Real-Time Pricing Strategy Based on the Stability of Smart Grid for Green Internet of Things

The ever increasing demand of energy efficiency and the strong awareness of environment have led to the enhanced interests in green Internet of things (IoTs). How to efficiently deliver power, especially, with the smart grid based on the stability of network becomes a challenge for green IoTs. Therefore, in this paper we present a novel real-time pricing strategy based on the network stability in the green IoTs enabled smart grid. Firstly, the outage is analyzed by considering the imbalance of power supply and demand as well as the load uncertainty. Secondly, the problem of power supply with multiple-retailers is formulated as a Stackelberg game, where the optimal price can be obtained with the maximal profit for retailers and users. Thirdly, the stability of price is analyzed under the constraints. In addition, simulation results show the efficiency of the proposed strategy

Mobile Metaverse: A Road Map from Metaverse to Metavehicles

With the rapid development of communication technologies and extended reality (XR), the services and applications of the Metaverse are gradually entering our lives. However, the current development of the Metaverse provides users with services that are homogeneous with the user experience that the Internet has brought in the past, making them more like an extension of the Internet. In addition, as a mobile application carrier for the Metaverse, it is also worth considering how vehicles with diverse onboard components can develop in synergy with the Metaverse. In this article, we focus on the core of the Metaverse, namely user experience, and provide a road map from Metaverse to Metaverse vehicles (Metavehicles). Specifically, we first elaborate on six features of the Metaverse from the perspective of user experience and propose a hierarchical framework for the Metaverse based on the evolutionary logic of the features. Under the guidance of this framework, we discuss the empowerment of onboard components of Metavehicles on the development of the Metaverse, and analyze the service experience that Metavehicles can bring to two types of users, namely drivers and passengers. Finally, considering the differentiated development levels of Metaverse and autonomous driving, we further establish a hierarchical framework for Metavehicles from three aspects (i.e., enhance Metaverse, enhance driving experience, and enhance entertainment experience), providing an evolutionary path for the development of Metavehicles.Comment: 7 pages, 5 figure

Two-dose-level confirmatory study of the pharmacokinetics and tolerability of everolimus in Chinese patients with advanced solid tumors

<p>Abstract</p> <p>Background</p> <p>This phase I, randomized, multicenter, open-label study investigated the pharmacokinetics, safety, and efficacy of the oral mammalian target of rapamycin inhibitor everolimus in Chinese patients with advanced solid tumors.</p> <p>Methods</p> <p>A total of 24 patients with advanced breast cancer (n = 6), gastric cancer (n = 6), non-small cell lung cancer (n = 6), or renal cell carcinoma (n = 6) who were refractory to/unsuitable for standard therapy were randomized 1:1 to oral everolimus 5 or 10 mg/day. Primary end points were pharmacokinetic parameters and safety and tolerability. Pharmacokinetic 24-h profiles were measured on day 15; trough level was measured on days 2, 8, 15, 16, and 22. Tolerability was assessed continuously. This final analysis was performed after all patients had received 6 months of study drug or had discontinued.</p> <p>Results</p> <p>Everolimus was absorbed rapidly; median T_maxwas 3 h (range, 1-4) and 2 h (range, 0.9-6) in the 5 and 10 mg/day groups, respectively. Pharmacokinetic parameters increased dose proportionally from the 5 and 10 mg/day doses. Steady-state levels were achieved by day 8 or earlier. The most common adverse events suspected to be related to everolimus therapy were increased blood glucose (16.7% and 41.7%) and fatigue (16.7% and 33.3%) in the everolimus 5 and 10 mg/day dose cohorts, respectively. Best tumor response was stable disease in 10 (83%) and 6 (50%) patients in the 5 and 10 mg/day groups, respectively.</p> <p>Conclusions</p> <p>Everolimus 5 or 10 mg/day was well tolerated in Chinese patients with advanced solid tumors. The observed safety and pharmacokinetic profile of everolimus from this study were consistent with previous studies.</p> <p>Trial registration</p> <p>Chinese Health Authorities 2008L09346</p

Scalable Resource Management for Dynamic MEC: An Unsupervised Link-Output Graph Neural Network Approach

Deep learning has been successfully adopted in mobile edge computing (MEC) to optimize task offloading and resource allocation. However, the dynamics of edge networks raise two challenges in neural network (NN)-based optimization methods: low scalability and high training costs. Although conventional node-output graph neural networks (GNN) can extract features of edge nodes when the network scales, they fail to handle a new scalability issue whereas the dimension of the decision space may change as the network scales. To address the issue, in this paper, a novel link-output GNN (LOGNN)-based resource management approach is proposed to flexibly optimize the resource allocation in MEC for an arbitrary number of edge nodes with extremely low algorithm inference delay. Moreover, a label-free unsupervised method is applied to train the LOGNN efficiently, where the gradient of edge tasks processing delay with respect to the LOGNN parameters is derived explicitly. In addition, a theoretical analysis of the scalability of the node-output GNN and link-output GNN is performed. Simulation results show that the proposed LOGNN can efficiently optimize the MEC resource allocation problem in a scalable way, with an arbitrary number of servers and users. In addition, the proposed unsupervised training method has better convergence performance and speed than supervised learning and reinforcement learning-based training methods. The code is available at \url{https://github.com/UNIC-Lab/LOGNN}

JIEYUANSHEN DECOCTION EXERTS ANTIDEPRESSANT EFFECTS ON DEPRESSIVE RAT MODEL VIA REGULATING HPA AXIS AND THE LEVEL OF AMINO ACIDS NEUROTRANSMITTER

Background: Jieyuanshen decoction (JYAS-D) - a traditional Chinese medicine was invented by Professor Nie based on classic formulas, chaihu jia longgu muli decoction has been proved as having favorable curative effects on depression in clinical practices. The aim of this study was to investigate the antidepressant effects and its molecular mechanism of JYAS-D. Materials and Methods: The model of depression was established by Chronic Unpredictable Stress. Different doses (8.2 g/kg, 16.3 g/kg, 32.7 g/kg) of JYAS-D was orally administered; Fluoxetine was orally administered with 10mg/kg. All treatments lasted for 28 days. Sucrose preference and open-field tests were adopted to observe the behavior of rats. OPA (ortho-phthalaldehyde) derivatization method was used to detect the contents of amino acid neurotransmitter. RIA (Radiation immunity analysis) method was used to measure the serum concentrations of CORT (Corticosterone), ACTH (Adrenocorticotropic hormone) and CRH (Corticotropin-releasing hormone). ELISA (Enzyme linked immunosorbent assay) method was adopted to examine the contents of Glucocorticoid receptor (GR) and Mineralocorticoid receptor (MR) in hippocampus. Results: Compared with the model group, sucrose preference was increased in all treatment groups. The concentration of serum CORT was reduced in the middle dose of JYAS-D and control groups; the concentration of serum ACTH was reduced in the low and high-dose of JYAS-D; the concentration of serum CRH was reduced in the middle and high-dose of JYAS-D. The content of hippocampus GR was increased in the middle and high-dose of JYAS-D; the content of hippocampus Glu (Glutamic acid) was reduced among the low, middle and high-dose of JYAS-D and fluoxetine group, the ratio of Glu/γ-GABA (γ-aminobutyric acid was reduced in the low and high-dose of JYAS-D. Conclusion: JYAS-D had a significant antidepressant-like effect on rat model through regulating serum concentration of CORT, ACTH and CRH, increasing the content of hippocampus GR and regulating the equilibrium of amino acids neurotransmitter