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This paper aims to explore the effects of mineral diesel fuel carbon soot (MCS) and biodiesel carbon soot (BCS) on the lubrication of polyalphaolefin (PAO) and diesel fuels.

Two styles of carbon soot were prepared from the natural combustion of mineral diesel fuel oil (MDO) and biodiesel oil (BDO). Tribological tests were conducted on a high-frequency reciprocating rig. Friction surfaces were characterized using three-dimensional laser scanning confocal microscopy and Raman spectroscopy.

The addition of MCS and BCS to PAO could reduce friction in most cases. MCS had a negligible effect on the wear for contents not exceeding 1.0 per cent. By contrast, BCS exhibited a considerable negative influence on the wear resistance even at low contents. For diesel fuels, MCS reduced both friction and wear, whereas BCS substantially deteriorated the lubrication of BDO. MCS formed a Fe₃O₄/C composite lubricating film on the friction surface. BCS also entered the contact region, but it did not form an effective Fe₃O₄/C composite lubricating film.

This work compared MDO and BDO from a different perspective, i.e. the effects of their combustion carbon soot on the lubrication of lubricating oil and fuel oil. The significant negative effect of BCS on the lubrication of lubricating oil and BDO is a problem that could occur in the industrial application of BDO.

Laser cladding deposition is limited in industrial application by the micro-defects and residual tensile stress for the thermal forming process, leading to lower fatigue strength compared with that of the forging. The purpose of this paper is to develop an approach to reduce stress and defects.

A hybrid process of laser cladding deposition and shot peening is presented to transform surface strengthening technology to the overall strengthening technology through layer-by-layer forming and achieve enhancement.

The results show that the surface stress of the sample formed by the hybrid process changed from tensile stress to compressive stress, and the surface compressive stress introduced could reach more than four times the surface tensile stress of the laser cladding sample. At the same time, internal micro-defects such as pores were reduced. The porosity of the sample formed by the hybrid process was reduced by 90.12% than that of the laser cladding sample, and the surface roughness was reduced by 43.16%.

The authors believe that the hybrid process proposed in this paper can significantly expand the potential application of laser cladding deposition by solving its limitations, promoting its efficiency and applicability in practical cases.

The purpose of this paper is to establish a situation evaluation model of the robot and ball in SimuroSot5vs5 platforms and enhance the strength of the team in a SimuroSot5vs5.

This paper presents a mathematical model based on situation evaluations which can improve the strength of the team in SimuroSot5vs5. The situation evaluation focus on four aspects includes scores of both sides, possession of teams on ground, ball strategy, and treat. The statistical analysis of the score can verify validity and stability of current strategy in confrontation. To evaluate the situation more effectively without blindness, possession on both teams is, respectively, evaluated. Ball strategy analyzes coordinate transformation to the ball on the ground and illustrates the circumstance of both teams on the offensive position accurately in length and breadth. To know the circumstance on the field more completely and synthetically, a threat situation evaluation model is built. An effective and practical algorithm for comprehensive situation evaluation is successfully finished. The experiments prove validity and performance of the proposed situation evaluation model.

A mathematical model is designed to achieve situation evaluation, and the strategy can change in accordance with different situations on the ground.

The system is specifically applied to SimuroSot5vs5 platform. The extensibility of the system is limited.

When the robot and ball is in high speed movement, a large calculated amount will slow the speed of the system.

The paper shows that situation evaluation in SimuroSot decision support systems will enhance the battle effectiveness of the soccer team.

The purpose of this paper is to evaluate the influence of a mixture of nutrient solution, bacteria and biofilm on the consolidation, unconfined compression and desiccation characteristics of two soils that could be used in waste containment applications.

Experimental work was conducted to investigate the influence of biofilm on the desiccation, strength and consolidation characteristics of two barrier soils. The soils were evaluated with water alone and with a biofilm solution composed of nutrients, bacteria and exopolymeric substances (EPS). These solutions were mixed with a locally available clay (“red bull tallow” (RBT)) as well as a mix of 65 percent sand and 35 percent bentonite (65‐35 Mix).

Reductions in strength and increases in ductility are observed with biofilm amendment for two soil types. The shear strength was reduced from 413 to 313 kPa and from 198 to 179 kPa for RBT and 65‐35 Mix, respectively. Desiccation tests reveal an increase in moisture retention for early time increments in amended specimens, while both increases and decreases are noted after extended drying. Increases in the rate of consolidation and modest decreases in the compression and swell index were observed. In particular, the consolidation coefficient was increased from 0.036 to 0.064 cm²/min and from 0.060 to 0.093 cm²/min for RBT and 65‐35 Mix, respectively.

These results are useful in establishing the broader impacts of using biofilm as an additive to increase the performance (e.g. reduce hydraulic conductivity and increase resistance to crack formation) of barrier materials in waste containment applications. Moreover, the data provide insight into the geotechnical implications of biofilm‐producing methanotrophic activity that occurs naturally in the covers of municipal solid waste landfills.

Very little research has been published on the influence of biofilm on the behavior of barrier materials in general, and on geotechnical properties in particular. This paper is unique in making the connection between methanotrophic activity, soil modification and barrier material performance.

Recently, the double joint photographic experts group (JPEG) compression detection tasks have been paid much more attention in the field of Web image forensics. Although there are several useful methods proposed for double JPEG compression detection when the quantization matrices are different in the primary and secondary compression processes, it is still a difficult problem when the quantization matrices are the same. Moreover, those methods for the different or the same quantization matrices are implemented in independent ways. The paper aims to build a new unified framework for detecting the doubly JPEG compression.

First, the Y channel of JPEG images is cut into 8 × 8 nonoverlapping blocks, and two groups of features that characterize the artifacts caused by doubly JPEG compression with the same and the different quantization matrices are extracted on those blocks. Then, the Riemannian manifold learning is applied for dimensionality reduction while preserving the local intrinsic structure of the features. Finally, a deep stack autoencoder network with seven layers is designed to detect the doubly JPEG compression.

Experimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches.

To verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions.

This paper aims to propose a unified framework to detect the double JPEG compression in the scenario whether the quantization matrix is different or not, which means this approach can be applied in more practical Web forensics tasks.

This paper aims to present a method to improve the sensitive and low probabilities of false alarm of a manipulator in a human–robot interaction environment, which can improve the performance of the system owing to non-linear uncertainty in the model of the robot controller.

A novel collision detection method based on adaptive residual estimation is proposed, promoting the detection accuracy of the collision of the manipulator during operation. First, a general momentum residual estimator is designed to incorporate the non-linear factors of the manipulator (e.g. joint friction, speed and acceleration) into the residual-related uncertainty of the model. Second, model parameters are estimated through gradient correction. The residual filter is used to determine the dynamic threshold, resulting in higher detection accuracy. Finally, the performance of the residual estimation scheme is evaluated by comparing the dynamic threshold with residual in real-time experiments where a single Universal Robot 5 robot end–effector collides with the obstacle.

Experimental results demonstrate that the collision detection system can improve sensitivity and lead to low probabilities of false alarm of non-linear uncertainty in the model.

The method proposed in this article can be applied to industry and human–robot interaction area.

An adaptive collision detection method is proposed in this paper to address non-linear uncertainties of the model in industrial application.

Here, we examine the challenges to democratization in Bahrain, with a particular focus on how the recent 2011 Uprising has resulted in a deepening of authoritarianism. It is argued that the recent unrest has brought into sharp relief the absence of “quality” democracy in Bahrain, and that any form of democratic transition is dependent on the will of a conservative Al Khalifa-Saudi nexus. While the pro-democracy movement may have prompted minor concessions on the part of the government, the extent of the popular mobilization triggered the Al Khalifa regime’s authoritarian reflex, and they have reacted to throttle the Uprising by putting in place legislative, ideological, and political barriers to reform, which points not only to a current de-democratization, but also a lack of future democratization. In addition to arguing for the post-2011 undoing of democracy in Bahrain, this paper also points to two major barriers to future democratization; (1) a conservative, post-Independence Al Khalifa-Saudi coalition assisted by large military resources (2) protracted communal tension brought about by the government’s instrumentalization of sectarianism.

With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets,

A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability.

A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case.

The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited.

A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.

The purpose of this paper is to describe the policy and trends in rural education in China over the past 40 years; and also discuss a number of challenges that are faced by China’s rural school system.

The authors use secondary data on policies and trends over the past 40 years for preschool, primary/junior high school, and high school.

The trends over the past 40 years in all areas of rural schooling have been continually upward and strong. While only a low share of rural children attended preschool in the 1980s, by 2014 more than 90 percent of rural children were attending. The biggest achievement in compulsory education is that the rise in the number of primary students that finish grade 6 and matriculate to junior high school. There also was a steep rise of those going to and completing high school. While the successes in upscaling rural education are absolutely unprecedented, there are still challenges.

This is descriptive analysis and there is not causal link established between policies and rural schooling outcomes.

The authors illustrate one of the most rapid rises of rural education in history and match the achievements up with the policy efforts of the government. The authors also explore policy priorities that will be needed in the coming years to raise the quality of schooling.

This is the first paper that documents both the policies and the empirical trends of the success that China has created in building rural education from preschool to high school during the first 40 years of reform (1978-2018). The paper also documents – drawing on the literature and the own research – the achievements and challenges that China still face in the coming years, including issues of gender, urbanization, early childhood education and health and nutrition of students.

The purpose of this paper is to study the correlation between the dynamic features of the running-in attractor and the wear particle group, so as to characterize the running-in attractor by means of the wear particle group.

Wear particles are collected in phased wear experiments, and their dynamic features are investigated by the equivalent mean chord length L. Then, the correlation between the equivalent mean chord length L and the correlation dimension D of the running-in attractor is studied.

In the wear process, the equivalent means chord length L first decreases, then remains steady, and finally increases, this process agrees with the increase, stabilization and decrease of the correlation dimension D. Therefore, the wear particle group has a dynamic nature, which characterizes the formation, stabilization, and disappearance of a running-in attractor. Consequently, the dynamic characteristics and evolution of a running-in attractor can be revealed by the wear particle group.

The intrinsic relationship between the wear particle group and the running-in attractor is proved, and this is advantageous for further revealing the dynamic features of the running-in attractor and identifying the wear states.