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Physical environments, especially the sound environments of ILSs on a university campus, have become increasingly important in satisfying the diverse needs of students. Poor sound environments are widely acknowledged to lead to inefficient and underutilised spaces and to negatively influence students' learning outcomes. This study proposes two hypotheses to explore whether students' sound environment perceptions are related to their individual characteristics and whether students' preferences for the type of ILS are related to their sound environment sensitivities.

An investigation through a questionnaire survey has been conducted on both students' individual characteristics affecting their sound environment perceptions in informal learning spaces (ILSs) of a university campus and their sensitivities to the sound environments in ILSs affecting their preferences for the type of ILSs.

The research findings indicate that students' sound environment perceptions are associated with some of their individual characteristics. In addition, the results show that students' sound environment sensitivities affect their preferences for the type of ILS they occupy.

This study could help architects and managers of university learning spaces to provide better sound environments for students, thereby improving their learning outcomes. The article contributes valuable insights into the correlation between students' individual characteristics, sound environment perceptions and preferences for ILSs. The research findings add to the existing knowledge in this field and offer practical implications for enhancing design and management of university learning environments.

The purpose of this paper is to analyse privacy and interaction preferences in the social dimension of individual learning students and how the spatial configuration affects individual learners’ choices of learning spaces.

This empirical survey study was conducted in an Australian university’s informal learning spaces. Space syntax theories are applied to construct a four-quadrant theoretical framework.

The research findings indicate that based on the differences between students in their individual characteristics, there are significant differences in their needs for privacy and interaction. This study reveals that the spatial configuration affects individual learners’ choices of learning spaces.

This study could assist universities in providing students with more effective and diverse informal learning spaces.

The purpose of this paper is to propose meta‐synthetic ideas and knowledge asset management approaches to build a comprehensive strategic framework for Beijing City in China.

Methods include a review of relevant literature in both English and Chinese, case studies of different types of support frameworks in the UK, the USA, Singapore and Hong Kong, formulation of a meta‐synthetic support framework for Beijing City, and justification of its application to policy development by various studies. Three stages of meta‐synthetic support frameworks are proposed.

The suggested meta‐synthetic support frameworks are highly appropriate for the optimisation of, and innovation in, management and services systems of government information resources. The proposed knowledge asset management approaches offer significant practical value in improving the competence and capabilities of service‐oriented government, providing a set of solutions to identified, urgent problems, including a joint administration system for creating value, a release and distribution management system for sharing and protecting value, and a licensing and authorisation management system for adding value.

This paper focuses on the formulation of a theoretical support framework for the reuse of government information resources and the justification of its effectiveness to guide policy development at strategic level. Case studies of its application at operational level are ongoing and will be discussed in future papers.

The suggested meta‐synthetic support frameworks support the efficiency, effectiveness and economy of intelligent traffic administration, good governance of value‐added services based on government information resources, and intellectual activity around city travel and traffic. The study has wide implications for the improvement of service‐oriented government performance, public satisfaction and the image of government.

The paper presents the adaptation of meta‐synthetic ideas and knowledge asset management approaches to collaboration, optimisation, innovation and compliance management issues in the reuse of government information resources. The advantages of different types of support systems and frameworks are integrated as a coherent whole for a strategic framework of legal, regulatory and standards support to China and Beijing.

The purpose of the paper is to identify the main determinants of the development of the neighbourhood electric vehicle (NEV) industry in China, including influences from private stakeholders as well as the government, and domestic as well as foreign interest groups. Particular emphasis is placed on the role of state‐owned enterprises (SOEs) and the relations between the government and the SOE sector.

The paper follows an inductive approach, and is largely based on interviews with industry actors and representatives for relevant government agencies, complemented with secondary data.

The preliminary findings suggest that unlike Western market economies, where the impact of public policy on innovation is relatively transparent and well recorded (in the form of fiscal and financial incentives and formal legislation), much of Chinese innovation is driven by less formal decisions taken in the nexus between SOEs and relevant state agencies. Hence, although China suffers no lack of legislation at various levels (including laws, decrees, and guidelines) it is often difficult to identify the specific drivers for change.

The findings are useful for understanding the development of the NEV industry in China.

The current paper is the first application of the GIST (Governance of Innovation towards Sustainability Technology) framework to the case of the Chinese NEV industry.

Spray curing has become the preferred curing method for most cement concrete members because of its lower cost and sound effect. However, the spray curing quality of members is vulnerable to random variation environment factors and anthropogenic interferences. This paper aims to introduce the machine learning algorithm into the spray curing system to optimize its control method to improve the spray curing quality of members.

The critical parameters affecting the spray curing quality of members were collected through experiments, such as the temperature and humidity of the member's surface, the temperature, humidity and wind speed of the environment. The C4.5 algorithm was used as a weak classifier algorithm, and the AdaBoost.M1 algorithm was used to cascade multiple weak classifiers to form a robust classifier according to the collected data.

The results showed that the model constructed by the AdaBoost.M1 algorithm had achieved higher accuracy and robustness among the two algorithms. Based on the classification model built by the AdaBoost.M1 algorithm, the spray curing system can cause automatic decision-making spray switching according to the member's real-time curing state and environment.

With the classification model constructed by the AdaBoost.M1 algorithm, the spray curing system can overcome the disadvantages that external factors greatly influence the current control method of the spray curing system, and the intelligent control of the spray curing system was realized to a certain extent. This paper provides a reference for applying machine learning algorithms in the intellectual transformation of bridge construction equipment.

The purpose of this article is to better understand Japanese manufacturing in mainland China and clarify how traditional Japanese subcontracting has changed and is changing to fit the economic environment there.

This article looks at the subcontracting practices of the Toyota Group along with the evolution of Shanghai Koito's operations in mainland China. The research for this study was conducted from 1995‐2003 and is based on visits to Toyota's China headquarters in Beijing and its technical center in Tianjin, Shanghai Koito Company, Sichuan Toyota, and Tianjin Toyota.

When Japanese style subcontracting in mainland China is compared with that of traditional Japanese subcontracting, a stark contrast is revealed. First of all, it is clear that Japanese‐affiliated enterprises in China are moving away from an insular, vertical subcontracting structure dominated by a single assembler. In the new subcontracting system, characteristic features – such as a broad customer base and localization – contrast with earlier features that included a substantial delegation of authority, regulated interfirm competition, and long‐term relations.

This paper is based on two case studies and so, while its findings may be accurate for the companies in question, helpful for understanding Japan's auto industry in mainland China, and may be more widely applicable, the findings are unlikely to be universally applicable.

With short‐term guidance corresponding to the needs of localization and the effective use of cheap labor coming to the fore, the examples of Toyota and Shanghai Koito may provide helpful illustrations of the kind of adaptation needed to succeed in mainland China. In particular, by moving away from a reliance on its traditional Japanese customers for sales, Shanghai Koito seems to have positioned itself well to avoid the hardship of dwindling sales that other more traditionally oriented Japanese suppliers have begun to face. Moreover, its growing independence may be an important indicator of what the future may look like for Japanese manufacturing.

Looking at the history of industrial development in East Asia, the adaptation of Japanese business practices to different economies in the region has been an important theme. This study provides an up‐to‐date review of a number of current issues facing Japanese automakers as they develop their operations in mainland China.

Project portfolio risk (PPR) management plays an important role in promoting the smooth implementation of a project portfolio (PP). Accurate PPR prediction helps managers cope with risks timely in complicated PP environments. However, studies on accurate PPR impact degree prediction, which consists of both risk occurrence probabilities and risk impact consequences considering project interactions, are limited. This study aims to model PPR prediction and expand PPR prediction tools.

In this study, the authors build a PPR prediction model based on a genetic algorithm and back-propagation neural network (GA-BPNN) integrated with entropy-trapezoidal fuzzy numbers. Then, the authors verify the proposed model with real data and obtain PPR impact degrees.

The test results indicate that the proposed method achieves an average absolute error of 0.002 and an average prediction accuracy rate of 97.8%. The former is reduced by 0.038, while the latter is improved by 32.1% when compared with the results of the original BPNN model. Finally, the authors conduct an index sensitivity analysis for identifying critical risks to effectively control them.

This study develops a hybrid PPR prediction model that integrates a GA-BPNN with entropy-trapezoidal fuzzy numbers. The authors use this model to predict PPR impact degrees, which consist of both risk occurrence probabilities and risk impact consequences considering project interactions. The results provide insights into PPR management.

The emergence of the COVID-19 epidemic has considerably increased the intricacy of information, exacerbating the difficulties firms encounter in efficiently processing and understanding accurate data and knowledge. Consequently, the COVID-19 epidemic has profoundly exacerbated production ambiguity for firms, thereby disrupting their regular business operations and supply chain activities. Digital technologies (DTs) are essential tools for firms to process and interpret information and knowledge, thereby improving their resilience against supply chain interruptions.

This research investigates the effect of digital technologies on firm resilience throughout COVID-19, utilizing PLS-SEM and artificial neural networks (ANN) derived from a comprehensive survey of Pakistani manufacturing firms.

Our research assesses the mediating role of supply chain integration, memory, and absorptive capacity, as well as the moderating influence of information complexity. The outcomes demonstrate that supply chain integration (SCI), memory (SCM), and absorptive capacity (SCAC) mediate digital technologies’ influence on firm resilience. Moreover, in situations where information is highly complex, DTs have a greater effect on a firm’s resilience.

The results enhance our comprehension and awareness of the resilience-related effects of DTs and offer significant management insights for strengthening firm resilience in the setting of the COVID-19 pandemic.

The purpose of this paper is to clarify the relationship between fatigue life and kinematics of angular contact ball bearing. It proposes a new modeling method of spin to roll ratio based on raceway friction, which is more accurate than the traditional raceway control theory.

The uniform model of spin to roll ratio based on raceway friction in a wide speed range is proposed using quasi-statics method, which considers centrifugal force, gyroscopic moment, friction force of raceway and other influencing factors. The accuracy is considerably improved compared with the static model without increasing too much computation.

A uniform model for spin to roll ratio of angular contact ball bearing based on raceway friction is established, and quite different relationships between fatigue life and speed under two operating conditions are found.

The conclusion of this paper is based on the bearing basic fatigue life calculation theory provided by ISO/TS 16281; however, the accuracy of theory needs to be further verified.

This paper provides guidance for applying angular contact ball bearing, especially at a high speed.

This paper reveals the changing trend of fatigue life of angular contact ball bearing with the speed under different loads.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2020-0030

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.