In this paper, a joint non-orthogonal multiple access and time division multiple access (NOMA-TDMA) scheme is proposed in Industrial Internet of Things (IIoT), which allowed multiple sensors to transmit in the same time-frequency resource block using NOMA. The user scheduling, time slot allocation, and power control are jointly optimized in order to maximize the system α-fair utility under transmit power constraint and minimum rate constraint. The optimization problem is nonconvex because of the fractional objective function and the nonconvex constraints. To deal with the original problem, we firstly convert the objective function in the optimization problem into a difference of two convex functions (D.C.) form, and then propose a NOMA-TDMA-DC algorithm to exploit the global optimum. Numerical results show that the NOMA-TDMA scheme significantly outperforms the traditional orthogonal multiple access scheme in terms of both spectral efficiency and user fairness.
Background: Psoriasis is a chronic autoimmune disease. At present, it is very difficult to treat; however, clinical trials have shown that the traditional Chinese medicine (TCM) treatment of psoriasis has certain advantages. The Chinese herbal medicine Jia Wei Jing Xie Yin (JWJXY) has its origins in Jing Xie Yin, a medicine created by the TCM doctor Wu Jun. Previous studies have shown that JWJXY has good clinical efficacy for patients with blood-heat type psoriasis, but its mechanism is unknown.Methods: This paper aimed to further study the therapeutic effect and mechanism of JWJXY on an imiquimod (IMQ)-induced, psoriasis-like mouse model (0.4 mL, i.g., 6 days). The histopathological skin changes were observed by hematoxylin and eosin (HE) staining, the infiltration of cluster of differentiation 11B (CD11b) and cluster of differentiation 4 (CD4) cells was observed by immunohistochemistry, lymphocyte subsets were detected by flow cytometry, T helper (Th)17 cell expression was perceived by flow cytometry, and Th17 cell-related gene expression was detected by real-time quantitative polymerase chain reaction (qPCR).Results: JWJXY significantly reduced the skin thickness of the IMQ-induced model mouse. Compared with that in the vehicle group, the skin tissue of the mice in the JWJXY group showed significantly reduced infiltration of CD11b + and CD4 + T cells. Flow cytometry results showed that JWJXY decreased the proportion of B220 and Th17 cells in the spleen tissue of the mice. There was no significant effect on the proportion of Th1 or regulatory T cells (Treg) cells. Compared with that in the vehicle group, the skin tissue of the mice in the JWJXY group showed significantly decreased expression of interleukin-17A (IL-17A), IL-17F, retinoic acid receptor-related orphan receptor gamma t (RORγt), IL-1β, interferon gamma (IFN-γ), and tumor necrosis factor alpha (TNF-α) messenger RNA (mRNA). Conclusions:The study confirmed the therapeutic effect of JWJXY on psoriasis. Its mechanism of action might be to inhibit the Th17 cell response but not the Th1 and Treg response.
The metaverse is expected to provide immersive entertainment, education, and business applications. However, virtual reality (VR) transmission over wireless networks is dataand computation-intensive, making it critical to introduce novel solutions that meet stringent quality-of-service requirements. With recent advances in edge intelligence and deep learning, we have developed a novel multi-view synthesizing framework that can efficiently provide computation, storage, and communication resources for wireless content delivery in the metaverse. We propose a three-dimensional (3D)-aware generative model that uses collections of single-view images. These single-view images are transmitted to a group of users with overlapping fields of view, which avoids massive content transmission compared to transmitting tiles or whole 3D models. We then present a federated learning approach to guarantee an efficient learning process.The training performance can be improved by characterizing the vertical and horizontal data samples with a large latent feature space, while low-latency communication can be achieved with a reduced number of transmitted parameters during federated learning. We also propose a federated transfer learning framework to enable fast domain adaptation to different target domains. Simulation results have demonstrated the effectiveness of our proposed federated multi-view synthesizing framework for VR content delivery.
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