Modelling diversity in hospital strategies in city-scale ambulance dispatching with coupled game-theoretic model and discrete-event simulation.

The optimization in the ambulance dispatching process is significant for patients who need early treatments. However, the problem of dynamic ambulance redeployment for destination hospital selection has rarely been investigated. The paper proposes an approach to model and simulate the ambulance dispatching process in multi-agent healthcare environments of large cities.

IMPACT: In-Memory ComPuting Architecture based on Y-FlAsh Technology for Coalesced Tsetlin machine inference.

The increasing demand for processing large volumes of data for machine learning (ML) models has pushed data bandwidth requirements beyond the capability of traditional von Neumann architecture. In-memory computing (IMC) has recently emerged as a promising solution to address this gap by enabling distributed data storage and processing at the micro-architectural level, significantly reducing both latency and energy. In this article, we present In-Memory comPuting architecture based on Y-FlAsh technology for Coalesced Tsetlin machine inference (IMPACT), underpinned on a cutting-edge memory device, Y-Flash, fabricated on a 180 nm complementary metal oxide semiconductor (CMOS) process.

Prognostic Value of Ceramide Dynamics in Patients with Acute Coronary Syndrome.

A dynamic study of ceramide concentrations and their association with recurrent event risk could enhance our understanding of cardiovascular complications. To assess the prognostic value of ceramide concentrations (Cer(d18:1/16:0), Cer(d18:1/18:0), Cer(d18:1/24:1), Cer(d18:1/24:0)) and their dynamics in combination with standard clinical and laboratory parameters and therapeutic interventions in ACS patients. Among 110 ACS patients, triple blood sampling was performed for targeted lipidomic analysis using high-performance liquid chromatography-tandem mass spectrometry.