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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 study is to describe the proposed alpha solar rotary mechanism (ASRM) and how it is used to accurately modify the solar array of the China Space Station (CSS) in orbit to maintain continuous tracking of the sun to provide power. It also highlights the need to evaluate the performance of the ASRM and predict potential failure modes in various extreme scenarios.

To evaluate the performance of the ASRM, a dynamic model was created and tested under normal and faulty conditions. In addition, a multidirectional stiffness test was conducted on the prototype to verify the accuracy of the ASRM's dynamic model. The high-precision ASRM model was then used to predict potential failure modes and damaged parts in various extreme scenarios.

The simulation results were in good agreement with the test results, with a maximum error of less than 8.85%. The high-precision ASRM's model was able to accurately predict potential failure modes and damaged parts in extreme scenarios, demonstrating the effectiveness of the proposed model and simulation evaluation test.

The proposed high-precision ASRM model and simulation evaluation test provide an effective way to evaluate the structural safety and optimize the design of the spacecraft. This information can be used to improve the performance and reliability of the CSS's solar array and ensure continuous power supply to the station.

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.

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 traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.

The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.

The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.

The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

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 investigates query processing in MANETs. Cache techniques and multi‐join database operations are studied. For data caching, a group‐caching strategy is proposed. Using the cache and the index of the cached data, queries can be processed at a single node or within the group containing this single node. For multi‐join, a cost evaluation model and a query plan generation algorithm are presented. Query cost is evaluated based on the parameters including the size of the transmitted data, the transmission distance and the query cost at each single node. According to the evaluations, the nodes on which the query should be executed and the join order are determined. Theoretical analysis and experiment results show that the proposed group‐caching based query processing and the cost based join strategy are efficient in MANETs. It is suitable for the mobility, the disconnection and the multi‐hop features of MANETs. The communication cost between nodes is reduced and the efficiency of the query is improved greatly.

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.

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.