy cosmic rays (UHECRs) by expanding the observation area of the Telescope Array (TA) experiment by a factor of 4. This expansion will increase the statistics of UHECR events with energies greater than 1019.5 eV. The SD, which means the additionally deployed surface detectors (SD) for the TA×4 experiment, has been collecting data since 2019, and the analysis of this data is currently underway. In this presentation, we will report comparisons between the Monte Carlo simulation and the data obtained by the TA×4 SD array and highlight the agreement between the two. We will also report on the UHECR energy spectrum observed by the TA×4 SD array from October 2019 to September 2022. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Xiong, Ke Du, Hongyang Niyato, Dusit Kang, Jiawen Shen, Xuemin Poor, H. Vincent The Engineering Research Center of Network Management Technology High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Frailty and Other Factors Associated With Early Outcomes in Middle-to Older Age Trauma Patients: A Prospective Cohort Study

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American Journal of Geriatric Psychiatry 2024年第2期32卷 244-255页

作者： Yeh, Tian-Shin Kang, Jiunn-Horng Littlejohns, Thomas J. Wu, Chia-Chieh Chen, Jin-Hua Piravej, Krisna Chiu, Wen-Ta Lam, Carlos Department of Physical Medicine and Rehabilitation School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Physical Medicine and Rehabilitation Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Epidemiology and Nutrition Harvard T. H. Chan School of Public Health Harvard University Boston MA United States Department of Physical Medicine and Rehabilitation National Taiwan University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation College of Medicine National Taiwan University Taipei Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taipei Taiwan Graduate Institute of Nanomedicine and Medical Engineering College of Biomedical Engineering Taipei Medical University Taipei Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Nuffield Department of Population Health University of Oxford Oxford United Kingdom Emergency Department Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Emergency School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei Taiwan Institutional Research Center Office of Data Science Taipei Medical University Taipei Taiwan Biostatistics Center Department of Medical Research Wan Fang Hospital Taipei Medical University Taipei Taiwan Department of Rehabilitation Medicine Faculty of Medicine Chulalongkorn University Bangkok Thailand Department of Chula Neuroscience Center King Chulalongkorn Memorial Hospital Bangkok Thailand Graduate Institute of Injury Prevention and Control College of Public Health Taipei Medical University Taipei Taiwan AHMC Health System Alhambra CA United States

Objectives: To prospectively investigate associations of frailty and other predictor variables with functional recovery and health outcomes in middle-aged and older patients with trauma. Design: Single-center prospective cohort study. Setting: Emergency department of Wan Fang Hospital in Taiwan. Participants: Trauma patients aged 45 and older. Measurements: Frailty was assessed with the Clinical Frailty Scale (CFS). Injury mechanisms, pre-existing diseases, and fracture locations were recorded at baseline. The primary outcome was functional recovery assessed using the Barthel Index (BI). Secondary outcomes were new care needs, unscheduled return visits, and falls 3 months postinjury. Results: A total of 588 participants were included in the final analysis. For every one-point increase in the CFS, the multivariable-adjusted odds ratio (OR, 95% confidence interval [CI]) of failure to retain the preinjury BI was 1.34 (1.16–1.55);associations were consistent across levels of age and injury severities. Significant joint associations of frailty and age with poor functional recovery were observed. CFS was also associated with new care needs (OR for every one-point increase, 1.36, 95% CI, 1.17–1.58), unscheduled return visits (OR 1.26, 95% CI, 1.04–1.51), and falls (OR 1.23, 95% CI, 1.01–1.51). Other variables associated with failure to retain preinjury BI included road traffic accident and presence of hip fracture. Conclusion: Frailty was significantly associated with poor functional and health outcomes regardless of injury severity in middle-aged and older patients with trauma. Injury mechanisms and fracture locations were also significant predictors of functional recovery postinjury. © 2023 American Association for Geriatric Psychiatry

关键词： Clinical Frailty Scale Frailty functional recovery injury middle-to older age

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Numerical study of piezoelectric energy harvester utilizing motorcycle vibrations

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AIP Conference Proceedings 2024年第1期3215卷

作者： Mujahid Abdurrahim Radon Dhelika Mohammad Akita Indianto Mechanical Engineering Department Faculty of Engineering Universitas Indonesia Kampus Baru UI Depok Indonesia 16424 Energy Systems Engineering Graduate Program Faculty of Engineering Universitas Indonesia Kampus Baru UI Depok Indonesia 16424 Tropical Renewable Energy Center Faculty of Engineering Universitas Indonesia Kampus Baru UI Depok Indonesia 16424

A numerical study of the piezoelectric energy harvester is performed to see its visibility for application in harvesting motorcycle vibration. The target vibration of 40-70 Hz is the requirement as it is the frequent vibration during motorcycle operations. A composite cantilever structure of SU-8/Al/PVDF/Al is set as the main body of the structure with a thickness of 200/0.3/5/0.3 µm. The length is set as the study point to see the suitable structure for the application. The study found that by designing the correct length, the cantilever’s resonance frequency can match the target vibration frequency; on the other hand longer cantilever results in higher deflection and energy generation of the piezoelectric structure. The study concludes that the length of 33 mm is suitable for covering the 40-70 Hz target vibration with a resonance frequency of 54.3 Hz. The voltage generated is 24.8 mV. This finding is one important step to developing an energy harvester device that can be used in wide applications.

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Artificial intelligence in gastric cancer:applications and challenges

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Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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