MGCN: Semi-supervised Classification in Multi-layer Graphs with Graph Convolutional Networks

Graph embedding is an important approach for graph analysis tasks such as node classification and link prediction. The goal of graph embedding is to find a low dimensional representation of graph nodes that preserves the graph information. Recent methods like Graph Convolutional Network (GCN) try to consider node attributes (if available) besides node relations and learn node embeddings for unsupervised and semi-supervised tasks on graphs. On the other hand, multi-layer graph analysis has been received attention recently. However, the existing methods for multi-layer graph embedding cannot incorporate all available information (like node attributes). Moreover, most of them consider either type of nodes or type of edges, and they do not treat within and between layer edges differently. In this paper, we propose a method called MGCN that utilizes the GCN for multi-layer graphs. MGCN embeds nodes of multi-layer graphs using both within and between layers relations and nodes attributes. We evaluate our method on the semi-supervised node classification task. Experimental results demonstrate the superiority of the proposed method to other multi-layer and single-layer competitors and also show the positive effect of using cross-layer edges

An Entropy-Based Failure Prediction Model for the Creep and Fatigue of Metallic Materials

It is well accepted that the second law of thermodynamics describes an irreversible process, which can be reflected by the entropy increase. Irreversible creep and fatigue damage can also be represented by a gradually increasing damage parameter. In the current study, an entropy-based failure prediction model for creep and fatigue is proposed based on the Boltzmann probabilistic entropy theory and continuum damage mechanics. A new method to determine the entropy increment rate for creep and fatigue processes is proposed. The relationship between entropy increase rate during creep process and normalized creep failure time is developed and compared with the experimental results. An empirical formula is proposed to describe the evolution law of entropy increase rate and normalized creep time. An entropy-based model is developed to predict the change of creep strain during the damage process. Experimental results of metals and alloys with different stresses and at different temperatures are adopted to verify the proposed model. It shows that the theoretical predictions agree well with experimental data

An Entropy Based Low-Cycle Fatigue Life Prediction Model for Solder Materials

Fatigue damage is an irreversible progression which can be represented by the entropy increase, and it is well known that the second law of thermodynamics can describe an irreversible process. Based on the concept of entropy, the second law of thermodynamics can provide the changing direction of system. In the current study, a new entropy increment model is developed based on the frame work of continuum damage mechanics. The proposed model is applied to determine the entropy increment during the fatigue damage process. Based on the relationship between entropy and fatigue life, a new fatigue life prediction model is proposed with clear physical meaning. To verify the proposed model, eight groups of experiments were performed with different aging and experimental conditions. The theoretical predictions show good agreement with the experimental data. It is noted that with higher aging temperatures, the value of ε th / ε cr becomes larger and the residual fatigue life reduces. The value of ε th / ε cr goes larger and the residual fatigue life becomes shorter with higher strain amplitude

Effect of Combustion Boundary Conditions and n-Butanol on Surrogate Diesel Fuel HCCI Combustion and Emission Based on Two-Stroke Diesel Engine

The combustion and emission characteristics of surrogate diesel fuel homogeneous charge compression ignition (HCCI) with different combustion boundary conditions and n-butanol (NB) mixing ratios are studied in this work. Engine data of a two-stroke low-speed direct-injection marine diesel engine were selected for the reactor. HCCI combustion was achieved by compressing a completely homogeneous mixture of fuel and air. The results show that NO emissions decrease slightly with the increase of initial boundary pressure at a constant equivalence ratio and initial temperature. In addition, the different initial boundary temperature has little effect on NO emission. The results also indicate that the ignition delay time of the mixed fuel rises with the increase of n-butanol mixing ratio. The emissions and reaction rate of NOx reduce significantly with the increase of n-butanol percentage in surrogate diesel fuel and n-butanol mixing combustion at a constant equivalence ratio and total mole fraction. Meanwhile, CO2 emissions also decrease significantly with the increase of n-butanol mixing ratio

Towards Explanation for Unsupervised Graph-Level Representation Learning

Due to the superior performance of Graph Neural Networks (GNNs) in various domains, there is an increasing interest in the GNN explanation problem "\emph{which fraction of the input graph is the most crucial to decide the model's decision?}" Existing explanation methods focus on the supervised settings, \eg, node classification and graph classification, while the explanation for unsupervised graph-level representation learning is still unexplored. The opaqueness of the graph representations may lead to unexpected risks when deployed for high-stake decision-making scenarios. In this paper, we advance the Information Bottleneck principle (IB) to tackle the proposed explanation problem for unsupervised graph representations, which leads to a novel principle, \textit{Unsupervised Subgraph Information Bottleneck} (USIB). We also theoretically analyze the connection between graph representations and explanatory subgraphs on the label space, which reveals that the expressiveness and robustness of representations benefit the fidelity of explanatory subgraphs. Experimental results on both synthetic and real-world datasets demonstrate the superiority of our developed explainer and the validity of our theoretical analysis

Optimal Control Strategy for Marine Ssp Podded Propulsion Motor Based on Strong Tracking-Epf

Aiming at the non-linearity of state equation and observation equation of SSP (Siemen Schottel Propulsor) propulsion motor, an improved particle filter algorithm based on strong tracking extent Kalman filter (ST-EKF) was presented, and it was imported into the marine SSP propulsion motor control system. The strong tracking filter was used to update particles in the new algorithm and produce importance densities. As a result, the problems of particle degeneracy and sample impoverishment were ameliorated, the propulsion motor states and the rotor resistance were estimated simultaneously using strong track filter (STF), and the tracking ability of marine SSP propulsion motor control system was improved. Simulation result shown that the improved EPF algorithm was not only improving the prediction accuracy of the motor states and the rotor resistance, but also it can satisfy the requirement of navigation in harbor. It had the better accuracy than EPF algorithm

Systematic Review of Studies of Workplace Exposure to Environmental Tobacco Smoke and Lung Cancer Risk

Background and objective It has been reported that there was a close relationship between lung cancer risk and environmental tobacco smoke at workplace. The aim of this study is to explore the relationship between workplace environmental tobacco smoke exposure and lung cancer risk among non-smoking subjects. Methods By searching Medline, CENTRAL (the Cochrane central register of controlledtrials), EMBASE, CBM, CNKI and VIP et al, we collected both domestic and overseas published documents on workplace environmental tobacco smoke exposure and lung cancer risk. Random or fixed effect models were applied to conduct systematic review on the study results, the combined odds ratio (OR) and the 95% confidence interval (CI) were calculated as well. Results 22 reports were included into the combined analysis, which indicated that 25% lung cancer risk was increased by exposing to workplace environment tobacco smoke (OR=1.25, 95%CI: 1.13-1.39, P < 0.001). For female the increased risk was 22% (OR=1.22, 95%CI: 1.05-1.42, P=0.011). For male the increased risk was 54%, but it does not reach the statistical significance (OR=1.54, 95%CI: 0.74-3.18, P=0.247). Conclusion Workplace environmental tobacco smoke exposure is an important risk factor of lung cancer risk among non-smoking subjects. Especially for non-smoking women who expose to workplace environment tobacco smoke have a close relationship with lung cancer

Engineered Skin Substitute Regenerates the Skin with Hair Follicle Formation

Currently, engineered skin substitutes (ESS) are unable to regenerate cutaneous appendages. Recent studies have shown that skin-derived precursors (SKPs), which are extensively available, have the potential to induce hair follicle neogenesis. Here, we demonstrate that ESS consisting of culture-expanded SKPs and epidermal stem cells (Epi-SCs) reconstitute the skin with hair follicle regeneration after grafting into nude mice. SKPs seeded in a C-GAG matrix proliferated and expressed higher levels of hair induction signature genes—such as Akp2, Sox2, CD133 and Bmp6—compared to dermal fibroblasts. Moreover, when ESS prepared by seeding a mixture of culture-expanded murine SKPs and human adult Epi-SCs into a C-GAG matrix was grafted into full-thickness skin wounds in nude mice, black hairs were generated within 3 weeks. Immunofluorescence analysis showed that the SKPs were localized to the dermal papillae of the newly-formed hair follicle. Our results indicate that SKPs can serve as the hair-inductive cells in ESS to furnish it with hair genesis potentia