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A generalized distribution with wide range of skewness and elongation will be suitable for the data mining and compatible for the misspecification of the distribution. Hence, the purpose of this paper is to present a distribution-based approach for estimating degradation reliability considering these conditions.

Tukey’s g-and-h distribution with the quantile expression is introduced to fit the degradation paths of the population over time. The Newton–Raphson algorithm is used to approximately evaluate the reliability. Simulation verification for parameter estimation with particle swarm optimization (PSO) is carried out. The effectiveness and validity of the proposed approach for degradation reliability is verified by the two-stage verification and the comparison with others’ work.

Simulation studies have proved the effectiveness of PSO in the parameter estimation. Two degradation datasets of GaAs laser devices and crack growth are performed by the proposed approach. The results show that it can well match the initial failure time and be more compatible than the normal distribution and the Weibull distribution.

Tukey’s g-and-h distribution is first proposed to investigate the influence of the tail and the skewness on the degradation reliability. In addition, the parameters of the Tukey’s g-and-h distribution is estimated by PSO with root-mean-square error as the object function.

This paper aims to improve the diversity and richness of haptic perception by recognizing multi-modal haptic images.

First, the multi-modal haptic data collected by BioTac sensors from different objects are pre-processed, and then combined into haptic images. Second, a multi-class and multi-label deep learning model is designed, which can simultaneously learn four haptic features (hardness, thermal conductivity, roughness and texture) from the haptic images, and recognize objects based on these features. The haptic images with different dimensions and modalities are provided for testing the recognition performance of this model.

The results imply that multi-modal data fusion has a better performance than single-modal data on tactile understanding, and the haptic images with larger dimension are conducive to more accurate haptic measurement.

The proposed method has important potential application in unknown environment perception, dexterous grasping manipulation and other intelligent robotics domains.

This paper proposes a new deep learning model for extracting multiple haptic features and recognizing objects from multi-modal haptic images.

This work aims to combine the cloud robotics technologies with deep reinforcement learning to build a distributed training architecture and accelerate the learning procedure of autonomous systems. Especially, a distributed training architecture for navigating unmanned aerial vehicles (UAVs) in complicated dynamic environments is proposed.

This study proposes a distributed training architecture named experience-sharing learner-worker (ESLW) for deep reinforcement learning to navigate UAVs in dynamic environments, which is inspired by cloud-based techniques. With the ESLW architecture, multiple worker nodes operating in different environments can generate training data in parallel, and then the learner node trains a policy through the training data collected by the worker nodes. Besides, this study proposes an extended experience replay (EER) strategy to ensure the method can be applied to experience sequences to improve training efficiency. To learn more about dynamic environments, convolutional long short-term memory (ConvLSTM) modules are adopted to extract spatiotemporal information from training sequences.

Experimental results demonstrate that the ESLW architecture and the EER strategy accelerate the convergence speed and the ConvLSTM modules specialize in extract sequential information when navigating UAVs in dynamic environments.

Inspired by the cloud robotics technologies, this study proposes a distributed ESLW architecture for navigating UAVs in dynamic environments. Besides, the EER strategy is proposed to speed up training processes of experience sequences, and the ConvLSTM modules are added to networks to make full use of the sequential experiences.

The purpose of this study is to ensure the successful implementation of a landing cushion for the new generation armored vehicles with significantly enhanced quality. Furthermore, to introduce a high-precision landing cushioning analysis model.

To accurately analyze the cushioning performance of the new generation armored vehicles, a nonlinear finite element dynamics model considering the complex travel system was established. The model considered the influence of various nonlinear factors to measure the dynamic response difference between the proposed and traditional models. The cushioning performance of airbags under different landing conditions and their various influence factors were analyzed.

The travel system has a large influence on the key points of the vehicle, whose rear end of the upper deck has a larger acceleration fluctuation compared with the traditional model. The increase in the body material stiffness is helpful to reduce this fluctuation. The established nonlinear finite element model can effectively analyze the landing cushioning performance of airborne armored vehicles. The area of the external airbag vent has a large influence on the cushioning performance, and the cushioning system has excellent cushioning performance under various operating conditions.

This study introduces the travel system, which is ignored by traditional analytical models. The interactions between various types of complex structures are included in the analysis process in its entirety, leading to valuable new conclusions. Quantitatively reveals the analytical errors of traditional simulation models in multiple dimensions and the reasons for their formation. Based on a high-precision simulation model, it is verified that the designed airbag cushioning system has an excellent cushioning effect for the new generation of heavy airborne armored vehicles.

The novelty of this work comes from the need for smooth landing with low overload for a new type of large-load airborne armored vehicle and provides a high-precision model that quantifies the traditional analytical modeling errors and error principle.

The purpose of this paper is to study the effect of alignment of conductive particles on the piezoresistivity of composite based on a theoretical model. The piezoresistivity of composite is associated with the characteristics of conductive network formed by the conductive particles distributed in the composite, which can be changed through aligning the conductive particles.

The orientations of the tunnel resistors formed by each two adjacent conductive particles are dependent on the aligned level of the conductive particles, and different orientations induce different deformations for a tunnel resistor under external strain, which determines the piezoresistivity of the composites. To investigate the resistance behavior of composites with various characteristics of conductive networks, a piezoresistivity model is developed in this paper by considering the aligned level of conductive particles.

The results obtained from the proposed piezoresistivity model indicate that the sensitivity and stability of composites can be enhanced through aligning the conductive particles. Also, the piezoresistivity of composites filled with randomly distributed conductive particles is isotropic, and it turns to be anisotropic when the conductive particles are aligned.

The change and its mechanism of the piezoresistivity upon the aligned level of conductive particles have been pointed out in this paper based on the proposed model. The achievement of this paper will help the people understand, predict and optimize the piezoresistivity of composites, and provide a new approach to design a strain sensor based on the piezoresistivity.

Grassroots innovations (GRIs) can contribute greatly to inclusive development and reach out populations and areas not reached by or ignored by the formal sector. The purpose of the paper is to study how China's science and technology (S&T) policies and programs are translated into GRIs.

A case study of the grassroots organizations of farmer Chen Guangxing, of Baodi County, Tianjin City, is applied.

Government S&T policies and programs do transfer to the grassroots innovators; the government support is continuous and all-round; the grassroots innovators' leadership is important to get the support; the research and diffusion projects that grassroots innovators received are integrated in some cases; and the S&T training contributes to farmers' research and project application.

There is a need to increase financial support to the grassroots innovators, provide guidance to them and set up a GRI reporting mechanism.

The paper gives an insight into the synergies and illustrates how and why the grassroots innovators benefit from the laws, policies and ensuing programs that are not directly meant for them.

The purpose of this paper is to explore in depth the impact and transmission mechanism of different international capital flows on domestic employment and wages in China within a systematic framework; also to reveal whether the empirical results can confirm the basic model inferences.

Using dynamic economic model and empirical experiment, this study designs and conducts the analysis within a systematic framework. The authors acquire the needed and credible empirical data.

The international capital inflows will increase the average wage level, and the international capital outflows will significantly reduce the level of domestic wages. The unofficially recorded capital flows would appear negatively related to the domestic wages. Due to the complexities of relevant elements, the impact of different international capital flows on domestic employment is of insignificance. It is noteworthy that the impact of international capital flows on the average wage changes of different provinces will tend to converge to a certain extent.

The results have reflected that the capital flows between the different provinces have no obvious frictions and barriers.

The paper explores in depth the impact and transmission mechanism of different international capital flows on domestic employment and wages within a systematic framework. The empirical analysis related to the China different provinces is an exploratory experiment.

The effect of using microcrystalline cellulose (MCC) as an additive in coating paint films for non-stick coatings was studied in this work. This paper aims to discuss the benefits of MCC blended in the coating paint film that consists of poly(methyl methacrylate) (PMMA) and dammar.

PMMA and dammar mixed at a specific Wt.% ratio with xylene as its solvent. Two sets of mixtures were prepared, where one mixture contained MCC and another, without. The mixtures were applied to metal substrates as coating paint films. The performance of the non-stick coating paint film was observed through the adhesive test between adhesion layers on the coating paint film and also through the cross-hatch test for the adhesion of the non-stick coating paint film to the metal substrate. The results correlate with the surface roughness and glossiness tests.

The results showed that for the coating paint films, Sample B consisted of 80:20 Wt.% ratio of PMMA-dammar with an addition of 5 Wt.% MCC had an excellent performance as non-stick coating paint films. The MCC formed microparticles on the surface of the coating paint film sample and this causes the coating paint film samples with MCC to develop a rougher surface compared to the coating paint film without MCC. Sample B coating paint film had the highest average surface roughness (Ra) of 383 µm. The cross-hatch test showed the coating paint film with the addition of MCC had stronger adhesiveness on the substrate’s surface thus prevents the coating from peeling off from the surface.

The developed coating paint film in this study would be suitable for outdoor applications to prevent illegal advertisements and stickers.

MCC added to the coating paint film improves the surface performance as a non-stick coating.

The purpose of this paper is to assess the government efforts in criminalising and combating bank fraud and corruption in China and their policy implications.

An integrated method is used to gather the data for this study, including government documents, statutes, congressional reports, legal cases, news reports, online survey and interviews with key policy‐makers, investigators and prosecutors.

This research finds that a major problem of bank fraud and corruption in China is the gigantic web of government officials, bank insiders and criminal businesses in committing fraud. The harshness of the Chinese law has not automatically resulted in making the struggle against bank fraud more effective. Law, enforcement and punishment are not certain, predictable, and applied consistently in order to deter fraud. Political, ideological and legal differences have hindered China's pursuit of escaped criminals in foreign countries.

This paper indicates that a three‐pronged approach – deterrence, prevention and education – is needed to address bank fraud and corruption. The industry's preventive efforts are of far greater importance than any extreme penalty. There is a need for a reconstruction of business ethics to ensure willing compliance with the law by individuals and organizations.

The paper is of value to law enforcement policy‐makers, banking regulators, financial institutions and academic researchers with interests in bank fraud and corruption issues.