In this note is shown that the controller proposed in the paper [ISA Trans 71 (2017) 196-205] has the conceptual flaw of not being decentralized as claimed. A corrected control law is then proposed, maintaining most of the characteristics of the original controller while using only the available information for each agent. The stability of this proposed corrected controller is shown using Lyapunov function technique.
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http://dx.doi.org/10.1016/j.isatra.2020.04.011 | DOI Listing |
JMIR Form Res
January 2025
School of Nursing, Li Ka Shing Faculty of Medicine, University of Hong Kong, 5/F, Academic Building, Pokfulam, Hong Kong, China (Hong Kong), 852 39176690.
Background: Breastfeeding is vital for the health and well-being of both mothers and infants, and it is crucial to create supportive environments that promote and maintain breastfeeding practices.
Objective: The objective of this paper was to describe the development of a breastfeeding-friendly app called "bfGPS" (HKU TALIC), which provides comprehensive territory-wide information on breastfeeding facilities in Hong Kong, with the goal of fostering a breastfeeding-friendly community.
Methods: The development of bfGPS can be categorized into three phases, which are (1) planning, prototype development, and preimplementation evaluation; (2) implementation and updates; and (3) usability evaluation.
Drug Des Devel Ther
January 2025
Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Korean J Neurotrauma
December 2024
Department of Neurosurgery, Chungnam National University Hospital, Chungnam National University School of Medicine, Daejeon, Korea.
Korean J Neurotrauma
December 2024
Department of Neurosurgery, Deyang People's Hospital, Deyang, China.
Comput Struct Biotechnol J
December 2024
Department of Assisted Reproduction, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, China.
Manual semen evaluation methods are subjective and time-consuming. In this study, a deep learning algorithmic framework was designed to enable non-invasive multidimensional morphological analysis of live sperm in motion, improve current clinical sperm morphology testing methods, and significantly contribute to the advancement of assisted reproductive technologies. We improved the FairMOT tracking algorithm by incorporating the distance and angle of the same sperm head movement in adjacent frames, as well as the head target detection frame IOU value, into the cost function of the Hungarian matching algorithm.
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