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The purpose of this paper is to explore a novel measurement approach for wheel-terrain contact angle using laser scanning sensors based on near-terrain perception. Laser scanning sensors have rarely been applied to the measurement of wheel-terrain contact angle for wheeled mobile robots (WMRs) in previous studies; however, it is an effective way to measure wheel-terrain contact angle directly with the advantages of simple, fast and high accuracy.

First, kinematics model for a WMR moving on rough terrain was developed, taking into consideration wheel slip and wheel-terrain contact angle. Second, the measurement principles of wheel-terrain contact angle using laser scanning sensors was presented, including “rigid wheel - rigid terrain” model and “rigid wheel - deformable terrain” model.

In the proposed approach, the measurement of wheel-terrain contact angle using laser scanning sensors was successfully demonstrated. The rationality of the approach was verified by experiments on rigid and sandy terrains with satisfactory results.

This paper proposes a novel, fast and effective wheel-terrain contact angle measurement approach for WMRs moving on both rigid and deformable terrains, using laser scanning sensors.

Improving the prediction accuracy of design time for complex products is significant for improving the accuracy of product development and control plans. The purpose of this study is to propose an intelligent pre-estimation method of design time for complex products based on v-SVM.

First, an evaluation model for designer knowledge abilities based on v-SVM is built, which considers the fuzziness and dynamics of designer knowledge abilities. Next, a pre-estimation method for the design time of complex products based on v-SVM is built. This method takes into account the impacts of designer knowledge abilities and design task characteristics on the design time. Then, an adaptive genetic algorithm is programmed to optimize the parameters in the evaluation model and the pre-estimation method. Finally, a practical application and comparative analysis of the proposed pre-estimation method is suggested to verify the validity and applicability of this research.

First, the evaluation of designer knowledge abilities is a prediction problem that is both fuzzy and multivariate time series. Second, the pre-estimation of design time is a problem that is fuzzy and multivariate. Third, the pre-estimation accuracy of the proposed method is higher when compared with traditional methods.

This paper presents an intelligent pre-estimation method of design time for complex products. Unlike previous research, the pre-estimation method takes into account the impacts of both the designer knowledge abilities and the design task characteristics on the design time.

Organizational networking has been acclaimed as a useful tool for knowledge transfer. However, the demerit associated with knowledge transfer is the leakage of commercially valuable information/knowledge. The purpose of this paper is to empirically establish a useful framework for helping collaborative projects reduce potential knowledge leakages.

The study applies a hybrid methodology to collect data. Postulates are tested through SPSS 23 and Process Macro 3.0 model 7.

The study finds that knowledge leakage is negatively influenced by contract completeness. Contract completeness has a positive effect on trust and a negative effect on distrust. Partner’s learning intent moderates the relationship of contract completeness with trust and distrust. Trust and distrust negatively mediate the relationship between contract completeness and knowledge leakage.

The study contributes to the theories of information processing and knowledge management by empirically stating how contract completeness, an organizational structure, supports knowledge management under the influence of partner’s opportunism. The study sees positivism in distrust and explains how practitioners maintain an observatory eye on partner’s opportunism by virtue of distrust ultimately adding value to the distrust literature.

The conceptual framework is novel because this is the first attempt to investigate the moderation effect of partner’s opportunism on the relationship of contract completeness and relational factors; and the mediation effects of trust and distrust between contract completeness and knowledge leakage.

Constructed upon the knowledge-based view, the purpose of this study is to investigate the interrelationship between internal marketing, knowledge management processes and knowledge worker satisfaction. The study also postulates that specific combinations of internal marketing dimensions and knowledge management processes can lead to improved knowledge worker satisfaction.

The study sample is gathered from 248 personnel of Pakistan higher education institutions (HEI’s). The interrelationships are checked through Smart PLS 3.2.8. The fuzzy set qualitative comparative analysis (fsQCA) is used to examine configurational paths for improving knowledge worker satisfaction.

The results of the study show that in HEI’s, internal marketing has a substantial influence on knowledge management processes, and knowledge management processes strongly enhance knowledge worker satisfaction. The result from fsQCA reveals multiple configurational paths to improve knowledge worker satisfaction.

There is a scarcity of research that has explored the association of internal marketing, knowledge management processes and knowledge worker satisfaction. This study attempts to examine their inter-relationships in HEI’s. Methodologically, the study contributes by combining direct and configurational methods to foster the knowledge of organizational (higher education) matters. The use of fsQCA reveals multiple pathways to improve knowledge worker satisfaction and exposes asymmetric relationships between internal marketing and knowledge management processes that lead to knowledge worker satisfaction. The study identifies the interactions among variables that might not be directly obvious via conventional symmetric methods.

Social media facilitates consumer exchanges on product opinions and provides comprehensive knowledge of online products. The interaction between consumers and e-retailers evolves into a collective set of dynamics within a complex system. Agent-based modeling is well suited to stimulate such complex systems. The purpose of this paper is to integrate agent-based model and technique for order performance by similarity to ideal solution (TOPSIS) to simulate decision behaviors of e-retailers in competitive online markets.

An agent-based network model using the TOPSIS driven by actual price data is developed. The authors ran an experimental model to simulate interactions between online consumers and e-retailers and to record simulation data. A nonparametric test is used to conduct data analysis and evaluate the sensibility of parameters.

Simulation results showed that different profits could be obtained for various brands under different social network structures. E-retailers could achieve more profits through cross-selling than single-selling; however, the highest profits can be achieved when some adopt cross-selling, whereas others use single-selling. From a game perspective, the equilibrium for price-adjustment frequency can be determined from the simulation data. Thus, price adjustment differences significantly affect e-retailer profit.

This study provides new insights into the evolutionary dynamics of online markets. This work also indicates how to build an integrated simulation model with an agent-based model and TOPSIS and how to use an integrated simulation model and interpret its results.

Telecommunications regulation in Hong Kong (China) is interesting as a case study of pro‐competitive regulation in a geographically small city of some 6.8 million people. It is also of particular interest because The Office of the Telecommunications Authority, the sector regulator, has for four years running (1999‐2002) been voted the best Asian regulator by readers of Telecom Asia. What policies were applied to warrant this sustained approval rating? This paper examines these policies and suggests further improvements.

The purpose of this paper is to establish the index system of leading industries in Kashgar urban agglomeration, and use the multi-attribute weighted intelligent grey target decision-making evaluation model to measure the comprehensive effect, so as to select the leading industries of Kashgar urban agglomeration.

First, 18 industries in Kashgar urban agglomerations are taken as objectives, and four indexes, namely, demand income elasticity index, growth rate index, labor productivity growth rate index and contribution rate of output value, are selected to construct an evaluation system for leading industry selection in Kashgar urban agglomerations. Then, grey incidence degree method is used to determine the decision-making power of each decision-making objective. Finally, multi-attribute weighted intelligent grey target decision-making evaluation model is used to measure the comprehensive effect of the objective system of leading industries in Kashgar urban agglomerations.

It can be seen that the multi-attribute weighted intelligent grey target decision-making evaluation model is more convenient to be used in selecting regional leading industries, and the results are accurate and feasible. Based on the calculation results and the actual economic development requirements of Kashgar urban agglomeration, the leading industries of Kashgar urban agglomeration can be determined as: wood processing, furniture, paper making and printing; wholesale and retail; construction; equipment manufacturing; transportation, storage and postal services.

First, it is a new method in selecting regional leading industry by using the multi-attribute weighted intelligent grey target decision-making evaluation model. Second, since there is relatively little research on Kashgar urban agglomeration, especially on leading industries in Kashgar urban agglomeration. The research in this paper can not only enrich the research on selecting leading industries in urban agglomeration but also provide scientific reference for relevant government agencies to formulate economic development plans.

High-ductility cementitious composites (HDCC) have an excellent crack controlled capacity and corrosion resistance capacity, which has a promising application in structure engineering under harsh environment. The purpose of this study is to explore the corrosion mechanism of steel bar in HDCC.

Intact and the pre-cracked HDCC specimens under the coupled action of different dry–wet cycles and chloride attack were designed, and intact normal concrete (NC) was also considered for comparison. Corrosion behavior of a steel bar embedded in HDCC was analyzed by an electrochemical method, a chloride permeability test and X-ray computed tomography.

Steel corrosion probability is related to the chloride permeability of the HDCC cover, and the chloride permeability resistance of HDCC is better than that of NC. Besides, crack is the key factor affecting the corrosion of steel bars, and the HDCC with narrower cracks have a lower corrosion rate. Slight pitting occurs at the crack tips. In addition, the self-healing products and corrosion products fill up the cracks in HDCC, preventing the external aggressive ions from entering and thereby decreasing the steel corrosion rate.

HDCC has a superior corrosion resistance than that of NC, effects of variable crack width on corrosion behavior of steel bar in HDCC under the coupled actions of different dry–wet cycles and chloride attack are investigated, which can provide the guide for the design application of HDCC material in structure engineering exposed to marine environment.

Conventional learning-based visual odometry (VO) systems usually use convolutional neural networks (CNN) to extract features, where some important context-related and attention-holding global features might be ignored. Without essential global features, VO system will be sensitive to various environmental perturbations. The purpose of this paper is to design a novel learning-based framework that aims to improve accuracy of learning-based VO without decreasing the generalization ability.

Instead of CNN, a context-gated convolution is adopted to build an end-to-end learning framework, which enables convolutional layers that dynamically capture representative local patterns and composes local features of interest under the guidance of global context. In addition, an attention mechanism module is introduced to further improve learning ability and enhance robustness and generalization ability of the VO system.

The proposed system is evaluated on the public data set KITTI and the self-collected data sets of our college building, where it shows competitive performance compared with some classical and state-of-the-art learning-based methods. Quantitative experimental results on the public data set KITTI show that compared with CNN-based VO methods, the average translational error and rotational error of all the test sequences are reduced by 45.63% and 37.22%, respectively.

The main contribution of this paper is that an end-to-end deep context gate convolutional VO system based on lightweight attention mechanism is proposed, which effectively improves the accuracy compared with other learning-based methods.

This paper aims to explore the impact of flexible human resource management system (FHRMS) on employee innovation performance in Chinese context. The boundary conditions of employability and organizational identity are also discussed.

This study is based on 322 questionnaire survey from employees in China. The questionnaires were collected at two time points. This study used the method of multi-level linear model analysis for empirical test.

This study has three conclusions. First, FHRMS have a significant positive impact on employee innovation performance, with employability playing a mediating role. Second, organizational identity plays a moderating role between employability and employee innovation performance. Third, organizational identity moderates the mediating effect of a human source management systems on employee innovation performance through employability.

The results are helpful to open the “black box” of the influence of FHRMS on employees’ individual innovation performance. This study provides a reference for enterprises to establish flexible human source management system to improve employee innovation performance.