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This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

Early termination of public–private partnerships (PPPs) in China is caused by various risk factors, resulting in significant losses. This study aimed to clarify the key factors and identify the causal relationships among these factors.

Social network analysis (SNA) was used to analyze 37 risk factors that were summarized from 97 early terminated PPP cases and to identify the relationships among these key risk factors. Interpretive structural modeling (ISM) was conducted to explore the causal relationships. Data were collected from case documents, questionnaires and interviews.

A total of 17 key risk factors were identified and distributed in a hierarchical structure with six tiers. Among these key risk factors, the root causes affecting the early termination of PPP projects were government oversight in decision-making, local government transition, policy and law changes and force majeure. The direct cause was insufficient returns. Furthermore, local government and private sector defaults were essential mediating factors. Local government transition and the low willingness of the private sector were highlighted as potential key risks.

The cases and experts were all from China, and outcomes in other countries or cultures may differ from those of this study. Therefore, further studies are required.

This research provides knowledge regarding the key risk factors leading to the early termination of PPP projects and guidance on avoiding these factors and blocking the factors' transmission in the project lifecycle.

This study contributes to the knowledge of risk management by emphasizing the importance of local government transition, the low willingness of the private sector and project cooperation and operation, whose significance is ignored in the existing literature. The proposed ISM clarifies the role of risk factors in causing early termination and explains their transmission patterns.

This paper aims to focus on studying the addition of nano-tungsten disulfide (WS₂) on fretting wear performance of ultra-high-molecular-weight-polyethylene (UHMWPE).

In this study, the effect of WS₂ content on fretting wear performance of UHMWPE was investigated. The fretting wear performance of the UHMWPE and WS₂/UHMWPE nanocomposites were evaluated on oscillating reciprocating friction and wear tester. The data of the friction coefficient and the specific wear rate were obtained. The worn surfaces of composites were observed. The transfer film and its component were analyzed.

With the addition of 0.5% WS₂, the friction coefficient and specific wear rate increased. With the content increased to 1% and 1.5%, the friction coefficient and specific wear rate decreased. The lowest friction coefficient and specific wear rate were obtained with the addition of 1.5% nano-WS₂. Continuingly increasing content, the friction coefficient and wear rate increased but lower than that of pure UHMWPE.

The research indicated the fretting wear performance related to the content of nano-WS₂ with the incorporation of WS₂ into UHMWPE.

The result may help to choose the appropriate content.

The main originality of the research is to reveal the fretting behavior of UHMWPE and WS₂/UHMWPE nanocomposites. It makes us realize the nano-WS₂ had an effect on the fretting wear performance of UHMWPE.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0151/

China's New Rural Pension Program (NRPP) has been implemented for a decade, but the factors that facilitate rural residents' participation have received little attention. This study aims to investigate whether financial literacy has an influence on rural residents' behavior of participation in the NRPP. In particular, this study further verifies if high financial literacy is important and whether financial education can enhance the impact of financial literacy on current, long-term and dynamic pension decisions of rural households.

This study investigates the impact of financial literacy on rural residents' participation in China's NRPP using the China Household Financial Survey (CHFS) Data of 2015 and 2017. This study constructs an analytical framework for current, long-term and dynamic impacts and comprehensively analyzes the value of financial literacy in the decision making of the NRPP. This study uses the instrumental variable method to solve the possible endogeneity problem. In addition, the authors also demonstrate the positive role of high financial literacy in household pension decisions. Further analysis reveals gender and regional heterogeneity in the impact of financial literacy on pension decisions. The moderating effect model explores whether financial education has a significant moderating effect on financial literacy and pension decision making of the NRPP.

Financial literacy can improve the participation behavior of households in rural areas (dynamic effect) and promote their current and long-term participation in the NRPP, choosing a higher pension contribution level in the NRPP. However, financial literacy has no significant effect on the change in the contribution amount of the NRPP. Further research finds that high financial literacy has comparative advantages in household pension decision making in rural areas. There are gender and regional differences in the impact of financial literacy on pension decisions. In addition, effective financial literacy education enhances the current, long-term and dynamic impacts of residents' financial literacy on NRPP participation and pension contributions.

This study comprehensively considers the impact of financial literacy on pension decision making behavior from three aspects: current, long-term and dynamic, making up for the dearth in the existing literature that only focuses on the impact of financial literacy on current financial behaviors and bridging the gap between the theoretical framework and experimental results. Our study proposes new policy implications: (1) Governments and financial institutions should pay attention to financial literacy and education levels in rural areas and carry out financial education and training programs to increase social welfare levels by increasing rural residents' participation and pension contribution. (2) The community can strengthen the policy advocacy of the NRPP and make people develop a stronger sense of trust toward it. The government can also subsidize individual accounts through financial support.

This study comprehensively considers the impact of financial literacy on pension decision-making behavior from three aspects: current, long-term and dynamic, making up for the dearth in the existing literature that only focuses on the impact of financial literacy on current financial behaviors and bridging the gap between the theoretical framework and experimental results. Our study proposes new policy implications: (1) Governments and financial institutions should pay attention to financial literacy and education levels in rural areas and carry out financial education and training programs to increase social welfare levels by increasing rural residents' participation and pension contribution. (2) The community can strengthen the policy advocacy of the NRPP and make people develop a stronger sense of trust toward it. The government can also subsidize individual accounts through financial support.

This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

At present, electrical heating clothing is widely used to keep ourselves warm at low temperature. The purpose of this paper is to explore the heat transfer performance of electrical heating fabric and the thermal comfort of human skin at low temperature.

The combined model of skin-electrical heating fabric system was established to simulate human skin tissue wearing electrical heating clothing. A series of simulation experiments are designed on the basis of verifying the effectiveness of the combined model. The temperature distribution inside the combined model and on the skin surface under different heating powers is simulated and analyzed. At the same time, the influence of ambient temperature on the thermal performance of electrical heating fabric was explored.

The skin model with blood vessels reflected the temperature change of human skin wearing electrical heating clothing. The higher the heating power of the electrical heating fabric was, the greater the temperature of the skin surface changed, the faster the temperature rose and the longer the time required to reach the stable state would be. After the heating element was electrified, it had the greatest effect on the average temperature of the epidermis and dermis, had smaller effect on the average temperature of subcutaneous layer and had little effect on the temperature of blood vessels. When the heating power was the same, the higher the ambient temperature was, the more obvious the heating effect of electrical heating fabric was. Electrical heating fabrics with different heating powers were suitable for different ambient temperature ranges.

A reasonable and effective evaluation method for the thermal comfort of electrical heating fabric was provided by establishing the skin model and combined model of the skin-electrical heating fabric system. It provides a reference for the design and application of electrical heating clothing.

The purpose of this paper is to explore the key factors that affect consumer redemption intention toward mobile coupons recommended in social network sites (SNS).

A research model that integrates recommendation trust, positive utilities, and negative utilities of coupon redemption is developed. With the important role of trust in social recommendation taken into consideration, the key drivers of recommendation trust were analyzed in the model. Data were collected from 210 users with mobile coupon recommendation experience in one of the largest SNS (i.e. WeChat) in China. The authors used partial least squares technique to analyze the model.

Recommendation trust and positive utilities (economic benefits and perceived enjoyment) positively affect the intention of mobile coupon redemption. Perceived risk, as a negative utility, negatively influences coupon redemption intention. In addition, swift trust (structure assurance, perceived similarity, trust propensity, and expertise of the recommender), knowledge-based trust (familiarity with the retailers), and emotion-based trust (social tie strength) are key drivers that promote recommendation trust.

While prior research investigated mobile coupon redemption behavior in which coupons were issued by merchants, limited research analyzed consumer responses toward mobile coupons in social recommendation. This study examines the effects of recommendation trust, positive utilities, and negative utilities on mobile coupon redemption in the context of social recommendation and recognizes the key drivers of recommendation trust.

Precast wall lifting during prefabricated building construction faces multiple non-lean problems, such as inaccurate lifting-time estimation, unreasonable resource allocation and improper process design. This study aims to identify the pathways for improving lifting performance to advance lean construction of prefabricated buildings.

This study developed a methodological framework that integrates the discrete event simulation method, the elimination, combination, rearrangement and simplification (ECRS) technique and intelligent optimization tool. Two schemes of precast wall lifting, namely, the enterprise's business as usual (BAU) and enterprise-leading (EL) schemes, were set to benchmark lifting performance. Furthermore, a best-practice (BP) scheme was modeled from the perspective of lifting activity ECRS and resource allocation for performance optimization.

A real project was selected to test the effect of the methodological framework. The results showed that compared with the EL scheme, the BP scheme reduced the total lifting time (TLT) by 6.3% and mitigated the TLT uncertainty (the gap between the maximum and minimum time values) by 20.6%. Under the BP scheme, increasing the resource inputs produces an insignificant effect in reducing TLT, i.e. increasing the number of component operators in the caulking subprocess from one to two only shortened the TLT by 3.6%, and no further time reduction was achieved as more component operators were added.

To solve non-lean problems associated with prefabricated building construction, this study provides a methodological framework that can separate a typical precast wall lifting process into fine-level activities. Besides, it also identifies the pathways (including the learning effect mitigation, labor and machinery resource adjustment and activities’ improvement) to reducing TLT and its uncertainty.

An optimal solution method based on 2-norm is proposed in this study to solve the inverse kinematics multiple-solution problem caused by a high redundancy. The current research also presents a motion optimization based on the 2-Norm of high-redundant mobile humanoid robots, in which a kinematic model is designed through the entire modeling.

The current study designs a highly redundant humanoid mobile robot with a differential mobile platform. The high-redundancy mobile humanoid robot consists of three modular parts (differential driving platform with two degrees of freedom (DOF), namely, left and right arms with seven DOF, respectively) and has total of 14 DOFs. Given the high redundancy of humanoid mobile robot, a kinematic model is designed through the entire modeling and an optimal solution extraction method based on 2-norm is proposed to solve the inverse kinematics multiple solutions problem. That is, the 2-norm of the angle difference before and after rotation is used as the shortest stroke index to select the optimal solution. The optimal solution of the inverse kinematics equation in the step is obtained by solving the minimum value of the objective function of a step. Through the step-by-step cycle in the entire tracking process, the kinematic optimization of the highly redundant humanoid robot in the entire tracking process is realized.

Compared with the before and after motion optimizations based on the 2-norm algorithm of the robot, its motion after optimization shows minimal fluctuation, improved smoothness, limited energy consumption and short path during the entire mobile tracking and operating process.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

Motion optimization based on the 2-norm of a highly redundant humanoid mobile robot with the entire modeling is performed on the basis of the entire modeling. This motion optimization can make the highly redundant humanoid mobile robot’s motion path considerably short, minimize energy loss and shorten time. These researches provide a theoretical basis for the follow-up research of the service robot, including tracking and operating target, etc. Finally, the motion optimization algorithm is verified by the tracking and operating behaviors of the robot and an example.

The purpose of this paper is to target on individual perceptions of BIM practice in terms of BIM benefits, critical success factors (CSFs) and challenges in Chongqing which represented the less BIM-developed metropolitan cities in China.

Adopting a questionnaire-survey approach followed by statistical analysis, the study further divided the survey population from Chongqing into subgroups according to their employer types and organization sizes. A further subgroup analysis adopting statistical approach was conducted to investigate the effects of employer type and organization size on individual perceptions.

Subgroup analysis revealed that governmental employees held more conservative and neutral perceptions toward several items in BIM benefit, CSFs and challenges. It was inferred that smaller organizations with fewer than 100 full-time employees perceived more benefits of BIM in recruiting and retaining employees, and considered more critical of involving companies with BIM knowledge in their projects.

This study contributed to the body of knowledge in managerial BIM in terms that: it extended the research of individual perceptions toward BIM implementation by focusing on less BIM-mature regions; it contributed to previous studies of influencing factors to BIM practice-based perceptions by introducing factors related to organization type and sizes; and it would lead to future research in establishing BIM climate and culture which address perceptions and behaviors in BIM adoption at both individual and organizational levels.