Many investigations have been carried out in order to develop models which allow the understanding of complex physical processes involved in urban flooding. The modelling of the interactions between overland flows on streets and flooding flows from rivers and sewer networks is one of the main objectives of recent and current research programs in hydraulics and urban hydrology. However, the modelling of the discharge distribution in the street network with crossroad needs further research due to the complexity of the flow through junctions. This paper outlines the ability of the improved one-dimensional CANOE software to simulate the street flows through the virtual network (developed under the Hy(2)Ville French National project framework) with several cross-roads. The improvements are done by adding in CANOE the energy losses coefficients deriving from the calibration phase based on the experimental study of the flow through small scale physical model of cross-road channels. Comparisons between 1D and 2D simulated distribution of discharges through the virtual network show a good agreement for the global distribution. However, large differences are observed focusing on the individual cross-road intersections in the virtual network.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2166/wst.2010.133 | DOI Listing |
BMJ Open
January 2025
Department of Rheumatology and Physiotherapy, Third Faculty of Medicine, Charles University and Thomayer University Hospital, Prague, Czech Republic
Introduction: Upper limb (UL) impairment is common in people with multiple sclerosis (pwMS), and functional recovery of the UL is a key rehabilitation goal. Technology-based approaches, like virtual reality (VR), are increasingly promising. While most VR environments are task-oriented, our clinical approach integrates neuroproprioceptive 'facilitation and inhibition' (NFI) principles.
View Article and Find Full Text PDFPoult Sci
January 2025
Hebei Agricultural University, Baoding, Hebei 071000, China; Key Laboratory of Intelligent Equipment and New Energy Utilization in Livestock and Poultry Farming of Hebei Province, Baoding, Hebei 071000, China.
At present, in the context of the highly intensive development of livestock and poultry breeding, digital management is becoming increasingly important, and digital twin systems are gradually being applied. To solve the contradiction between data acquisition and sensor network congestion, a virtual acquisition method based on historical data and real-time reference of point data is proposed when constructing a digital twin system. Firstly, computational fluid dynamics (CFD) simulation was used to analyze and determine the temperature distribution and environmental characteristics inside the layer house, and the collection area was preliminarily divided according to the CFD simulation results.
View Article and Find Full Text PDFJ Aging Health
January 2025
Université de Sherbrooke, Sherbrooke, QC, Canada.
This study aimed to document the typology of social participation and network among older Canadians and examine their associations with health. Using 2011-2015 cross-sectional data from the Canadian Longitudinal Study on Aging, a latent profile analysis was conducted to identify patterns of social participation and network, and multinomial logistic regressions examined associations with self-rated health. Four types of social participation and networks characterized older Canadians: diverse (74.
View Article and Find Full Text PDFAnesthesiology
January 2025
Department of Anesthesiology, Brigham and Women's Hospital and Harvard Medical School, Boston MA, USA.
Introduction: Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. We sought to determine whether internal EEG subparameters extracted by the Bispectral Index (BIS) monitor, a device commonly used to estimate depth-of-anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest.
Methods: In this retrospective cohort study, we trained a 3-layer neural network to predict recovery of consciousness to the point of command following versus not based on 48 hours of continuous EEG recordings in 315 comatose patients admitted to a single US academic medical center after cardiac arrest (Derivation cohort: N=181; Validation cohort: N=134).
Background: Amyloid PET (Positron Emission Tomography) is crucial in detecting amyloid burden within the brain. However, the diversity of amyloid tracers and the scarcity of paired data significantly challenge the collaboration between cross-center studies. In this research, we introduce a novel patch-based 3D end-to-end image transformation model.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!