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Purpose This paper aims to study different morphology Cu6Sn5 effect on Babbitt alloy tribological properties. Design/methodology/approach Different morphology Cu6Sn5 of Babbitt was conducted by different cooling modes. Bare Babbitt was marked by Babbitt-0, Babbitt modified by first cooling mode (marked by Babbitt-1) and Babbitt modified by second cooling mode (marked by Babbitt-2). The microstructure and microhardness of specimens were tested. Then, tribological properties of Babbitt-0, Babbitt-1 and Babbitt-2 were performed by reciprocating mode under lubricated condition. Findings The results showed that shape Cu6Sn5 of Babbitt was changed from mixed needle and star-like shape to short rod-like or granular shape. The microhardness of Babbitt-1 was highest than that of Babbitt-0 and Babbitt-2. Compared with Babbitt-0 and Babbitt-2, tribological properties of Babbitt-1 were better under lubricated condition due to short rod-like and sparse distribution of Cu6Sn5. Moreover, the simulation result of strain and stress of Babbitt-1 was lowest than that of Babbitt-0 and Babbitt-2. Originality/value Different morphology (shape and distributed) of Cu6Sn5 was obtained by different cooling modes. Modulated different forms of Cu6Sn5 around SnSb was beneficial to improve Babbitt alloy tribological properties.

Predicting grain losses and waste rate (LWR) is critical for agricultural planning and grain policy development. Capturing the stage interaction and generating robust features are the main challenges in grain LWR prediction. In this article, we propose MTGA, a Multitask Gated recurrent unit (GRU) Autoencoder, approach to 1) obtain the robust feature representation for the prediction task and 2) explore the time-ordered interactions among different stages of the grain chain. Specifically, we design multiple GRU encoder–decoder pairs to co-reconstruct the stage features in a common space for robust feature learning. Then, an attention mechanism is proposed better to fuse the reconstructed features from the GRU encoder–decoder pairs. Furthermore, we utilize the multitask for reconstructed loss and grain LWR prediction. We introduce the reconstructed loss task as an auxiliary task to help us to represent the robust features. Besides, we introduce the LWR prediction as main task to learn the parameters for prediction task. We collected the data with questionnaires, interviews, or data from grain management institutes for experiments. The evaluation results show that grain LWR prediction by our approach achieves the best results compared to several state-of-the-art prediction models. Moreover, our method gains overall performance decline of 12.5–18.3% on mean absolute error and root mean square error metrics.

Tourism is one of the largest service industries and a popular leisure activity participated by people with friends or family. A significant problem faced by the tourists is how to plan sequences of points of interest (POIs) that maintain a balance between the group preferences and the given temporal and spatial constraints. Most traditional group itinerary recommendation methods adopt predefined preference aggregate strategies without considering the group members’ distinctive characteristics and inner relations. Besides, POI textual information is beneficial to capture overall group preferences but is rarely considered. With these concerns in mind, this paper proposes an AMT-IRE (short for Attentive Multi-Task learning-based group Itinerary REcommendation) framework, which can dynamically learn the inner relations between group members and obtain consensus group preferences via the attention mechanism. Meanwhile, AMT-IRE integrates POI categories and POI textual information via another attention network. Finally, the group preferences are used in a variant of the orienteering problem to recommend group itineraries. Extensive experiments on six datasets validate the effectiveness of AMT-IRE.

Background Evidence has been shown that abnormal DNA methylation plays a vital role in the progression of breast cancer via silencing of gene expression. The results of bisulfite sequencing showed that the methylation status of HOPX in breast cancer tissues was higher than that in normal breast cancer tissues, but little known about the biological functions of HOPX in breast cancer. Methods A total of 13 paired breast cancer and adjacent noncancerous tissues were subjected to bisulfite sequencing. Meanwhile, the methylation levels of cg218995965 and cg24862548 in breast cancer cells were detected by methylation-specific PCR (MSP). Flow cytometry, wound healing and transwell invasion assays were used to detect the apoptosis, migration and invasion in breast cancer cells. In addition, the expressions of HOPX, p21, cyclin D1 and CDK4 in cells were detected with Western blot assay. Results Bisulfite sequencing indicated that the CpG sites (cg218995965 and cg24862548) in the HOPX promoter region showed significantly higher methylation in breast cancer tissues. In addition, methylation-specific PCR revealed that HOPX was significantly hypermethylated in breast cancer cell lines MDA-MB-468 and MCF-7. Furthermore, overexpression of HOPX significantly inhibited the proliferation of MDA-MB-468 and MCF-7 cells via inducing the apoptosis. Moreover, upregulation of HOPX markedly inhibited the migration and invasion abilities of MDA-MB-468 cells. Meanwhile, overexpression of HOPX obviously induced cell cycle arrest in MDA-MB-468 cells via upregulation of p21, and downregulation of cyclin D1 and CDK4. Additionally, overexpression of HOPX suppressed tumor growth of breast cancer in vivo. Conclusion Our data showed that HOPX, a tumor suppressor, is epigenetically silenced in breast cancer. Overexpression of HOPX could suppress the progression of breast cancer, and thus indicating that it might serve as a potential target for the treatment of patients with breast cancer.

As an e-commerce feature, the personalized recommendation is invariably highly-valued by both consumers and merchants. The e-tourism has become one of the hottest industries with the adoption of recommendation systems. Several lines of evidence have confirmed the travel-product recommendation is quite different from traditional recommendations. Travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books and food), which gives rise to the extreme sparsity of travel data. Meanwhile, the choice of a suitable travel product is affected by an army of factors such as departure, destination, and financial and time budgets. To address these challenging problems, in this article, we propose a Probabilistic Matrix Factorization with Multi-Auxiliary Information (PMF-MAI) model in the context of the travel-product recommendation. In particular, PMF-MAI is able to fuse the probabilistic matrix factorization on the user-item interaction matrix with the linear regression on a suite of features constructed by the multiple auxiliary information. In order to fit the sparse data, PMF-MAI is built by a whole-data based learning approach that utilizes unobserved data to increase the coupling between probabilistic matrix factorization and linear regression. Extensive experiments are conducted on a real-world dataset provided by a large tourism e-commerce company. PMF-MAI shows an overwhelming superiority over all competitive baselines on the recommendation performance. Also, the importance of features is examined to reveal the crucial auxiliary information having a great impact on the adoption of travel products.

Purpose Currently, lubrication analysis of piston ring is generally done under engine rated operating condition. However, the engine (such as the vehicle engine) does not always operate in rated operating condition, and its operating condition changes frequently in actual use. In addition, the lubrication status of piston ring is generally assumed as the flooded lubrication or a certain form of poor lubrication in most of the lubrication analysis. Design/methodology/approach In this paper, based on the equations about the flow rate of lubricating oil and the variation of control volume, the flow model of lubricating oil in the piston ring-cylinder liner conjunction is established. The lubrication analysis of piston ring for a four-stroke engine under different engine operating conditions is done, in which the lubricating oil at the inlet of piston ring is considered as the lubricating oil attached on the relevant location of cylinder wall after the piston ring moves over at the previous stroke. Findings There is remarkable difference for the lubrication characteristics of the piston ring under different engine operating conditions. The worst lubrication status of piston ring may not take place under engine rated operating condition. Originality/value In this paper, based on the measured engine cylinder pressure, the lubrication analysis of piston ring for a four-stroke engine under different engine operating conditions is done in which the lubricating oil supply condition at the inlet of piston ring is considered. The results of this paper are helpful for the design and research of engine piston ring-cylinder liner conjunction.

Currently used turbulent lubrication model that considers the effects of surface roughness can only handle the isotropic rough surfaces and cannot handle the rough surfaces with directional properties. In this paper, based on the stochastic method and the Ng-Pan turbulent model, the stochastic turbulent lubrication models of one-dimensional longitudinal rough surface, one-dimensional transverse rough surface and isotropic rough surface are derived. The turbulent lubrication performance of a journal bearing with isotropic rough surface is calculated by the stochastic turbulent lubrication model. The results are in good agreement with the corresponding experimental data. Moreover, the proposed stochastic turbulent lubrication models are also suitable for parts of friction pairs such as journal bearing and thrust bearing.

In the journal-bearing system, the journal inevitably moves along the bearing axis, and the axial movement of journal may affect the performance of misaligned journal bearing. In this article, a numerical model for the thermohydrodynamic lubrication of bearing considering the axial movement of journal is established, and based on which a comprehensive analysis on the performance of misaligned journal bearing is conducted by solving the generalized Reynolds equation, heat conduction equation and energy equation. The results show that axial movement of misaligned journal has more obvious influence on the bearing lubrication characteristics at the lower speed, larger inclination angle and eccentricity. The impact of axial movement on the bearing lubrication performance is greatly affected by the thermal effect and surface roughness.

In actual operation of an engine, the crankshaft will move along the axis direction of engine main bearings under the combined influence of various influencing factors. Considering the axial movement and crankshaft deformation, a new lubrication model of engine main bearings with rough surface was built based on the average Reynolds equation. The influence of axial movement of deformed crankshaft on performance of main bearing with rough surface was studied. In this study, the axial movement law of deformed crankshaft is measured through experiment, and the finite element method was applied to solve the tilting of crankshaft in the bearing bore resulted from the crankshaft deformation. The results show that the axial movement of deformed crankshaft has a significant impact on performance of main bearing with rough surface. No matter whether or not the surface roughness is considered, the journal center trajectories of main bearings are unclosed three-dimensional space curve due to the axial movement of deformed crankshaft. The influence level of the axial movement of deformed crankshaft on lubrication characteristics of main bearings is directly related to the surface roughness. The influence trend and level of the surface roughness on lubrication characteristics of main bearings are obviously changed when considering the axial movement of crankshaft.

As an e-commerce feature, the recommender system can enhance the consumer shopping experience and create huge benefits for businesses. The e-tourism has become one of the largest service industries with the application and popularity of recommender systems. Many studies have confirmed that the travel product recommendation is widely different from traditional recommendations. Due to the financial and time costs, travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books, movies and food). In addition, choosing the appropriate travel product will be influenced by many factors, such as departure, destination and price. To tackle this challenging problem, we propose a MV-GAN (short for Multi-View Graph Attention Network for travel recommendation) model. It enriches user and product semantics through both metapath-guided neighbors aggregation and multi-view fusion in heterogeneous travel product recommendation graph. In particular, we design node-level and path-level attention networks for learning user and product representations from every single view. To collaboratively integrate multiple types of relationships in different views, a view-level attention mechanism is proposed to aggregate the node representations and obtain global user and product representations. We evaluate the proposed method on a public dataset and a dataset constructed from a large tourism e-commerce website in China. Extensive experiments not only validate the effectiveness of MV-GAN, but also show its potentially good interpretability.

Aspect-level sentiment classification is an important tool for e-business platforms to help the user and suppliers to summary the fine-grained aspect-based sentiment opinions. The same review document may have different sentiment polarity for each aspect. Existing aspect-level sentiment classification usually learn the aspect-based sentiment polarity with traditional supervised learning and deep learning models with inter-attention, which may be insufficient. In this paper, we propose an intra- and inter-attention network model to predict the aspect-level of sentiment category. The core of our model is the representation module, intra-attention module and inter-attention module. In the representation module, we use the character-level semantic and word-level semantic information, respectively. In the intra-attention module, we use the intra-attention to capture the sentiment features relevant among each word. In addition, we use the aspect-sentiment pair attention to represent the sentiment features of the document under the specific aspect information and sentiment features in the inter-attention module. Experiments on four real-world datasets show that our proposed model outperforms the state-of-the-art baselines in the prediction accuracy.

Recently, Location-based Social Networks (LBSNs) have been widely used in people's daily life. This generates the demand for effective and efficient POI recommendation. Existing research focused on improving the POI recommendation performance by some extra information such as social links, geographical distance and others, but they failed to integrate those complex influenced factors together. In this paper, we propose a POI recommendation framework with interactive behaviors and user preference dynamic embedding to consider the social and geographical information with interactive behavior embedding, along with users' preference dynamics embedding, respectively. To combine the interactive behavior and users' preference dynamics behavior, we use a fusion parameter to decide the contribution of interactive behaviors embedding and user preferences embedding. Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposed model.

Background Evidence has been shown that abnormal DNA methylation plays a vital role in the progression of breast cancer via silencing of gene expression. The results of bisulfite sequencing showed that the methylation status of HOPX in breast cancer tissues was higher than that in normal breast cancer tissues, but little known about the biological functions of HOPX in breast cancer. Methods A total of 13 paired breast cancer and adjacent noncancerous tissues were subjected to bisulfite sequencing. Meanwhile, the methylation levels of cg218995965 and cg24862548 in breast cancer cells were detected by methylation-specific PCR (MSP). Flow cytometry, wound healing and transwell invasion assays were used to detect the apoptosis, migration and invasion in breast cancer cells. In addition, the expressions of HOPX, p21, cyclin D1 and CDK4 in cells were detected with Western blot assay. Results Bisulfite sequencing indicated that the CpG sites (cg218995965 and cg24862548) in the HOPX promoter region showed significantly higher methylation in breast cancer tissues. In addition, methylation-specific PCR revealed that HOPX was significantly hypermethylated in breast cancer cell lines MDA-MB-468 and MCF-7. Furthermore, overexpression of HOPX significantly inhibited the proliferation of MDA-MB-468 and MCF-7 cells via inducing the apoptosis. Moreover, upregulation of HOPX markedly inhibited the migration and invasion abilities of MDA-MB-468 cells. Meanwhile, overexpression of HOPX obviously induced cell cycle arrest in MDA-MB-468 cells via upregulation of p21, and downregulation of cyclin D1 and CDK4. Additionally, overexpression of HOPX suppressed tumor growth of breast cancer in vivo. Conclusion Our data showed that HOPX, a tumor suppressor, is epigenetically silenced in breast cancer. Overexpression of HOPX could suppress the progression of breast cancer, and thus indicating that it might serve as a potential target for the treatment of patients with breast cancer.

Based on the coordinate transformation method, the generalized turbulent lubrication equation considering the axial motion of the journal is derived. The finite-difference method is used to solve the generalized turbulent lubrication equation. The variations of turbulent lubrication performance with axial displacement for different axial movement velocity of the journal, journal misalignment angle, and initial mid-plane eccentricity ratio are obtained. The results show that when the axial movement velocity of the journal increases, the mid-plane eccentricity ratio of the bearing and the minimum film thickness remain unchanged, the average Reynolds number, maximum film pressure, load capacity, frictional power, and side leakage increases. As the axial displacement of the journal increases, the greater the misalignment angle of the journal, the greater the degree of misalignment, maximum film pressure, load capacity, and misalignment moment of the bearing. The greater the initial mid-plane eccentricity ratio, the greater the degree of journal misalignment, maximum film pressure, load capacity, frictional power, and misalignment moment.

In current lubrication analysis of piston skirt, the flooded status is generally considered in the piston skirt–cylinder liner frictional pair in all strokes of an engine operating cycle. However, the quantity of lubricating oil at the entrance of piston skirt cannot always ensure the sufficient lubrication status of piston skirt–cylinder liner frictional pair when the piston moves from the bottom dead center to the top dead center in actual engine. In this article, based on the model of piston secondary motion, fluid lubrication, and lubricating oil flow, the lubrication performance of piston skirt–cylinder liner frictional pair is analyzed, in which the quantity of lubricating oil detained on the surface of cylinder liner after the piston skirt moves from the top dead center to the bottom dead center and is considered as the quantity of lubricating oil at entrance of piston skirt when the piston moves from the bottom dead center to the top dead center. The results show that compared with current analysis, in which the sufficient lubrication of piston skirt–cylinder liner frictional pair is assumed in all strokes of engine, there are remarkable changes for the lubrication performance of piston skirt–cylinder liner frictional pair and the piston secondary motion when the lubrication status of the frictional pair in the upstroke of piston is determined by considering actual lubricating oil transport in the lubrication analysis of piston skirt.

Unbalanced inertia force and moment of crankshaft generated by cylinder pressure have a significant effect on the vibration characteristics of diesel engine, but the study on the lubrication characteristics of crankshaft by balance rate needs to be studied. Based on Greenwood-Tripp asperity contact theory and average Reynolds equation, an elastohydrodynamic (EHD) lubrication model of the main bearing for a V-12 diesel engine is developed, and the influence of different balance rates on lubrication characteristics is analyzed. On this basis, a factorial experiment of ten influencing factors including the balance rate is designed, and the factors that significantly affected the lubrication characteristics of the main bearing are screened out by factor effect diagram, statistical analysis and residual probability diagram. The results show that the crankshaft with different balance rate has a great influence on the lubrication performance. With the increase of balance rate, the minimum oil film thickness first increases and then decreases, and the maximum oil film pressure first decreases and then increases. The balance rate, bearing width, bearing clearance, bush repair and oil viscosity have significant effects on the performance of main bearing, among which the balance rate of crankshaft has the greatest effect on the minimum oil film thickness, the maximum rough contact pressure and the average friction power loss.