Internet of Vehicles (IoV) is a hot research niche exploiting the synergy between Cooperative Intelligent Transportation Systems (C-ITS) and the Internet of Things (IoT), which can greatly benefit of the upcoming development of 5G technologies. The variety of end-devices, applications, and Radio Access Technologies (RATs) in IoV calls for new networking schemes that assure the Quality of Service (QoS) demanded by the users. To this end, network slicing techniques enable traffic differentiation with the aim of ensuring flow isolation, resource assignment, and network scalability. This work fills the gap of 5G network slicing for IoV and validates it in a realistic vehicular scenario. It offers an accurate bandwidth control with a full flow-isolation, which is essential for vehicular critical systems. The development is based on a distributed Multi-Access Edge Computing (MEC) architecture, which provides flexibility for the dynamic placement of the Virtualized Network Functions (VNFs) in charge of managing network traffic. The solution is able to integrate heterogeneous radio technologies such as cellular networks and specific IoT communications with potential in the vehicular sector, creating isolated network slices without risking the Core Network (CN) scalability. The validation results demonstrate the framework capabilities of short and predictable slice-creation time, performance/QoS assurance and service scalability of up to one million connected devices.
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http://dx.doi.org/10.3390/s19143107 | DOI Listing |
Alzheimers Dement
December 2024
University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
Background: The common marmoset (Callithrix jacchus) is an important animal model in neuroscience and neurological diseases, presenting primate-specific evolutionary features such as an expanded frontal cortex. We established a new consortium with funding support from the National Institute on Aging to generate, characterize, and validate MArmosets as Research MOdels of AD (MARMO-AD). This consortium develops and studies gene-edited marmoset models carrying genetic risk for AD, comparing them against wild-type aging marmosets from birth throughout their lifespan, using non-invasive longitudinal assessments.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, ON, Canada.
Background: In-vivo detection of neuropathology is critical for early diagnosis of neurodegenerative diseases. Post-mortem brain magnetic resonance imaging (MRI) of pathological protein inclusions could further our ability to detect them in vivo and correlate MRI parameters to histopathological substrates. In this post-mortem study, we aimed to identify MRI correlates of neurodegenerative disease pathology in a brain with various forms of proteinopathies.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Background: Obesity in midlife is a risk factor for developing Alzheimer disease later in life. However, the metabolic and inflammatory effects of body fat varies based on its anatomical localization. In this study, we aimed to investigate the association of MRI-derived abdominal visceral and subcutaneous adipose tissue (VAT and SAT), liver proton-density fat fraction (PDFF), thigh fat-to-muscle ratio (FMR), and insulin resistance with whole-brain amyloid burden in cognitively normal midlife individuals.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Maryland of Baltimore, Baltimore, MD, USA.
Background: Dys-connectivity has been repeatedly shown in Alzheimer's Disease (AD) but the change of connectivity gradient across the brain is under-studied. In this study, we used resting state fMRI (rsfMRI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to build a whole brain functional connectivity matrix. We then compared the major connectivity gradients decomposed from the connectivity matrix from normal controls (NC), mild cognitive impairment (MCI), and AD patients.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.
Background: Within the research field of neurodegenerative disorders, unbiased analysis of body fat composition, particularly muscle mass, is gaining attention as a potential biological marker for refining Alzheimer's disease risk. The objective of this study was to employ a deep learning model for fully automated and accurate segmentation of thigh tissues, potentially contributing to early Alzheimer's diagnostics.
Method: In an IRB-approved study, 49 participants underwent thigh Dixon MRI scans with a TR=9.
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