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This study aims to examine the ability of Generative Pre-trained Transformer 4 (GPT-4), one of the most powerful large language models, to generate a literature review for peer-reviewed journal publications. The objective is to determine whether business scholars can rely on GPT-4’s assistance with literature reviews and how the nature of human–artificial intelligence (AI) interaction may affect the quality of the reviews generated by GPT-4.

A survey of 30 experienced researchers was conducted to assess the quality of the literature reviews generated by GPT-4 in comparison with a human-authored literature review published in a Social Science Citation Index (SSCI) journal. The data collected were then analyzed with analysis of variance to ascertain whether we may trust GPT-4’s assistance in writing literature reviews.

The statistical analysis reveals that when a highly structured approach being used, GPT-4 can generate a high-quality review comparable to that found in an SSCI journal publication. However, when a less structured approach is used, the generated review lacks comprehensive understating and critical analysis, and is unable to identify literature gaps for future research, although it performed well in adequate synthesis and quality writing. The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction.

The findings suggest that we may trust GPT-4 to generate literature reviews that align with the publication standards of a peer-reviewed journal when using a structured approach to human–AI interaction. Nonetheless, cautions should be taken due to the limitations of this study discussed in the text.

By breaking down the specific tasks of a literature review and using a quantitative rather than qualitative assessment method, this study provides robust and more objective findings about the ability of GPT-4 to assist us with a very important research task. The findings of this study should enhance our understanding of how GPT-4 may change our research endeavor and how we may take a full advantage of the advancement in AI technology in the future research.

The purpose of this study is to improve the stability and dyeing properties of natural curcumin by adsorption packaging technology, and promote the clean dyeing technology of wool fabrics.

The response surface method was used to optimize the dyeing process of wool fabrics. The color fastnesses and the K/S value of the dyed wool fabrics were tested and analyzed, as well as the scanning electron microscopy (SEM) observation of wool fibers.

The mordant dyeing method was optimized using the response surface method under pH 3.5 and a 1:50 dye bath ratio. The results showed that the mordant dyeing method was one-bath, two-step post-mordant and the optimized dyeing process was as follows: dyeing time 70 min, dyeing temperature 70°C and the dosage of mordant was 2% and yielding a K/S value of 35.22. The dyed wool had excellent rub and wash fastness (grade 4+), but inadequate light fastness, to be improved later. The results of SEM demonstrated that the optimized dyeing processes had no adverse effects on wool fibers.

No comprehensive and systematic study reports have been conducted on the dyeing process of wool fabric using natural curcumin pigment, which is adsorbed and packaged by ZIF-8, and researchers have not used statistical analysis to optimize the dyeing process using response surface methodology.

There are various uncertain and nonlinear problems in hydraulic legged robot systems, including parameter uncertainty, unmodeled dynamics and external disturbances. This study aims to eliminate uncertainties and improve the foot trajectory tracking control performance of hydraulic legged robots, a high-performance foot trajectory tracking control method based on fixed-time disturbance observers for hydraulic legged robots is proposed.

First, the robot leg mechanical system model and hydraulic system model of the hydraulic legged robot are established. Subsequently, two fixed-time disturbance observers are designed to address the unmatched lumped uncertainty and match lumped uncertainty in the system. Finally, the lumped uncertainties are compensated in the controller design, and the designed motion controller also achieves fixed-time stability.

Through simulation and experiments, it can be found that the proposed tracking control method based on fixed-time observers has better tracking control performance. The effectiveness and superiority of the proposed method have been verified.

Both the disturbance observers and the controller achieve fixed-time stability, effectively improving the performance of foot trajectory tracking control for hydraulic legged robots.

This study was conducted to analyze the effects of machining parameters on the specific energy consumption in the computerized numerical control lathe turning operation of a hardened alloy steel roll at low cutting speeds. The aim was to minimize its consumption.

The design matrix was based on three variable factors at three levels. Response surface methodology was used for the analysis of experimental results. Optimization was carried out by using the desirability function and genetic algorithm. A multiple regression model was used for relationship build-up.

According to desirability function, genetic algorithm and multiple regression analysis, optimal machining parameters were cutting speed 40 m/min, feed 0.2 mm/rev and depth of cut 0.50 mm, which resulted in minimal specific energy consumption of 0.78, 0.772 and 0.78 kJ/mm³, respectively. Correlation analysis and multiple regression model found a quadratic relationship between specific energy consumption with power consumption and material removal rate.

In the past, many researchers have developed mathematical models for specific energy consumption, but these models were developed at high cutting speed, and a majority of the models were based on the material removal rate as the independent variable. This research work developed a mathematical model based on the machining parameters as an independent variable at low cutting speeds, for a new type of large-sized hardened alloy steel roll. A multiple regression model was developed to build a quadratic relationship of specific energy consumption with power consumption and material removal rate. This work has a practical application in hot rolling industry.

This paper aims to facilitate reliable online diagnosis of early faults in the stator winding inter-turn short circuits of induction motors (IMs) under various operating conditions.

A novel fault characteristic component, the characteristic current amplitude, is proposed for the fault. Defined as the product of short-circuit coefficient and short-circuit current, the characteristic current is derived from the positive and negative-sequence components of the stator-side current and voltage.

Simulation models of the IMs pre- and postfault, along with an experimental platform for the motor’s inter-turn short circuit, were established. The characteristic current amplitude proves more robust against voltage unbalance and load variations, which offers enhanced reliability and sensitivity for early fault diagnosis of inter-turn short circuit in IMs stator windings.

A novel feature is proposed. Compared with negative-sequence current, which is considered as a traditional fault feature, the characteristic current amplitude exhibits a greater robustness against the imbalanced conditions, which simultaneously possesses the attributes of both reliability and expeditiousness in fault detection.

Building a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.

This paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.

Finally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.

In this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

Linear projects often involve lengthy construction periods, necessitating dynamic adjustments to the plan. Completely rescheduling remaining activities every time can lead to unnecessary time and cost wastage and significant deviations in resource supply. To address these issues, this paper proposes a dynamic scheduling method designed to effectively manage both time and cost during construction projects.

Determining the rescheduling frequency through a hybrid driving strategy and buffer mechanism, introducing rolling window technology to determine the scope of local rescheduling and constructing a local rescheduling model under the constraints of time and cost deviation with the objective of minimizing the cost. Combined decision-making for construction and rushing modes constrained by multiple construction scenarios. Opposite learning is introduced to optimize the hybrid algorithm solution.

Arithmetic examples and cases confirm the model’s feasibility and applicability. The results indicate that (1) continuous rescheduling throughout project construction is essential and effective and (2) a well-structured buffer mechanism can prevent redundant rescheduling and enhance overall control of cost and schedule deviations.

This study introduces an innovative dynamic scheduling framework for linear engineering, offering a method for effectively controlling schedule deviations during construction. The developed model enhances rescheduling efficiency and introduces a combined quantization strategy to increase the model’s applicability to linear engineering. This model emerges as a promising decision support tool, facilitating the implementation of sustainable construction scheduling practices.

The main data used to develop this case were remote, synchronous interviews with the three characters in the case. The authors conducted two interviews with the main character in the case, Geoff Brown, specifically: (1) an initial 30 min interview to determine the fit and focus of the case and to help create the interview protocol for the full case interview (this initial interview was conducted on March 12, 2024); and (2) an hour-long interview to ask targeted questions to fully develop the case narrative (this interview was conducted on March 28, 2024). Geoff Brown was also involved in reviewing drafts of the case, approving the final version of the case and reviewing the assignment questions in this instructors’ manual (IM).

This case focuses on Geoff Brown, Executive Director at Alberta Chicken Producers (ACP), which is a not-for-profit organization in Alberta, Canada, that is responsible for representing 250 regulated chicken producers. Brown is grappling with what to do with the remote/hybrid work policy at ACP. Part of the impetus for reconsidering this policy was the comments from ACP’s long-tenured Office Manager and Executive Assistant, who had been asking Brown to bring this policy forward to a staff meeting for discussion throughout the past year. Brown now feels ready to move these discussions forward but is unsure of how to proceed and what the best practices would be to ensure that the policy in place for remote work is beneficial for work engagement, individual and organizational work performance, work–life balance, employee relationships and fairness perceptions.

The target audience for this case is undergraduate and graduate students taking a course in the disciplines of human resources management or organizational behavior. This case will be especially relevant for a human resources management course when studying the topics of employee benefits (e.g. work–life balance), health and safety (e.g. stress) and work design (e.g. telecommuting), and this case will be especially relevant for an organizational behavior course when studying the topics of motivation (e.g. fairness), communication, organizational culture and decision-making.

The purpose of this study is to reveal the significant contribution of MXene on enhancing tribological properties and to obtain the influence mechanism of various factors on friction characteristics of rolling bearing under extreme conditions.

Under extreme working conditions, the friction characteristics of rolling bearings directly determine the safety and reliability of the transmission system. In this study, MXene is added to the origin lubricating grease (OLG) of rolling bearing to enhance their friction characteristics. Then, the effects of inner ring speed, radial load, grease filling volume and other factors on the friction coefficient of rolling bearing are analyzed using the Taguchi method.

The results indicate that the ranking of various factors affecting the friction coefficient is: radial load, inner ring speed, MXene additive content in grease and grease filling volume. Especially, the radial load and inner ring speed exhibit extremely significant effects, while the MXene additive content in grease (P < 0.05) has a significant influence on the friction coefficient of rolling bearing. The optimal condition for rolling bearing lubricated with MXene additives lubricating grease (MALG) achieves the lowest friction coefficient of 0.0049 under 1,000 rpm, 9 kN and 35% grease filling volume.

This study could offer reference solution for utilizing MXene nano-lubrication to fufill the demands of precision, heavy-load, or long-lifespan bearings. Furthermore, the lubrication approach has the potential to be expanded into aerospace, defense, and various industrial fields, thereby significantly promoting its practial engineering applications.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.