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The purpose of this paper is to study the effect of silver (Ag) precursor on friction and wear.

Thermogravimetric analysis of the oil with Ag precursor and X-ray diffraction of the decomposition product of the Ag complex were performed. The tribological behavior was evaluated on a UMT-3M Tribometer (CETR) using pure military-grade oil lubricant and the prepared lubricant at room temperature and at 300°C. Energy-dispersive spectroscopy of the chemical composition on the wear scar was performed.

When there is 1 weight per cent Ag precursor in the lubricant, the coefficient friction reduces by about 8 per cent at room temperature and by about 14 per cent at 300°C, and the wear probability also decreases, from moderate wear to mild wear at 300°C. There were more productions which could form metal Ag boundary films possessing low shearing stress and excellent lubricity at 300°C.

Ag precursors used as additives in a military-grade oil lubricant and excellent lubricity found at high temperature.

Since the multi-component of powder metallurgy was dispersed, and each component sheared flow and tiered under the action of friction force, it was difficult to disclose the evolution characteristics of each component. Meanwhile, third body mixing with particles of each component covered on the friction surface, which further increased the difficulty of understanding evolution of each component and the corresponding third body in the friction process. To solve this problem, this paper aims to propose a mechanical assembled method which compact several component sheets in order.

Pure copper, aluminum and artificial graphite sheets with thickness 0.5, 1 and 2 mm, respectively, were assembled into a jig by mechanical compact method. The relationship between arrangement patterns of the components and its friction coefficient was studied by using fixed speed friction test machine, the speed range from 200 to 2,000 r/min and the pressure range from 0.25 to 0.64 MPa.

The testing results showed that when the distribution of same components was congregated, friction coefficient dropped from 0.6 to 0.4. While the distribution of different components was dispersed, friction coefficient dropped from 0.6 to 0.25. The friction coefficient decline was caused by performances changes of third body fluidity. The sufficiently mixed third body made third body adhesion weaker and increased third body fluidity. That provoked friction coefficient decreasing obviously at high speed. On the contrary, with the high congregation of same components, strong third body adhesion led to a rougher surface which contributed to a higher friction coefficient.

By means of the mechanical-assembled multi-layer components to reveal the influence mechanism of every component on friction properties, will provide a new test approach for tribology.

China has long been dedicated to introducing the Western management ideas to the local enterprise practice. But the situation has changed since the financial crisis, and the China model has become a hot word with its fast recovery from crisis. Moreover, Chinese traditional culture has become increasingly popular. Yin and yang are the most familiar Chinese philosophical terms to Western minds, and also the core concept of Chinese Taoist philosophy. The purpose of this paper is to analyze how the yin yang or lao‐tzu influences a firm's core competence and performance, and to demonstrate that the Taoist‐oriented Chinese culture remains meaningful and in many situations powerful in enterprise practice.

A case study is carried out from Town Star Ltd, established in 2001, and located in Linfen city, Shanxi province, China, a city which topped the list of the world's most polluted cities. It is an integrated joint‐stock company, specializing in coal coking chemical industry, gardening, logistics and technology development. Based on the humanized management doctrine, the company has formed the 5S management model which boosts its rapid and sustainable development. It coped well in major junctures such as industry restructuring and transferring, financial crisis, etc. Five key dimensions of the yin and yang, which were applied to the leader's daily behaviors and management style, are: Wu‐wei (inaction), Wu (emptiness), hardness with softness, moderation and He‐xie (harmony). First, this paper built a dynamic company growth model based on this philosophy which enhanced the core competitiveness effectively by emotional management innovation and personalized HRM, among which its pivotal regulator of variables is just yin yang conversion degree. Then an approach is provided to validate the model with empirical data collected from the years 2001‐2010 (2001‐2005, 2006‐2008, 2009‐2010) of Town Star Ltd.

It is demonstrated that yin yang ideology embedded in the company growth model organically is an important means to increase marginal benefit. In practice, the firm with yin yang philosophical ideology would shape a management paradigm which combined rules and human nature appropriately to a family‐oriented business atmosphere. Based on the above philosophical wisdom, Town Star Ltd has harvested good economic and social benefits, strong core competence through the elaborately constructed unique management model and harmonious humanity environment.

This paper opens an avenue for indigenous firms, as a framework for guiding management research and practice in coping with the post‐crisis era. In addition, it is expected to attract more attention from scholars, entrepreneurs both in China and in the rest of the world, to contribute to knowledge creation in Chinese management studies.

Not all apps can be favored on a smartphone. Users are bound to abandon some if they lose value. Analyzing the reasons behind this phenomenon can help develop strategies for avoiding app abandonment, which is the focus of this study.

This study conducted in-depth open interviews with 47 respondents who had abandoned or deleted mobile apps before and used the qualitative research method grounded theory to analyze the interview content, followed by open coding, axial coding and selective coding.

Seven main reasons are summarized after the relationship between every statement and concept is found and constructed, namely functional value, information expectation, emotional value, platform service, user trust, cost, and social influence.

Based on these findings, this study provided suggestions for mobile app developers to help them effectively improve the user experience and retain their customers.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.