Results 131 to 140 of about 4,413,107 (338)

Generative Adversarial Network Performance in Low-Dimensional Settings. [PDF]

open access: yesJ Res Natl Inst Stand Technol, 2021
Jimenez F, Koepke A, Gregg M, Frey M.
europepmc   +1 more source

EARTH NETWORKS LIGHTNING NETWORK PERFORMANCE

open access: yes, 2021
Richard Sonnenfeld   +4 more
openaire   +1 more source

Performance analysis of a hierarchical shipboard Wireless Sensor Network

open access: green, 2012
Hussein Kdouh   +5 more
openalex   +2 more sources

Engineered extracellular vesicles enriched with the miR‐214/199a cluster enhance the efficacy of chemotherapy in ovarian cancer

open access: yesMolecular Oncology, EarlyView.
Loss of the miR‐214/199a cluster is associated with recurrence in ovarian cancer. Engineered small extracellular vesicles (m214‐sEVs) elevate miR‐214‐3p/miR‐199a‐5p in tumor cells, suppress β‐catenin, TLR4, and YKT6 signaling, reprogram tumor‐derived sEV cargo, reduce chemoresistance and migration, and enhance carboplatin efficacy and survival in ...
Weida Wang   +12 more
wiley   +1 more source

Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk

open access: yesApplied Sciences
With the emergence of Vehicle-to-Everything (V2X) systems, it is important to investigate how deteriorating network parameters affect vehicle functionality based on wireless communication.
Roland Nagy   +2 more
doaj   +1 more source

Keratin 19 as a prognostic marker and contributing factor of metastasis and chemoresistance in high‐grade serous ovarian cancer

open access: yesMolecular Oncology, EarlyView.
Keratin 19 (KRT19) is overexpressed in high‐grade serous ovarian cancer with high levels of Kallikrein‐related peptidases (KLK) 4–7 and is associated with poor survival. In vivo analyses demonstrate that elevated KRT19 increases peritoneal tumour burden.
Sophia Bielesch   +13 more
wiley   +1 more source

Estimating Quality-of-Service in Urban Vehicular Networks Through Machine Learning

open access: yesIEEE Access
Machine Learning (ML) has emerged as a promising tool for addressing complex challenges in multiple domains. In the context of Vehicular Ad-Hoc Networks (VANETs), ML has gained much more attention due to its ability to solve major known problems in areas
Duarte Dias   +3 more
doaj   +1 more source

Home - About - Disclaimer - Privacy