Investigation of Electrotribological and Arc Erosion Behavior of Cu-15vol.%Cr in situ Composite

Electrotribological and arc erosion behavior of Cu-15vol.%Cr in situ composite was investigated in terms of coefficient of friction, interfacial resistance, bulk temperature, and wear rate as a function of normal pressure, sliding speed, and electrical current. Microstructural change due to electrical sliding was studied to understand wear mechanisms. Cu-15vol.%Cr in situ composite was selected in this research because it exhibits an excellent combination of mechanical strength and electrical/thermal conductivity. It was found that the average coefficient of friction decreased with increasing electrical current under dry electrical sliding. The average coefficient of friction was lower under lubricated electrical sliding than that under dry electrical sliding, but it increased with increasing electrical current. There are no significant effects of normal pressure and sliding speed on coefficient of friction under dry electrical sliding. Under lubricated electrical sliding, the coefficient of friction decreased with increasing normal pressure, but it did not change significantly with sliding speed. Both static and dynamic interfacial resistance decreased slightly with increasing normal pressure and the dynamic interfacial contact resistance decreased with increasing electrical current. The openness of circuit decreased with increasing normal pressure, increased with increasing sliding speed, and electrical current. The bulk temperature increased with increasing electrical current for both dry and lubricated electrical sliding. The non-electrical wear rate of the composite increased with increasing normal pressure and decreased with increasing sliding speed. The electrical wear rate decreased with increasing electrical current under dry electrical sliding, whereas the wear rate increased with increasing electrical current under lubricated electrical sliding. The effects of normal pressure and sliding speed on the wear rate of the composite under both dry and lubricated electrical sliding are dependent upon the level of electrical current. The sliding-induced subsurface deformation occurred not only in the sliding direction but also in the lateral directions perpendicular to the sliding direction. The complex deformation mode was revealed clearly by the morphological change of the ribbon-like filaments. The thickness of the subsurface deformation layer increased with increasing normal pressure and sliding speed under dry non-electrical sliding. The thickness of the subsurface deformation layer decreased with increasing electrical current under dry electrical sliding, whereas the thickness increased with increasing electrical current under lubricated electrical sliding. A hardened surface layer and less damage on the subsurface layer were accounted for reduction in wear rate as electrical current was applied

Investigation of Electrotribological and Arc Erosion Behavior of Cu-15vol.%Cr in situ Composite

Electrotribological and arc erosion behavior of Cu-15vol.%Cr in situ composite was investigated in terms of coefficient of friction, interfacial resistance, bulk temperature, and wear rate as a function of normal pressure, sliding speed, and electrical current. Microstructural change due to electrical sliding was studied to understand wear mechanisms. Cu-15vol.%Cr in situ composite was selected in this research because it exhibits an excellent combination of mechanical strength and electrical/thermal conductivity. It was found that the average coefficient of friction decreased with increasing electrical current under dry electrical sliding. The average coefficient of friction was lower under lubricated electrical sliding than that under dry electrical sliding, but it increased with increasing electrical current. There are no significant effects of normal pressure and sliding speed on coefficient of friction under dry electrical sliding. Under lubricated electrical sliding, the coefficient of friction decreased with increasing normal pressure, but it did not change significantly with sliding speed. Both static and dynamic interfacial resistance decreased slightly with increasing normal pressure and the dynamic interfacial contact resistance decreased with increasing electrical current. The openness of circuit decreased with increasing normal pressure, increased with increasing sliding speed, and electrical current. The bulk temperature increased with increasing electrical current for both dry and lubricated electrical sliding. The non-electrical wear rate of the composite increased with increasing normal pressure and decreased with increasing sliding speed. The electrical wear rate decreased with increasing electrical current under dry electrical sliding, whereas the wear rate increased with increasing electrical current under lubricated electrical sliding. The effects of normal pressure and sliding speed on the wear rate of the composite under both dry and lubricated electrical sliding are dependent upon the level of electrical current. The sliding-induced subsurface deformation occurred not only in the sliding direction but also in the lateral directions perpendicular to the sliding direction. The complex deformation mode was revealed clearly by the morphological change of the ribbon-like filaments. The thickness of the subsurface deformation layer increased with increasing normal pressure and sliding speed under dry non-electrical sliding. The thickness of the subsurface deformation layer decreased with increasing electrical current under dry electrical sliding, whereas the thickness increased with increasing electrical current under lubricated electrical sliding. A hardened surface layer and less damage on the subsurface layer were accounted for reduction in wear rate as electrical current was applied

DxFormer: A Decoupled Automatic Diagnostic System Based on Decoder-Encoder Transformer with Dense Symptom Representations

Diagnosis-oriented dialogue system queries the patient's health condition and makes predictions about possible diseases through continuous interaction with the patient. A few studies use reinforcement learning (RL) to learn the optimal policy from the joint action space of symptoms and diseases. However, existing RL (or Non-RL) methods cannot achieve sufficiently good prediction accuracy, still far from its upper limit. To address the problem, we propose a decoupled automatic diagnostic framework DxFormer, which divides the diagnosis process into two steps: symptom inquiry and disease diagnosis, where the transition from symptom inquiry to disease diagnosis is explicitly determined by the stopping criteria. In DxFormer, we treat each symptom as a token, and formalize the symptom inquiry and disease diagnosis to a language generation model and a sequence classification model respectively. We use the inverted version of Transformer, i.e., the decoder-encoder structure, to learn the representation of symptoms by jointly optimizing the reinforce reward and cross entropy loss. Extensive experiments on three public real-world datasets prove that our proposed model can effectively learn doctors' clinical experience and achieve the state-of-the-art results in terms of symptom recall and diagnostic accuracy.Comment: 7 pages, 4 figures, 3 table

Using tweets to help sentence compression for news highlights generation

We explore using relevant tweets of a given news article to help sentence com-pression for generating compressive news highlights. We extend an unsupervised dependency-tree based sentence compres-sion approach by incorporating tweet in-formation to weight the tree edge in terms of informativeness and syntactic impor-tance. The experimental results on a pub-lic corpus that contains both news arti-cles and relevant tweets show that our pro-posed tweets guided sentence compres-sion method can improve the summariza-tion performance significantly compared to the baseline generic sentence compres-sion method.

Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks

The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Although new data are beneficial for continuous training, without complete feedback information, i.e., conversion labels, training algorithms may suffer from overwhelming fake negatives. Existing methods tend to use multitask learning or design data pipelines to solve the delayed feedback problem. However, these methods have a trade-off between data freshness and label accuracy. In this paper, we propose Delayed Feedback Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages, i.e., preparing a data pipeline, building a dynamic graph, and training a CVR prediction model. In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships. The proposed method achieves both data freshness and label accuracy. We conduct extensive experiments on three industry datasets, which validate the consistent superiority of our method

Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework

Leaving in the Present and Future with IOT (Internet of Things)

Technology in today’s world offers us the extraordinary of everyday life. An electronic device user can turn off the light from one place or another while sitting another part of the globe using a simple device like the smart phone, a personal Computer or a tablet PC.Cars that are driverless are just around the corner, leaving in the world today means we are leaving in not just the present but the future, something few years ago will have been impossible.The new revolution is here with us and we are living with it, and that is the magic of IOT (internet of things) the present and the future is made possible with IOT

Preparation of graphene oxide–stabilized Pickering emulsion adjuvant for Pgp3 recombinant vaccine and enhanced immunoprotection against Chlamydia Trachomatis infection

BackgroundTraditional emulsion adjuvants are limited in clinical application because of their surfactant dependence. Graphene oxide (GO) has unique amphiphilic properties and therefore has potential to be used as a surfactant substitute to stabilize Pickering emulsions.MethodsIn this study, GO–stabilized Pickering emulsion (GPE) was prepared and used as an adjuvant to facilitate an enhanced immune response to the Chlamydia trachomatis (Ct) Pgp3 recombinant vaccine. Firstly, GPE was prepared by optimizing the sonication conditions, pH, salinity, GO concentration, and water/oil ratio. GPE with small-size droplets was characterized and chosen as the candidate. Subsequently, controlled-release antigen delivery by GPE was explored. Cellular uptake behaviors, M1 polarization, and cytokine stimulation by GPE + Pgp3 was considered in terms of the production of macrophages. Finally, GPE’s adjuvant effect was evaluated by vaccination with Pgp3 recombinant in BALB/c mouse models.ResultsGPE with the smallest droplet sizes was prepared by sonication under 163 W for 2 min at 1 mg/mL GO in natural salinity with a pH of 2 when the water/oil ratio was 10:1 (w/w). The optimized average GPE droplet size was 1.8 μm and the zeta potential was –25.0 ± 1.3 mv. GPE delivered antigens by adsorption onto the droplet surface, demonstrating the controlled release of antigens both in vitro and in vivo. In addition, GPE promoted antigen uptake, which stimulated proinflammatory tumor necrosis factor alpha (TNF-α), enhancing the M1 polarization of macrophages in vitro. Macrophage recruitment was also significantly promoted by GPE at the injection site. In the GPE + Pgp3 treatment group, higher levels of immunoglobin (IgG), immunoglobin G1 (IgG1), immunoglobin G2a (IgG2a) sera, and immunoglobin A (IgA) were detected in vaginal fluid, and higher levels of IFN-γ and IL-2 secretion were stimulated, than in the Pgp3 group, showing a significant type 1 T helper (Th1)-type cellular immune response. Chlamydia muridarum challenging showed that GPE enhanced Pgp3’s immunoprotection through its advanced clearance of bacterial burden and alleviation of chronic pathological damage in the genital tract.ConclusionThis study enabled the rational design of small-size GPE, shedding light on antigen adsorption and control release, macrophage uptake, polarization and recruitment, which enhanced augmented humoral and cellular immunity and ameliorated chlamydial-induced tissue damage in the genital tract