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With the continuous technological development of automated driving and expansion of its application scope, the types of on-board equipment continue to be enriched and the computing capabilities of on-board equipment continue to increase and corresponding applications become more diverse. As the applications need to run on on-board equipment, the requirements for the computing capabilities of on-board equipment become higher. Mobile edge computing is one of the effective methods to solve practical application problems in automated driving.

In this study, in accordance with practical requirements, this paper proposed an optimal resource management allocation method of autonomous-vehicle-infrastructure cooperation in a mobile edge computing environment and conducted an experiment in practical application.

The design of the road-side unit module and its corresponding real-time operating system task coordination in edge computing are proposed in the study, as well as the method for edge computing load integration and heterogeneous computing. Then, the real-time scheduling of highly concurrent computation tasks, adaptive computation task migration method and edge server collaborative resource allocation method is proposed. Test results indicate that the method proposed in this study can greatly reduce the task computing delay, and the power consumption generally increases with the increase of task size and task complexity.

The results showed that the proposed method can achieve lower power consumption and lower computational overhead while ensuring the quality of service for users, indicating a great application prospect of the method.

Autonomous driving depends on the collection, processing and analysis of environmental information and vehicle information. Environmental perception and processing are important prerequisite for the safety of self-driving of vehicles; it involves road boundary detection, vehicle detection, pedestrian detection using sensors such as laser rangefinder, video camera, vehicle borne radar, etc.

Subjected to various environmental factors, the data clock information is often out of sync because of different data acquisition frequency, which leads to the difficulty in data fusion. In this study, according to practical requirements, a multi-sensor environmental perception collaborative method was first proposed; then, based on the principle of target priority, large-scale priority, moving target priority and difference priority, a multi-sensor data fusion optimization algorithm based on convolutional neural network was proposed.

The average unload scheduling delay of the algorithm for test data before and after optimization under different network transmission rates. It can be seen that with the improvement of network transmission rate and processing capacity, the unload scheduling delay decreased after optimization and the performance of the test results is the closest to the optimal solution indicating the excellent performance of the optimization algorithm and its adaptivity to different environments.

In this paper, the results showed that the proposed method significantly improved the redundancy and fault tolerance of the system thus ensuring fast and correct decision-making during driving.

Current dynamic and turbulent business environment calls for adopting newer strategies so that organizations can create a niche for itself in the market, mass customization (MC) being one of them. The purpose of this paper is to identify the critical enablers necessary to realize the objectives of MC, study the relationship among them and prioritize them in order of their importance so that a clear roadmap can be easily prepared for successful implementation of MC.

A portfolio of enablers of MC has been elicited from a thorough review of literature and experts’ opinion. Then, contextually relevant relations are chosen for each pair. In addition, a hierarchy-based structural model is developed by using Interpretive Structural Modelling (ISM) technique.

The findings classify the enablers into different groups according to their driving and dependence power. The findings show the enablers of strategic importance that need focused attention. This paper develops a structural model including hierarchy of enablers that forms a basis for the firms considering transition to MC.

This paper allows the firms to differentiate the dependent and independent variables and their mutual relationships, also by identifying and establishing the connection and relationships among the enabling practices; firms can better prioritize the resources and implementation effort to successfully provide each customer exactly they want.

This paper happens to be one of the first of its kind in the area of mass customization research that presents a contextual model having a hierarchy of enablers.

The effects of the thermo-mechanical treatment on the properties and microstructure of the Al–Cu–Mg–Ag alloy were investigated.

A short-duration preprecipitation process is designed prior to predeformation aging. The novel predeformation aging (solution treatment + holding at 185 °C for 15 min+ rolling deformation + aging at 185 °C, also named T8) was performed on a heat-resistant Al–Cu–Mg–Ag alloy.

The purpose of this study indicate that a short-duration heat treatment before predeformation is beneficial to the precipitation of O during the aging process. The precursors of O during this process might pin the dislocation and cause the grains to orient along some specific direction, which might be advantageous to the precipitation of O while disadvantageous to that of θ′. This novel thermal-mechanical process could result in an increase in the quantity and decrease in the size of the precipitation of O, which leads to a remarkable strength effect. The potential increases while the current density decreases with an increase in the deformation amount, which implies a smaller intergranular corrosion rate. The fine deformed structure leads to an opposite behavior in the exfoliation corrosion test compared with that for intergranular corrosion.

The intergranular corrosion resistance of the Al–Cu–Mg–Ag alloy is enhanced, whereas the exfoliation corrosion resistance is reduced by novel predeformation aging.

This study aims to explore the opposite effects of single-category versus multi-category products information diversity on consumer decision making. Further, the authors investigate the moderating role of three categories of visitors – direct, hesitant and hedonic – in the relationship between product information diversity and consumer decision making.

The research utilizes a sample of 1,101,062 product click streams from 4,200 consumers. Visitors are clustered using the k-means algorithm. The diversity of information recommendations for single and multi-category products is characterized using granularity and dispersion, respectively. Empirical analysis is conducted to examine their influence on the two-stage decision-making process of heterogeneous online visitors.

The study reveals that the impact of recommended information diversity on consumer decision making differs significantly between single-category and multiple-category products. Specifically, information diversity in single-category products enhances consumers' click and purchase intention, while information diversity in multiple-category products reduces consumers' click and purchase intention. Moreover, based on the analysis of online visiting heterogeneity, hesitant, direct and hedonic features enhance the positive impact of granularity on consumer decision making; while direct features exacerbate the negative impact of dispersion on consumer decision making.

First, the article provides support for studies related to information cocoon. Second, the research contributes evidence to support the information overload theory. Third, the research enriches the field of precision marketing theory.

Two challenges faced by automotive component design projects within contracted design agencies are (1) specification changes requested by the manufacturers and (2) product information or core technology knowledge leakage to external actors. We examine the effects of targeted boundary activities that address these challenges under the contingencies of environmental uncertainty and project complexity.

Based on Boundary management theory, a bidirectional model of boundary buffering was conceptualized in the context of design agency teams developing automotive components. A survey is derived from the proposed model. Regression analysis is performed using empirical data from 234 auto component design projects in Chinese design agencies.

Boundary buffering activities that strengthen outside-in boundaries and inside-out boundaries directly improve the final design quality. Further, the magnitude of effect for outside-in buffering on design quality is enhanced under environmental uncertainty, while the impact of inside-out buffering on design quality is enhanced under project complexity.

Boundary activities should consider differences in boundary targets, directional flow of information, and context of scope.

Automotive component design agents should attend to both outside-in and inside-out boundary buffering, especially under conditions of environmental uncertainty or project complexity.

The proposed bidirectional view on boundary buffering adds perspective to team boundary management theory. Specific contingencies include common risk elements of project complexity and environmental uncertainty not typically associated with the need for buffering activities.

Model-assisted probability of detection (MAPOD) is an important approach used as part of assessing the reliability of nondestructive testing systems. The purpose of this paper is to apply the polynomial chaos-based Kriging (PCK) metamodeling method to MAPOD for the first time to enable efficient uncertainty propagation, which is currently a major bottleneck when using accurate physics-based models.

In this paper, the state-of-the-art Kriging, polynomial chaos expansions (PCE) and PCK are applied to “a^ vs a”-based MAPOD of ultrasonic testing (UT) benchmark problems. In particular, Kriging interpolation matches the observations well, while PCE is capable of capturing the global trend accurately. The proposed UP approach for MAPOD using PCK adopts the PCE bases as the trend function of the universal Kriging model, aiming at combining advantages of both metamodels.

To reach a pre-set accuracy threshold, the PCK method requires 50 per cent fewer training points than the PCE method, and around one order of magnitude fewer than Kriging for the test cases considered. The relative differences on the key MAPOD metrics compared with those from the physics-based models are controlled within 1 per cent.

The contributions of this work are the first application of PCK metamodel for MAPOD analysis, the first comparison between PCK with the current state-of-the-art metamodels for MAPOD and new MAPOD results for the UT benchmark cases.

This paper provides the strategic direction and coordination mechanism selection for the intelligent transformation of manufacturing enterprises.

A theoretical framework is developed through grounded theory and case analysis.

Collaboration value is the building block of the intelligent product ecosystem. The ecosystem is upgraded via a path of product coordination, platform coordination and network coordination.

This paper provides a framework for enterprises to build an intelligent product ecosystem.

The proposed intelligent product ecosystem framework is new to the literature and lays down a fruitful avenue for future research.

The impact of information technology (IT) on mass customization (MC) capability has been implied in the literature but seldom subjected to empirical examination. This study seeks to theoretically relate four types of IT applications with MC capability and empirically examines these relationships.

This study identifies four types of IT that potentially support MC capability, including product configurator IT, new product development IT, manufacturing IT, and supplier collaboration IT. Drawing on organizational information processing theory, this study associates the four IT types with a manufacturer's MC capability. A structural equation model is tested using survey data collected from a sample of manufacturing plants that focus on product customization.

The empirical results indicate that two of the four IT types strongly support a manufacturer's MC capability.

No strong relationship between configurator IT and MC was observed, which calls for further investigation. Data used are cross‐sectional in nature. A set of refined IT measures should be developed in future studies. In addition, future studies could control for the effects of more variables that may impact IT use by mass customizers.

The paper identifies managerial opportunities for investing in IT to support or enhance MC capability.

This study provides a theoretical foundation for the IT‐MC relationship and develops a classification framework of IT applications in manufacturing plants. The study is one of the first efforts that empirically examines the impact of multiple types of IT applications on MC.