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To realize classification of different pavements, a road roughness acquisition system design and an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation algorithm for road roughness detection is presented in this paper. The developed measurement system, including hardware designs and algorithm for software, constitutes an independent system which is low-cost, convenient for installation and small.

The inputs of restricted Boltzmann machine deep neural network are the vehicle vertical acceleration power spectrum and the pitch acceleration power spectrum, which is calculated using ADAMS finite element software. Adaboost Backward Propagation algorithm is used in each restricted Boltzmann machine deep neural network classification model for fine-tuning given its performance of global searching. The algorithm is first applied to road spectrum detection and experiments indicate that the algorithm is suitable for detecting pavement roughness.

The detection rate of RBM deep neural network algorithm based on Adaboost Backward Propagation is up to 96 per cent, and the false positive rate is below 3.34 per cent. These indices are both better than the other supervised algorithms, which also performs better in extracting the intrinsic characteristics of data, and therefore improves the classification accuracy and classification quality. Additionally, the classification performance is optimized. The experimental results show that the algorithm can improve performance of restricted Boltzmann machine deep neural networks. The system can be used for detecting pavement roughness.

This paper presents an improved restricted Boltzmann machine deep neural network algorithm based on Adaboost Backward Propagation for identifying the road roughness. Through the restricted Boltzmann machine, it completes pre-training and initializing sample weights. The entire neural network is fine-tuned through the Adaboost Backward Propagation algorithm, verifying the validity of the algorithm on the MNIST data set. A quarter vehicle model is used as the foundation, and the vertical acceleration spectrum of the vehicle center of mass and pitch acceleration spectrum were obtained by simulation in ADAMS as the input samples. The experimental results show that the improved algorithm has better optimization ability, improves the detection rate and can detect the road roughness more effectively.

Value creation in engineering projects has received considerable research attention. However, buyers’ expectations regarding project value do not always translate into the actions of project team members (actors). Drawing upon resource orchestration theory to explore the micro-foundations of project value creation (PVC), we investigate how suppliers organize project team members to create project value that meets buyers’ expectations.

We adopted two instructive engineering projects as comparative cases to analyze patterns in actor interactions in PVC.

The results reveal “social interaction-based value creation” and “structural interaction-based value creation,” which explain the PVC process from the collective-level buyer’s expectations and supplier’s value orientation, to individual-level actor interactions (mobilizing, connecting, and networking), knowledge orchestration activities (structuring, bundling, and leveraging), and finally the value creation process (identifying, configuring, and generating) in project teams to generate collective-level project benefits and values.

We propose a macro-approach to micro-foundations, with a focus on the buyer-expected PVC process on the supplier side. Furthermore, we explain how suppliers organize individual actions and orchestrate the individual knowledge through two patterns of individual-level actor interactions for collective-level PVC.

Arbitrary constraints might be included into the problem domain in many engineering applications, which represent specific features such as multi-domain interfaces, cracks with small yield stresses, stiffeners attached on the plate for reinforcement and so on. To imprint these constraints into the final mesh, additional techniques need to be developed to treat these constraints properly.

This paper proposes an automatic approach to generate quadrilateral meshes for the geometric models with complex feature constraints. Firstly, the region is decomposed into sub-regions by the constraints, and then the quadrilateral mesh is generated in each sub-region that satisfies the constraints. A method that deals with constraint lines and points is presented. A distribution function is proposed to represent the distribution of mesh size over the region by using the Laplace equation. The density lines and points can be specified inside the region and reasonable mesh size distribution can be obtained by solving the Laplace equation.

An automatic method to define sub-regions is presented, and the user interaction can be avoided. An algorithm for constructing loops from constraint lines is proposed, which can deal with the randomly distributed constraint lines in a general way. A method is developed to deal with constraint points and quality elements can be generated around constraint points. A function defining the distribution of mesh size is put forward. The examples of constrained quadrilateral mesh generation in actual engineering analysis are presented to show the performance of the approach.

An automatic approach to constrained quadrilateral mesh generation is presented in this paper. It can generate required quality meshes for special applications with complex internal feature constraints.

The purpose of this paper is to study the closed-loop guidance algorithm for launch vehicles in an atmospheric ascent phase and present a numerical trajectory reconstruction algorithm to satisfy the real-time requirement of generating the guidance commands.

An optimal control model for an atmospheric ascent guidance system is established directly; following that, the detailed process for necessary conditions of the optimal control problem is re-derived based on the calculus of variations. As a result, the trajectory optimization problem can be reduced to a root-finding problem of algebraic equations based on the finite element method (FEM). To obtain an accurate solution, the Newton method is introduced to solve the roots in a guidance update cycle.

The presented approach can accurately and efficiently solve the trajectory optimization problems. A moderate number of unknowns can yield a good optimal solution, which is well suited for the open-loop guidance. To meet the requirements of the rapidity and accuracy for the close-loop guidance, the fewer number of unknowns is artificially chosen to reduce the calculation time, and the on-board trajectory planning strategy can increase the precision of the optimal solution along with the decrease of time-to-go.

The closed-loop guidance algorithm based on an FEM can be found in this paper, which can solve the optimal ascent guidance problems for launch vehicles in the atmospheric flight phase rapidly, accurately and efficiently.

This paper re-derives the necessary conditions of the optimal solution in a different way compared to the previous work, and the closed-loop guidance algorithm combined with the FEM is also a new thought for the optimal atmospheric ascent guidance problems.

The purpose of this paper is to understand the consumption and demand of Chinese citizens for public digital culture, and make suggestions for government-supported public digital culture providers.

Through a questionnaire survey, this study investigates the provision of public digital cultural services (PDCS) from the perspective of consumption and demands.

The results indicate: the Chinese populace as a whole had low expenses on digital cultural services, and had not effectively utilized them to support their own development; significant disparities exist between demographics, particularly between urban and rural residents; the populace were strongly interested in participation in public digital culture, but the services had low actual utilization rates; and the services had been unable to meet the users’ quality-related demands.

The first study to approach the provision of PDCS from the side of consumption and user demand.

This paper aims to propose and verify an improved cascade active disturbance rejection control (ADRC) scheme based on output redefinition for hypersonic vehicles (HSVs) with nonminimum phase characteristic and model uncertainties.

To handle the nonminimum phase characteristic, a tuning factor stabilizing internal dynamics is introduced to redefine the system output states; its effective range is determined by analyzing Byrnes–Isidori normalized form of the redefined system. The extended state observers (ESOs) are used to estimate the uncertainties, which include matched and mismatched items in the system. The controller compensates observations in real time and appends integral terms to improve robustness against the estimation errors of ESOs.

Theoretical and simulation results show that the stability of internal dynamics is guaranteed by the tuning factor and the tracking errors of external commands are globally asymptotically stable.

The control scheme in this paper is expected to generate a reliable way for dealing with nonminimum phase characteristic and model uncertainties of HSVs.

In the framework of ADRC, a concise form of redefined outputs is proposed, in which the tuning factor performs a decisive role in stabilizing the internal dynamics of HSVs. By introducing an integral term into the cascade ADRC scheme, the compensation accuracy of matched and mismatched disturbances is improved.

This study aims to verify the respective and interactive effects of subnational formal and informal institutions (i.e. legal effectiveness and social trust) on foreign firm performance, and further identify the contingent factor (i.e. institutional experience) that moderates these relationships.

Drawing on the institutional-based view, this study develops several hypotheses that are tested using a comprehensive dataset from four main data sources. The authors’ unit of analysis is foreign firms operating in China. The authors ran ordinary least squares (OLS) regression model to investigate the effects. A series of robustness tests and endogeneity tests were performed.

The results show that both legal effectiveness and social trust at subnational level positively affect foreign firm performance respectively. Legal effectiveness and social trust at subnational level have complementary effect in promoting the performance of foreign firms. Foreign firm's institutional experience in target region of emerging economies host country strengthens the positive impact of subnational legal effectiveness on performance, but weakens the positive impact of subnational social trust on performance.

It is important to fully understand the impact of heterogeneous institutional environments of subnational regions in emerging economies on foreign firm performance, which would help foreign firm make a more suitable secondary choice decision of investment destinations at the subnational regional level.

First, drawing on institutional-based view, the authors incorporate the subnational formal and informal institutional factors to investigate their impacts on foreign firm performance by switching the attention from national level to subnational level in emerging economy host countries. Second, this research furthers existing studies by bridging a missing link between both subnational formal and informal institutional environments and foreign firms' outcomes. Third, the authors prove that the model of subnational formal and informal institutions in influencing foreign firms' performance is contingent on their institutional experience in target subnational region of emerging economy host country.

This paper aims to propose a novel splicing detection method using a discriminative robust local binary pattern (DRLBP) with a support vector machine (SVM). Reliable detection of image splicing is of growing interest due to the extensive utilization of digital images as a communication medium and the availability of powerful image processing tools. Image splicing is a commonly used forgery technique in which a region of an image is copied and pasted to a different image to hide the original contents of the image.

The structural changes caused due to splicing are robustly described by DRLBP. The changes caused by image forgery are localized, so as a first step, localized description is divided into overlapping blocks by providing an image as input. DRLBP descriptor is calculated for each block, and the feature vector is created by concatenation. Finally, features are passed to the SVM classifier to predict whether the image is genuine or forged.

The performance and robustness of the method are evaluated on public domain benchmark data sets and achieved 98.95% prediction accuracy. The results are compared with state-of-the-art image splicing finding approaches, and it shows that the performance of the proposed method is improved using the given technique.

The proposed method is using DRLBP, an efficient texture descriptor, which combines both corner and inside design detail in a single representation. It produces discriminative and compact features in such a way that there is no need for the feature selection process to drop the redundant and insignificant features.

As supervisor incivility and its negative effect may impact employees’ psychological health and even the sustainable development of hospitality enterprises, this study aims to explore the channels through which it affects employee turnover intention in China’s hospitality industry and suggest possible mitigation measures.

This study adopted exploratory factor analysis, measurement model analysis and the mediation and moderation model and used SPSS and PROCESS for the analysis.

This study found that the impact of supervisor incivility on the employees’ turnover intention would be through employees’ ego depletion and revealed that organizational support would alleviate such a negative effect. However, organizational support might not mitigate the impact of supervisor incivility on the employees’ ego depletion, which is inconsistent with previous studies. This study inferred that organizational support might be somewhat related to organizational pressure, thereby enhancing the impact of supervisor incivility on the employees’ ego depletion.

This study not only enriches incivility literature but also suggests new insights into the mixed role of organizational support.

Unlike previous studies that mainly focused on workplace pressure from colleagues or customers, this study broadens our understanding of the employees’ turnover intention affected by supervisors’ workplace incivility and the mixed role of organizational support.

The purpose of this paper is to investigate the effects of dedication- and constraint-based mechanisms on users’ post-adoption behavior in the social networking site (SNS) context.

The proposed framework uses user satisfaction and trust belief to capture the dedication-based mechanism and perceived switching costs and social norms to capture the constraint-based mechanism. Hypotheses were tested by applying partial least squares to data from 250 experienced Facebook users. A structural equation modeling was used to test the validity of the proposed research models.

The analysis results show that SNS users’ continuance intention is jointly affected by two distinctive mechanisms: a dedication-based one and a constraint-based one, the former playing a more critical role. The findings indicate that both perceived relative benefits and perceived enjoyment significantly influence user satisfaction. Learning and network size were found to be the key predictors of perceived switching costs.

This study applies the dedication- and constraint-based models by incorporating numerous sets of antecedents. The framework provides a theoretical lens of how two distinctive mechanisms influence SNS users’ post-adoption behaviors.

The analysis results provide several insights that can aid SNS providers understand SNS users post-adoption behaviors. Moreover, the findings will help SNS providers effectively facilitate dedication- and constraint-based mechanisms by enhancing the key antecedents of two distinctive mechanisms.

SNSs have become an important component of individuals lives. However, few systematic works investigate the fundamental mechanisms leading to SNS users’ continued usage. In an attempt to extend the horizons of SNS research, this study incorporates a set of antecedents to dedication- and constraint-based models.