The influence of additive content on microstructure and mechanical properties on the Csf/SiC composites after annealed treatment

AbstractIn this paper, micrometers long and 20–100nm diameter SiC nanowires had been synthesized in the short cut fiber toughened SiC composites (Csf/SiC) by annealing treatment. The present work demonstrated that it was possible to fabricate the in situ SiC nanowires toughened Csf/SiC composites by annealed treatment. The “vapor–liquid–solid” growth mechanism of the SiC nanowires was proposed. The mainly toughened mechanism concluded grain bridging, crack deflection, fiber debonding and SiC nanowires, which can improve fracture toughness

A Two-Stage Framework with Self-Supervised Distillation For Cross-Domain Text Classification

Cross-domain text classification aims to adapt models to a target domain that lacks labeled data. It leverages or reuses rich labeled data from the different but related source domain(s) and unlabeled data from the target domain. To this end, previous work focuses on either extracting domain-invariant features or task-agnostic features, ignoring domain-aware features that may be present in the target domain and could be useful for the downstream task. In this paper, we propose a two-stage framework for cross-domain text classification. In the first stage, we finetune the model with mask language modeling (MLM) and labeled data from the source domain. In the second stage, we further fine-tune the model with self-supervised distillation (SSD) and unlabeled data from the target domain. We evaluate its performance on a public cross-domain text classification benchmark and the experiment results show that our method achieves new state-of-the-art results for both single-source domain adaptations (94.17% $\uparrow$1.03%) and multi-source domain adaptations (95.09% $\uparrow$1.34%)

A Novel Iron Phosphate Cement Derived from Copper Smelting Slag and its Early Age Hydration Mechanism

Copper slag (CS), a by-product of copper smelting, is normally stockpiled, leading to wastes of resource and space as well as environment pollution. It has not been massively reutilized as a supplementary cementitious material in Portland cement due to its low reactivity. In the present study, CS is for the first time utilized as the base component to prepare an iron phosphate cement (IPC) by reacting with ammonium dihydrogen phosphate (ADP) at room temperature. The influence of the raw materials mass ratio (CS/ADP) on the microstructure and performance of IPC pastes are investigated. It is found that the compressive strength of IPC pastes at all ages is not a monotonic function of CS/ADP, and the paste with CS/ADP of 2.0 gives the highest strengths, i.e., 26.8, 38.9 and 47.5 MPa at 1, 3 and 28 d, respectively. The crystalline phases including FeH2P3O10·H2O and FePO4 are formed as the main reaction products to bind the unreacted CS particles. The early age hydration of IPC is found to be a multi-stage process, involving the initial dissolution of ADP and iron-containing phases of CS, the formation of FeH2P3O10·H2O, the initial generation of FePO4, and the attainment of the hydration reaction equilibrium. Unlike the magnesium phosphate cement, a redox reaction of Fe(Ⅱ) into Fe(Ⅲ) occurs due to the suitable range of pH and oxidation-reduction potential of the IPC system during the hydration reaction

Robust Semantic Communications with Masked VQ-VAE Enabled Codebook

Although semantic communications have exhibited satisfactory performance for a large number of tasks, the impact of semantic noise and the robustness of the systems have not been well investigated. Semantic noise refers to the misleading between the intended semantic symbols and received ones, thus cause the failure of tasks. In this paper, we first propose a framework for the robust end-to-end semantic communication systems to combat the semantic noise. In particular, we analyze sample-dependent and sample-independent semantic noise. To combat the semantic noise, the adversarial training with weight perturbation is developed to incorporate the samples with semantic noise in the training dataset. Then, we propose to mask a portion of the input, where the semantic noise appears frequently, and design the masked vector quantized-variational autoencoder (VQ-VAE) with the noise-related masking strategy. We use a discrete codebook shared by the transmitter and the receiver for encoded feature representation. To further improve the system robustness, we develop a feature importance module (FIM) to suppress the noise-related and task-unrelated features. Thus, the transmitter simply needs to transmit the indices of these important task-related features in the codebook. Simulation results show that the proposed method can be applied in many downstream tasks and significantly improve the robustness against semantic noise with remarkable reduction on the transmission overhead.Comment: 16 pages, 11 figures. arXiv admin note: text overlap with arXiv:2202.0333

A Unified Multi-Task Semantic Communication System for Multimodal Data

Task-oriented semantic communication has achieved significant performance gains. However, the model has to be updated once the task is changed or multiple models need to be stored for serving different tasks. To address this issue, we develop a unified deep learning enabled semantic communication system (U-DeepSC), where a unified end-to-end framework can serve many different tasks with multiple modalities. As the difficulty varies from different tasks, different numbers of neural network layers are required for various tasks. We develop a multi-exit architecture in U-DeepSC to provide early-exit results for relatively simple tasks. To reduce the transmission overhead, we design a unified codebook for feature representation for serving multiple tasks, in which only the indices of these task-specific features in the codebook are transmitted. Moreover, we propose a dimension-wise dynamic scheme that can adjust the number of transmitted indices for different tasks as the number of required features varies from task to task. Furthermore, our dynamic scheme can adaptively adjust the numbers of transmitted features under different channel conditions to optimize the transmission efficiency. According to simulation results, the proposed U-DeepSC achieves comparable performance to the task-oriented semantic communication system designed for a specific task but with significant reduction in both transmission overhead and model size

Probiotics, Prebiotics, and Synbiotics Improve Uremic, Inflammatory, and Gastrointestinal Symptoms in End-Stage Renal Disease With Dialysis: A Network Meta-Analysis of Randomized Controlled Trials

BackgroundProbiotics, prebiotics, and synbiotics are three different supplements to treat end stage renal disease (ESRD) patients by targeting gut bacteria. The comprehensive comparison of the effectiveness of different supplements are lacking.ObjectivesThe purpose of this network meta-analysis (NMA) is to assess and rank the efficacy of probiotics, prebiotics, and synbiotics on inflammatory factors, uremic toxins, and gastrointestinal symptoms (GI symptoms) in ESRD patients undergoing dialysis.MethodsRandomized clinical trials were searched from the PubMed, Embase, and Cochrane Register of Controlled Trials databases, from their inception until 4 September 2021. Random-effect model were used to obtain all estimated outcomes in network meta-analysis (NMA). Effect estimates were presented as mean differences (Mean ± SD) with 95% confidence interval (CI). The comprehensive effects of all treatments were ranked by the surface under the cumulative ranking (SUCRA) probabilities.ResultsTwenty-five studies involved 1,106 participants were included. Prebiotics were superior in decreasing Interleukin-6 (IL-6; SMD –0.74, 95% CI [–1.32, –0.16]) and tumor-necrosis factor-α (TNF-α; SMD –0.59, 95% CI [–1.09, –0.08]), synbiotics were more effective in declining C-reactive protein (CRP; SMD –0.69, 95% CI [–1.14, –0.24]) and endotoxin (SMD –0.83, 95% CI [–1.38, –0.27]). Regarding uremic toxins, prebiotics ranked highest in reducing indoxyl sulfate (IS; SMD –0.43, 95% CI [–0.81, –0.05]), blood urea nitrogen (BUN; SMD –0.42, 95% CI [–0.78, –0.06]), and malondialdehyde (MDA; SMD –1.88, 95% CI [–3.02, –0.75]). Probiotics were rated as best in alleviating GI symptoms (SMD: –0.52, 95% CI [–0.93, –0.1]).ConclusionOur research indicated prebiotics were more effective in declining IL-6, TNF-α, IS, MDA, and BUN, synbiotics lowering CRP and endotoxin significantly, and probiotics were beneficial for alleviating GI symptoms, which may contribute to better clinical decisions. This study was registered in PROSPERO (Number: CRD42021277056).Systematic Review Registration[http://www.crd.york.ac.uk/PROSPERO], identifier [CRD42021277056]

Optical surface waves over metallo-dielectric nanostructures: Sommerfeld integrals revisited

The asymptotic closed-form solution to the fundamental diffraction problem of a linear horizontal Hertzian dipole radiating over the metallo-dielectric interface is provided. For observation points just above the interface, we confirm that the total surface near-field is the sum of two components: a long-range surface plasmon polariton and a short-range radiative cylindrical wave. The relative phases, amplitudes and damping functions of each component are quantitatively elucidated through simple analytic expressions for the entire range of propagation: near and asymptotic. Validation of the analytic solution is performed by comparing the predictions of a dipolar model with recently published dataComment: 13 pages, 7 figures. To appear in Optics Express Vol. 16, No. 1