An angle-driven computer simulation model of aerial movement was used to determine the maximum amount of twist that could be produced in the second somersault of a double somersault on trampoline using asymmetrical movements of the arms and hips. Lower bounds were placed on the durations of arm and hip angle changes based on performances of a world trampoline champion whose inertia parameters were used in the simulations. The limiting movements were identified as the largest possible odd number of half twists for forward somersaulting takeoffs and even number of half twists for backward takeoffs. Simulations of these two limiting movements were found using simulated annealing optimisation to produce the required amounts of somersault, tilt and twist at landing after a flight time of 2.0s. Additional optimisations were then run to seek solutions with the arms less adducted during the twisting phase. It was found that 3½ twists could be produced in the second somersault of a forward piked double somersault with arms abducted 8° from full adduction during the twisting phase and that three twists could be produced in the second somersault of a backward straight double somersault with arms fully adducted to the body. These two movements are at the limits of performance for elite trampolinists.
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http://dx.doi.org/10.1016/j.jbiomech.2017.05.002 | DOI Listing |
Arch Microbiol
January 2025
Department of Stomatology, The Second Affiliated Hospital, Hengyang Medical College, University of South China, Hengyang, 421001, Hunan, China.
Treponema denticola, a bacterium that forms a "red complex" with Porphyromonas gingivalis and Tannerella forsythia, is associated with periodontitis, pulpitis, and other oral infections. The major surface protein (Msp) is a surface glycoprotein with a relatively well-established overall domain structure (N-terminal, central and C-terminal regions) and a controversial tertiary structure. As one of the key virulence factors of T.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Department of Clinical Pharmacy and Translational Science, The University of Tennessee Health Science Center, Memphis, TN, USA.
Background: The COVID-19 pandemic has highlighted the crucial role of artificial intelligence (AI) in predicting mortality and guiding healthcare decisions. However, AI models may perpetuate or exacerbate existing health disparities due to demographic biases, particularly affecting racial and ethnic minorities. The objective of this study is to investigate the demographic biases in AI models predicting COVID-19 mortality and to assess the effectiveness of transfer learning in improving model fairness across diverse demographic groups.
View Article and Find Full Text PDFSci Total Environ
January 2025
Université Paris Cité - Institut de Physique du globe de Paris, CNRS, F75005 Paris, France.
Nanoparticles (NPs) exhibit high reactivity and mobility in the environment, and a significant capacity to penetrate living organisms, potentially leading to harmful effects. Volcanoes are the second major source of natural NPs emitted into the atmosphere, with an estimated flux of 342 Tg/year. Few studies have focused on their fate.
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January 2025
IMT Atlantique, Lab-STICC, UMR CNRS 6285, team RAMBO, F-29238 Brest, France.
Rehabilitation is the process of helping people regain or improve lost or impaired function due to injury, illness, or disease. To assist in tracking the progress of patients undergoing rehabilitation, this paper proposes a lightweight graph-based deep-learning model for the automatic assessment of physical rehabilitation exercises. The model takes as input the 3D skeleton sequence of a patient performing a movement and outputs a continuous quality score, as a means for patient supervision that could complement or even substitute the need for ordinary clinical exams.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, London, Surrey, SM2 5PT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
This study aims to develop and evaluate a fast and robust deep learningbased auto-segmentation approach for organs at risk in MRI-guided radiotherapy of pancreatic cancer to overcome the problems of time-intensive manual contouring in online adaptive workflows. The research focuses on implementing novel data augmentation techniques to address the challenges posed by limited datasets. Approach: This study was conducted in two phases.
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