Oscillation of Third-Order Neutral Delay Differential Equations

The purpose of this paper is to examine oscillatory properties of the third-order neutral delay differential equation [a(t)(b(t)(x(t)+p(t)x(σ(t)))′)′]′+q(t)x(τ(t))=0. Some oscillatory and asymptotic criteria are presented. These criteria improve and complement those results in the literature. Moreover, some examples are given to illustrate the main results

Različiti utjecaji latentne topline u planetarnom graničnom sloju i mikrofizičkih procesa u oblacima na tajfun Sarika (2016)

Three simulation experiments were conducted on Typhoon (TC) “Sarika” (2016) using the WRF model, different effects of the latent heat in planetary boundary layer and cloud microphysical process on the TC were investigated. The control experiment well simulated the changes in TC track and intensity. The latent heat in planetary boundary layer or cloud microphysics process can affect the TC track and moving speed. Latent heat affects the TC strength by affecting the TC structure. Compared with the CTL experiment, both the NBL experiment and the NMP experiment show weakening in dynamics and thermodynamics characteristics of TC. Without the effect of latent heat, the TC cannot develop upwards and thus weakens in its intensity and reduces in precipitation; this weakening effect appears to be more obvious in the case of closing the latent heat in planetary boundary layer. The latent heat in planetary boundary layer mainly influences the generation and development of TC during the beginning stage, whereas the latent heat in cloud microphysical process is conducive to the strengthen and maintenance of TC in the mature stage. The latent heat energy of the cloud microphysical process in the TC core region is an order of magnitude larger than the surface enthalpy. But the latent heat release of cloud microphysical processes is not the most critical factor for TC enhancement, while the energy transfer of boundary layer processes is more important.Kako bi se ispitali različiti utjecaji latentne topline u planetarnom graničnom sloju i mikrofizičkih procesa u oblacima, WRF modelom su provedena tri eksperimenta za tajfun (TC) “Sarika” (2016). Kontrolnim eksperimentom dobro su simulirane promjene intenziteta i putanja TC-a. Latentna toplina u planetarnom graničnom sloju te mikrofizički procesi u oblacima mogu utjecati na putanju TC i na brzinu njegovog gibanja. Latentna toplina utječe na jačinu TC-a putem promjene strukture TC-a. U usporedbi s CTL eksperimentom i NBL i NMP eksperiment ukazuju na slabljenje dinamike i termodinamičkih svojstava TC-a. Bez utjecaja latentne topline TC se ne može vertikalno razvijati i stoga mu intenzitet slabi, a količina oborine se reducira; to slabljenje je očitije u slučaju kada je latentna toplina ograničena na planetarni granični sloj. Latentna toplina u planetarnom graničnom sloju uglavnom utječe na stvaranje i razvoj TC-a u početnoj fazi, dok latentna toplina vezana za mikrofizičke procese u oblaku pogoduje jačanju i održavanju njegove zrele faze. Latentna toplina mikrofizičkih procesa u oblakcima u jezgri TC-a je za red veličine veća od prizemne entalpije. Međutim, oslobađanje latentne topline pri mikrofizičkim procesima u oblacima nije najvažnije za jačanje TC-a, već je za njegovo jačanje važniji transfer energije u procesima planetarnog graničnog sloja.

RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning

The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multi-task learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.Comment: 11 pages, 8 figure

NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services

Large language models (LLMs) have triggered tremendous success to empower daily life by generative information, and the personalization of LLMs could further contribute to their applications due to better alignment with human intents. Towards personalized generative services, a collaborative cloud-edge methodology sounds promising, as it facilitates the effective orchestration of heterogeneous distributed communication and computing resources. In this article, after discussing the pros and cons of several candidate cloud-edge collaboration techniques, we put forward NetGPT to capably deploy appropriate LLMs at the edge and the cloud in accordance with their computing capacity. In addition, edge LLMs could efficiently leverage location-based information for personalized prompt completion, thus benefiting the interaction with cloud LLMs. After deploying representative open-source LLMs (e.g., GPT-2-base and LLaMA model) at the edge and the cloud, we present the feasibility of NetGPT on the basis of low-rank adaptation-based light-weight fine-tuning. Subsequently, we highlight substantial essential changes required for a native artificial intelligence (AI) network architecture towards NetGPT, with special emphasis on deeper integration of communications and computing resources and careful calibration of logical AI workflow. Furthermore, we demonstrate several by-product benefits of NetGPT, given edge LLM's astonishing capability to predict trends and infer intents, which possibly leads to a unified solution for intelligent network management \& orchestration. In a nutshell, we argue that NetGPT is a promising native-AI network architecture beyond provisioning personalized generative services

Circulating tissue factor-positive procoagulant microparticles in patients with type 1 diabetes

Aim: To investigate the count of circulating tissue factor-positive (TF+) procoagulant microparticles (MPs) in patients with type 1 diabetes mellitus (T1DM). Methods: This case-control study included patients with T1DM and age and sex-matched healthy volunteers. The counts of phosphatidylserine-positive (PS+) MPs and TF(+)PS(+)MPs and the subgroups derived from different cell types were measured in the peripheral blood sample of the two groups using multicolor flow cytometric assay. We compared the counts of each MP between groups as well as the ratio of the TF(+)PS(+)MPs and PS(+)MPs (TF(+)PS(+)MPs/PS(+)MPs). Results: We recruited 36 patients with T1DM and 36 matched healthy controls. Compared with healthy volunteers, PS(+)MPs, TF(+)PS(+)MPs and TF(+)PS(+)MPs/PS(+)MPs were elevated in patients with T1DM (PS(+)MPs: 1078.5 +/- 158.08 vs 686.84 +/- 122.04/mu L, P &lt;0.001; TF(+)PS(+)MPs: 202.10 +/- 47.47 vs 108.33 +/- 29.42/mu L, P &lt;0.001; and TF(+)PS(+)MPs/PS(+)MPs: 0.16 +/- 0.04 vs 0.19 +/- 0.05, P = 0.004), mostly derived from platelet, lymphocytes and endothelial cells. In the subgroup analysis, the counts of total and platelet TF(+)PS(+)MPs were increased in patients with diabetic retinopathy (DR) and with higher HbA1c, respectively. Conclusion: Circulating TF(+)PS(+)MPs and those derived from platelet, lymphocytes and endothelial cells were elevated in patients with T1DM.De tre första författarna delar förstaförfattarskapet.</p

Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles

Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However, the widely assumed existence of a central node to implement centralized federated learning-assisted MARL might be impractical in highly dynamic scenarios, and the excessive communication overheads possibly overwhelm the IoV system. Therefore, in this paper, we design a communication efficient cooperative MARL algorithm, named RSM-MAPPO, to reduce the communication overheads in a fully distributed architecture. In particular, RSM-MAPPO enhances the multi-agent Proximal Policy Optimization (PPO) by incorporating the idea of segment mixture and augmenting multiple model replicas from received neighboring policy segments. Afterwards, RSM-MAPPO adopts a theory-guided metric to regulate the selection of contributive replicas to guarantee the policy improvement. Finally, extensive simulations in a mixed-autonomy traffic control scenario verify the effectiveness of the RSM-MAPPO algorithm