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The purpose of this paper is to design the whole structure of high-speed automatic casing system (HSACS) for lithium-ion battery (LIB), and verify its rationality and reliability by kinematic simulation and casing test.

Based on the software of SolidWorks and ADAMS, the structure of working mechanisms for HSACS was designed, and virtual prototype models of HSACS and main turntable were established to realize the kinematic analysis. The HSACS casing test was also carried out and compared with simulation.

Simulation results for the designed HSACS were presented graphically and analyzed. The graphical results indicate that the coordination motions among the working mechanisms of HSACS are reasonable and no interference occurs. Casing test results show that the casing quality and production efficiency of HSACS equipment are satisfying.

In the present work, the developed virtual prototype models of HSACS provide reliable kinematic analysis results for the structural design of HSACS, and reasonable motion relations are realized in the designed structure of HSACS. Furthermore, not only the casing quality requirements can be met, but also the production efficiency of high-speed automatic casing machine for LIB is greatly improved via this kind of new HSACS structure.

The purpose of this article is to analyze the state-of-the art in a systematic way, identifying the main research groups and their related topics. The types of studies found are fundamental for understanding the application of artificial neural networks (ANNs) in cemented soils and the potential for using the technique, as well as the feasibility of extrapolation to new geotechnical or civil and environmental engineering segments.

This work is characterized as being bibliometric and systematic research of an exploratory perspective of state-of-the-art. It also persuades the qualitative and quantitative data analysis of cemented soil improvement, biocemented or microbially induced calcite precipitation (MICP) soil improvement by prediction/modeling by ANN. This study sought to compile and study the state of the art of the topic which possibilities to have a critical view about the theme. To do so, two main databases were analyzed: Scopus and Web of Science. Systematic review techniques, as well as bibliometric indicators, were implemented.

This paper connected the network between the achievements of the researches and illustrated the main application of ANNs in soil improvement prediction, specifically on cemented-based soils and biocemented soils (e.g. MICP technique). Also, as a bibliometric and systematic review, this work could achieve the key points in the absence of researches involving soil-ANN, and it provided the understanding of the lack of exploratory studies to be approached in the near future.

Because of the research topic the article suggested other applications of ANNs in geotechnical engineering, such as other tests not related to geomechanical resistance such as unconfined compression test test and triaxial test.

This article systematically and critically presents some interesting points in the direction of future research, such as the non-approach to the use of ANNs in biocementation processes, such as MICP.

Regarding the social environment, the paper brings approaches on methods that somehow mitigate the computational use, or elements necessary for geotechnical improvement of the soil, thereby optimizing the same consequently.

Neural networks have been studied for a long time in engineering, but the current computational power has increased the implementation for several engineering applications. Besides that, soil cementation is a widespread technique and its prediction modes often require high computational strength, such parameters can be mitigated with the use of ANNs, because artificial intelligence seeks learning from the implementation of the data set, reducing computational cost and increasing accuracy.

The purpose of this paper is to empirically investigate the effect of rural finance on industrial integration of rural primary, secondary and tertiary industries.

Using household-level data collected by Third National Agricultural Census and the provincial-level data collected from Wind database, the authors estimated the impacts of rural finance on rural industrial integration using Logit and Probit regression models. Further, the authors examined how the effect of rural finance varies with the age and education of householders, and with household and provincial characteristics by investigating the moderating effect.

The findings show that rural finance has a significant and positive effect on promoting farmers’ participation in new agricultural management organizations. This effect is more obvious in families whose householders are 40–50 years old, or families that have more educated members. This is because the middle-aged or educated people are more willing to accept and take part in industrial integration. The results further indicate that rural finance has a greater effect on industrial integration in provinces with a high degree of marketization, and in provinces with the high output value of industries and services in agricultural intermediate input.

The authors investigated the impact of rural finance on rural industrial integration empirically, and this topic is rarely covered before. The findings of this study also enrich the literature on financial development and economic growth as well as provide policy suggestions on how to promote rural industrial integration.

Considering the substantial benefits derived from the use of Building Information Modeling (BIM) in construction projects, governments and its related sectors have introduced mandatory policies requiring the use of BIM. However, little is known about the impact of mandatory policies on BIM-based project performance. Therefore, the purpose of this paper is to provide a systematical understanding on the impact of policy interventions on the implementation practice of innovative technologies.

This paper utilizes the propensity score matching and difference in differences (PSM-DID) method to investigate the impact of policy interventions on BIM-based project performance. Using the panel data collected from 2015 to 2021 in the Hong Kong construction industry, this paper explores the impact of the first mandatory BIM policy on the BIM-based project performance of three key stakeholders.

The subjective BIM performance and BIM return on investment (ROI) have significantly improved after implementing the mandatory BIM policy. The promotion effect of mandatory BIM policy on BIM-based project performance gradually increases over time. Moreover, the promotion effect of mandatory BIM policy on BIM performance shows significant heterogeneity for different stakeholders and organizations of different sizes.

This study examined the impact of policy interventions on BIM-based project performance. The research findings can provide a holistic understanding of the potential implications of innovative mandatory policy in performance improvement and offer some constructive suggestions to policymakers and industry practitioners to promote the penetration of BIM in the construction industry.

In order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots.

Choosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics.

The findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots.

Most of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336.

The purpose of this paper is to develop an appropriate machine learning model for predicting soil compaction degree while also examining the contribution rates of three influential factors: moisture content, electrical conductivity and temperature, towards the prediction of soil compaction degree.

Taking fine-grained soil A and B as the research object, this paper utilized the laboratory test data, including compaction parameter (moisture content), electrical parameter (electrical conductivity) and temperature, to predict soil degree of compaction based on five types of commonly used machine learning models (19 models in total). According to the prediction results, these models were preliminarily compared and further evaluated.

The Gaussian process regression model has a good effect on the prediction of degree of compaction of the two kinds of soils: the error rates of the prediction of degree of compaction for fine-grained soil A and B are within 6 and 8%, respectively. As per the order, the contribution rates manifest as: moisture content > electrical conductivity >> temperature.

By using moisture content, electrical conductivity, temperature to predict the compaction degree directly, the predicted value of the compaction degree can be obtained with higher accuracy and the detection efficiency of the compaction degree can be improved.

Job seekers frequently experience incivility during interactions with recruiters, which can negatively influence their job search behaviors. However, the underlying causes of such incivility remain underexplored. Based on attribution theory, this study examines how recruiters’ attributions about leader–member exchange (LMX) influence their psychological entitlement, which in turn shapes incivility toward job seekers.

The survey data was collected in three waves from 320 recruiters involved in the recruitment process.

The results found that when recruiters hold high levels of self-serving attribution, LMX quality is positively related to their psychological entitlement, which, in turn, promotes incivility toward job seekers.

Leaders should pay special attention to recruiters with high self-serving attributions when establishing LMX with them, as high-quality LMX may elicit a sense of psychological entitlement and potentially influence their uncivil behaviors.

Unlike prior studies that have focused on the negative outcomes of incivility experienced by job seekers, this study empirically reveals the potential antecedents of such incivility.

The purpose of this paper is to provide theoretical basis and data support for researchers to choose appropriate international partners, provide a basis for Chinese research funding agencies, such as National Natural Science Foundation of China (NSFC) to formulate international research collaboration (IRC) strategies and policies and provide recommendations for the improvement of the internationalization level of China's basic scientific research.

Based on existing research, this study took output of “Major International (Regional) Joint Research Project” (MIJRP) funded by NSFC and participated by Chinese scholars in the meantime as the analysis object, proposed hypotheses and constructed the indicators of IRC and research output (RO). In addition, the mathematical statistics was used to compare the RO of China's IRC and nonIRC, and the statistical analysis model was used to measure the influence on RO of collaboration country's research capacity, research collaboration between China and US, scope of international research collaboration and reprint author country.

The RO of China's IRC is higher than that of nonIRC; research capacity of collaboration country has no inevitable effect on the RO of China's IRC; the RO of China's IRC participated by Americans is higher than that without American scholars; expanding the scope of China's IRC to some degree can increase RO; the RO of China's IRC led by foreigners is higher than that led by Chinese. In particular, China–US IRC and foreign scholars acting as the reprint author are two major factors for the RO of China's IRC.

Most of the traditional research on IRC are based on the co-author papers, and this study tried to analyze the characteristics and regularities on IRC from a new view of international collaboration projects, which can be a supplement to the traditional international collaboration research on co-author papers.

The purpose of this paper is to design a biomimetic surface structure for use in a glass transport device to enhance the suspension lift of a glass transport unit.

This paper presents a surface structure of a suspended glass transport device based on the principle of bionics. First, a mapping model is constructed based on the wing structure. Second, the optimal structural parameters are given according to genetic algorithm optimization. Finally, the experimental comparison of the test bench verified the feasibility of the theory.

Through experimental comparison, the biomimetic suspension glass transport device saves 20% of air pressure compared with the ordinary suspended glass transport device, which verifies the effectiveness of the theoretical method.

This paper proposes a suspended glass transport device based on the principle of bionics, which saves the air pressure required for work. It is expected to be used in suspension glass transport devices.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-09-2019-0389/

This paper aims to investigate the part feeding scheduling problem with electric vehicles (EVs) for automotive assembly lines. A point-to-point part feeding model has been formulated to minimize the number of EVs and the maximum handling time by specifying the EVs and sequence of all the delivery tasks.

First, a mathematical programming model of point-to-point part feeding scheduling problem (PTPPFSP) with EVs is presented. Because the PTPPFSP is NP-hard, an improved multi-objective cuckoo search (IMCS) algorithm is developed with novel search strategies, possessing the self-adaptive Levy flights, the Gaussian mutation and elite selection strategy to strengthen the algorithm’s optimization performance. In addition, two local search operators are designed for deep optimization. The effectiveness of the IMCS algorithm is verified by dealing with the PTPPFSP in different problem scales.

Numerical experiments are used to demonstrate how the IMCS algorithm serves as an efficient method to solve the PTPPFSP with EVs. The effectiveness and feasibility of the IMCS algorithm are validated by approximate Pareto fronts obtained from the instances of different problem scales. The computational results show that the IMCS algorithm can achieve better performance than the other high-performing algorithms in terms of solution quality, convergence and diversity.

This study is applicable without regard to the breakdown of EVs. The current research contributes to the scheduling of in-plant logistics for automotive assembly lines, and it could be modified to cope with similar part feeding scheduling problems characterized by just-in-time (JIT) delivery.

Both limited electricity capacity and no earliness and tardiness constraints are considered, and the scheduling problem is solved satisfactorily and innovatively for an efficient JIT part feeding with EVs applied to in-plant logistics.