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Hydrophilic polymer coatings on artificial implants generate excellent tribological properties. The friction properties of polymer coatings are affected by salt ion factors. Herein, the atomic force microscopy (AFM) was used to show that the superlubricity was achieved between poly(vinylphosphonic acid) (PVPA)-modified Ti6Al4V and polystyrene (PS) microsphere probe lubricated with monovalent salt solutions (LiCl, NaCl, KCl, and CsCl). Considering that adhesion is an important cause of friction changes, the AFM was further utilized to obtain adhesion between friction pairs in different salt solutions. The results indicated that the larger the cation radius in the lubricant, the smaller the adhesion, and the lower the friction coefficient of the PVPA coating. The electrostatic interaction between the PVPA and one-valence cations in lubricants was analyzed by the molecular dynamics (MD) simulation as it was found to be the main influencing factor of the adhesion. Combined analysis results of friction and adhesion indicated that by adjusting the size of cation radius in lubricant, the adhesion between the tribo-pairs can be changed, and eventually the magnitude of friction can be affected. This study opens up a new avenue for analyzing the friction characteristics of hydrophilic polymer coatings from the perspective of intermolecular forces.

Sum-frequency generation (SFG) vibrational spectroscopy is a second-order nonlinear optical spectroscopy technique. Owing to its interfacial selectivity, SFG vibrational spectroscopy can provide interfacial molecular information, such as molecular orientations and order, which can be obtained directly, or molecular density, which can be acquired indirectly. Interfacial molecular behaviors are considered the basic factors for determining the tribological properties of surfaces. Therefore, owing to its ability to detect the molecular behavior in buried interfaces in situ and in real time, SFG vibrational spectroscopy has become one of the most appealing technologies for characterizing mechanisms at friction interfaces. This paper briefly introduces the development of SFG vibrational spectroscopy and the essential theoretical background, focusing on its application in friction and lubrication interfaces, including film-based, complex oil-based, and water-based lubricating systems. Real-time detection using SFG promotes the nondestructive investigation of molecular structures of friction interfaces in situ with submonolayer interface sensitivity, enabling the investigation of friction mechanisms. This review provides guidance on using SFG to conduct friction analysis, thereby widening the applicability of SFG vibrational spectroscopy.

Event sequences with marker and timing information are available in a wide range of domains, from machine log in automatic train supervision systems to information cascades in social networks. Given the historical event sequences, predicting what event will happen next and when it will happen can benefit many useful applications, such as maintenance service schedule for mass rapid transit trains and product advertising in social networks. Temporal point process (TPP) is one effective solution to solve the next event prediction problem due to its capability of capturing the temporal dependence among events. The recent recurrent temporal point process (RTPP) methods exploited recurrent neural network (RNN) to get rid of the parametric form assumption in the density functions of TPP. However, most existing RTPP methods focus only on the temporal dependence among events. In this work, we design a novel multi-relation structure RNN model with a hierarchical attention mechanism to capture not only the conventional temporal dependencies but also the explicit multi-relation topology dependencies. We then propose an RTPP algorithm whose density function conditioned on the event sequence embedding learned from our RNN model for cognitively predict the next event marker and time. The experiments show that our proposed MRS-RMTPP outperforms the state-of-the-art baselines in terms of both event marker prediction and event time prediction on three real-world datasets. The capability of capturing both ontology relation structure and temporal structure in the event sequences is of great importance for the next event marker and time prediction.

Effective event modeling allows accurate event identification and monitoring to enable timely response to emergencies occurring in various applications. Although event identification (or detection) has been extensively studied in the last decade, the triggering relationship among initial and subsequent events has not been well studied, which limits the understanding of event evolvements from both spatial and temporal dimensions. Furthermore, it is also useful to measure the impact of events to the public so that the important events can be first seen. In this paper, we propose to systematically study event modeling and ranking in a novel framework. A new method is introduced to effectively identify events by considering the spreading effect of event in the spatio-temporal space. To capture the triggering relationships among events, we adapt the self-exciting point process model by jointly considering event spatial, temporal and content similarities. As a step further, we define the event impact and estimate it via random walk based on the triggering relationships. Finally, events can be ranked at different time stamps. Extensive experimental results on real-life datasets demonstrate promising performance of our proposal in identifying, monitoring and ranking events.