1,046 research outputs found
Yielding and hardening of flexible fiber packings during triaxial compression
This paper examines the mechanical response of flexible fiber packings
subject to triaxial compression. Short fibers yield in a manner similar to
typical granular materials in which the deviatoric stress remains nearly
constant with increasing strain after reaching a peak value. Interestingly,
long fibers exhibit a hardening behavior, where the stress increases rapidly
with increasing strain at large strains and the packing density continuously
increases. Phase diagrams for classifying the bulk mechanical response as
yielding, hardening, or a transition regime are generated as a function of the
fiber aspect ratio, fiber-fiber friction coefficient, and confining pressure.
Large fiber aspect ratio, large fiber-fiber friction coefficient, and large
confining pressure promote hardening behavior. The hardening packings can
support much larger loads than the yielding packings contributing to the
stability and consolidation of the granular structure, but larger internal
axial forces occur within fibers.Comment: 14 pages, 4 figure
Preliminary analysis of PGRP-LC gene and structure characteristics in bumblebees
PGRP-LC is a significant pattern recognition receptor of the insect innate immune system that can recognize peptidoglycans and activate immune signaling pathways regulating the expression and release of antimicrobial peptides against infection. We for the first time analyzed the phylogenetic tree, purification and structure of bumblebee PGRP-LC. The results showed high conservation of bumblebee PGRP-LC among the 16 bumblebee species, and further phylogenetic analysis showed that the PGRP-LC phylogeny of different subgenera (Subterraneobombus, Megabombus, Melanobombus, Bombus) is consistent with that of the COI gene. Additionally, the phylogeny of PGRP-LCs among Bombus, Apis and the solitary bee Megachile rotundata coincides with the sociality evolution of bees. Moreover, bumblebee PGRP-LC (Bl-PGRP-LC) shares the Drosophila PGRP-LCx and PGRP-LCa topology, retaining conserved disulfide bonds and 80% binding residues involved in the interaction between TCT and PGRP-LCx. Therefore, Bl-PGRP-LC might share some similar binding characteristics with Drosophila PGRP-LCx. In addition, Bl-PGRP-LC has shorter β5 and β1 sheets, longer β2, β3, and β4 sheets and a shallow binding groove. To determine the characteristics of Bl-PGRP-LC, high-purity PGRP-LC inclusion bodies, soluble GST-tag Bl-PGRP-LC fusion protein and soluble pure Bl-PGRP-LC were obtained in vitro. The results will be helpful for further study of the function and structure of Bl-PGRP-LC
LMaaS: Exploring Pricing Strategy of Large Model as a Service for Communication
The next generation of communication is envisioned to be intelligent
communication, that can replace traditional symbolic communication, where
highly condensed semantic information considering both source and channel will
be extracted and transmitted with high efficiency. The recent popular large
models such as GPT4 and the boosting learning techniques lay a solid foundation
for the intelligent communication, and prompt the practical deployment of it in
the near future. Given the characteristics of "training once and widely use" of
those multimodal large language models, we argue that a pay-as-you-go service
mode will be suitable in this context, referred to as Large Model as a Service
(LMaaS). However, the trading and pricing problem is quite complex with
heterogeneous and dynamic customer environments, making the pricing
optimization problem challenging in seeking on-hand solutions. In this paper,
we aim to fill this gap and formulate the LMaaS market trading as a Stackelberg
game with two steps. In the first step, we optimize the seller's pricing
decision and propose an Iterative Model Pricing (IMP) algorithm that optimizes
the prices of large models iteratively by reasoning customers' future rental
decisions, which is able to achieve a near-optimal pricing solution. In the
second step, we optimize customers' selection decisions by designing a robust
selecting and renting (RSR) algorithm, which is guaranteed to be optimal with
rigorous theoretical proof. Extensive experiments confirm the effectiveness and
robustness of our algorithms
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