4,794 research outputs found
On parity functions in conformal field theories
We examine general aspects of parity functions arising in rational conformal
field theories, as a result of Galois theoretic properties of modular
transformations. We focus more specifically on parity functions associated with
affine Lie algebras, for which we give two efficient formulas. We investigate
the consequences of these for the modular invariance problem.Comment: 18 pages, no figure, LaTeX2
Novel chlorhexidine-loaded polymeric nanoparticles for root canal treatment
Persistence of microorganisms in dentinal tubules after root canal chemo-mechanical preparation has been well documented. The complex anatomy of the root canal and dentinal buffering ability make delivery of antimicrobial agents difficult. This work explores the use of a novel trilayered nanoparticle (TNP) drug delivery system that encapsulates chlorhexidine digluconate, which is aimed at improving the disinfection of the root canal system. Chlorhexidine digluconate was encapsulated inside polymeric self-assembled TNPs. These were self-assembled through water-in-oil emulsion from poly(ethylene glycol)-b-poly(lactic acid) (PEG-b-PLA), a di-block copolymer, with one hydrophilic segment and another hydrophobic. The resulting TNPs were physicochemically characterized and their antimicrobial effectiveness was evaluated against Enterococcus faecalis using a broth inhibition method. The hydrophilic interior of the TNPs successfully entrapped chlorhexidine digluconate. The resulting TNPs had particle size ranging from 140–295 nm, with adequate encapsulation efficiency, and maintained inhibition of bacteria over 21 days. The delivery of antibacterial irrigants throughout the dentinal matrix by employing the TNP system described in this work may be an effective alternative to improve root canal disinfection
DESiRED -- Dynamic, Enhanced, and Smart iRED: A P4-AQM with Deep Reinforcement Learning and In-band Network Telemetry
Active Queue Management (AQM) is a mechanism employed to alleviate transient
congestion in network device buffers, such as routers and switches. Traditional
AQM algorithms use fixed thresholds, like target delay or queue occupancy, to
compute random packet drop probabilities. A very small target delay can
increase packet losses and reduce link utilization, while a large target delay
may increase queueing delays while lowering drop probability. Due to dynamic
network traffic characteristics, where traffic fluctuations can lead to
significant queue variations, maintaining a fixed threshold AQM may not suit
all applications. Consequently, we explore the question: \textit{What is the
ideal threshold (target delay) for AQMs?} In this work, we introduce DESiRED
(Dynamic, Enhanced, and Smart iRED), a P4-based AQM that leverages precise
network feedback from In-band Network Telemetry (INT) to feed a Deep
Reinforcement Learning (DRL) model. This model dynamically adjusts the target
delay based on rewards that maximize application Quality of Service (QoS). We
evaluate DESiRED in a realistic P4-based test environment running an MPEG-DASH
service. Our findings demonstrate up to a 90x reduction in video stall and a
42x increase in high-resolution video playback quality when the target delay is
adjusted dynamically by DESiRED.Comment: Preprint (Computer Networks under review
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