A heterogeneous wireless network (HWN) environment contains many kinds of wireless networks, such as UMTS, LTE, and WLAN, where users move around within their coverage area. How to ensure mobile users select the most suitable network is a hot research topic for HWNs. Owing to the mobility of users, the interference of wireless signals, and the fluctuation of network status, the network attribute values obtained by mobile users are often uncertain. However, the traditional access-selection algorithms assume that mobile users can obtain accurate network attribute values, which makes users unable to access the most appropriate network. To solve this problem, this paper designs an access-selection algorithm for HWNs in the context of inaccurate network attribute values. First, the algorithm calculates the network attribute values based on the hesitant fuzzy theory, then calculates the weights of network attributes using the fuzzy analytic hierarchy process (FAHP), and finally sorts the candidate networks using the hesitant fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. The simulation results show that the proposed algorithm enables users to select the most suitable network to access under the inaccurate network attribute environment and obtain higher gains.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8894056 | PMC |
http://dx.doi.org/10.1155/2022/4646889 | DOI Listing |
Crit Care
January 2025
HCor Research Institute, Hospital do Coração, Rua Desembargador Eliseu Guilherme 200, 8th Floor, São Paulo, SP, 04004-030, Brazil.
Background: Limited data is available to evaluate the burden of device associated healthcare infections (HAI) [central line associated bloodstream infection (CLABSI), catheter associated urinary tract infection (CAUTI), and ventilator associated pneumonia (VAP)] in low and-middle-income countries. Our aim is to investigate the population attributable mortality fraction and the absolute mortality difference of HAI in a broad population of critically ill patients from Brazil.
Methods: Multicenter cohort study from September 2019 to December 2023 with prospective individual patient data collection.
Objectives: Patient-sharing networks based on administrative data are used to understand the organisation of healthcare. We examined the patient-sharing networks between different professionals taking care of patients with mental health or substance use problems.
Design: Register study based on the Register of Primary Health Care visits (Avohilmo) that covers all outpatient primary health care visits in Finland.
Alzheimers Dement
December 2024
University of Michigan, Ann Arbor, MI, USA.
Background: Inhibitory interneurons normally regulate neural networks underlying memory and cognition, but are disrupted in Alzheimer's disease. Proper interneuron activity reduces amyloid-beta, whereas hyperexcitability elevates amyloid levels. Still, the underlying pathologic processes mediating interneuron dysfunction remain unknown.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
NYU Grossman School of Medicine, New York, NY, USA.
Background: Down syndrome (DS) is strongly associated with Alzheimer's disease (AD), attributable to APP overexpression, displaying common features with early-onset AD (EOAD) and late-onset AD (LOAD) like Amyloid-β (Aβ) and tau pathology. Here, we evaluated the Aβ plaques proteome of DS, EOAD and LOAD.
Method: We used unbiased localized proteomics to analyze amyloid plaques and the adjacent plaque-devoid tissue ('AD non-plaque') from post-mortem paraffin-embedded tissues in three subtypes of AD (n = 20/group): DS (59.
Alzheimers Dement
December 2024
Department of Clinical Neurosciences and Cambridge University Hospitals NHS Trust, University of Cambridge, Cambridge, United Kingdom.
Background: Frontotemporal dementia (FTD) and Progressive Supranuclear Palsy (PSP) have distinct molecular pathologies, with Tau and TDP43 aggregation, and distinct patterns of regional brain atrophy. However, they share the synaptotoxicity of protein aggregation, and neurotransmitter loss (including GABA), which contribute to clinical and neurophysiological similarities. Defining the relationships between synaptic loss, network physiology and cognition builds bridges between preclinical and clinical studies, and facilitates early phase trials.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!