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The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σ_RS and ε_PEEQ that discern varying wear situations, thereby contributing to the enrichment of wear theory. Furthermore, the findings serve as a foundational basis for nondestructive and in situ wear detection methodologies, such as nonlinear ultrasonic detection, known for its sensitivity to σ_RS and ε_PEEQ.

This paper elucidates the wear mechanism through the lens of residual stress (σ_RS) and plastic deformation within distinct fretting regimes, using a two-dimensional cylindrical/flat contact model. It specifically explores the impact of the displacement amplitude and cycles on the distribution of residual stress and equivalent plastic strain (ε_PEEQ) in both gross slip regime and partial slip regimes.

Therefore, when surface observation of wear is challenging, detecting the σ_RS trend at the center/edge, region width and ε_PEEQ distribution, as well as the maximum σ_RS distribution along the depth, proves effective in distinguishing wear situations (partial or gross slip regimes). However, discerning wear situations based on ε_PEEQ along the depth direction remains challenging. Moreover, in the gross slip regime, using σ_RS distribution or ε_PEEQ along the width direction rather than the depth direction can effectively provide feedback on cycles and wear range.

This work introduces a novel perspective for investigating wear theory through the distribution of residual stress (σ_RS) and equivalent plastic strain (ε_PEEQ). It presents a feasible detection theory for wear situations using nondestructive and in situ methods, such as nonlinear ultrasonic detection, which is sensitive to σ_RS and ε_PEEQ.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0005/

The purpose of this paper is to investigate the effect of hexagonal boron nitride (h-BN) and poly (phenyl p-hydroxybenzoate) (PHBA) on improving the torsional tribological behavior of polytetrafluoroethylene (PTFE).

This paper investigates the torsional tribological behavior of PTFE composites filled with h-BN and PHBA under different angular displacements with a plane-on-plane torsional friction tester. The worn surface of PTFE composites was investigated by using a scanning electron microscope.

The shape of T–Θ curves of PTFE composites was influenced by both content fillers and torsional angule. The material with a higher coefficient of sliding friction exhibited the larger torsional angle under which the torsional regime transited from a partial slip to a gross slip. PTFE composites filled with 20 weight per cent PHBA and 10 weight per cent h-BN showed the best anti-wear properties. The specific wear rate of composites exhibits a negative correlation with material hardness. The wear volume loss presents a positive correlation with friction dissipation energy. The specific wear rate of all composites decreased with increasing torsional angle. The dominant wear mechanism of pure PTFE was adhesive wear. The slight plastic flow and plowing occurred on the worn surfaces of PTFE composites because of the higher hardness of composites and the lubrication of h-BN particles with layer crystal structure.

This paper put forward a kind of PTFE composite with low torsional wear rate, which can be used in the sliding slewing bearing or the center plate of a bogie.

Environmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control.

A biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand.

The proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO₂ emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO₂ emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO₂ emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO₂ Emissions Control Reduction.” It can provide managers with a way to double-target conversion.

We proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.

The purpose of this work is to controllably synthesize a carbon aerogel with programmable functionally graded performance via a simple and effective strategy.

This work uses polyvinyl alcohol (PVA) via the controllable sol-gel, lyophilization, and carbonization approach to achieve a programmable carbon aerogel. This design has the advantages of low raw material and preparation cost, simple and controllable synthetic process and low carbonization temperature.

The thermal stability and microstructure of PVA aerogel can be controlled by the crosslinking agent content within a certain range. The crosslinking agent content and the carbonization temperature are the key factors for functionally graded programming of carbon aerogels, including microstructure, oxygen-containing functional groups and adsorption performance. The adsorption ratio and adsorption rate of uranium can be controlled by adjusting initial concentration and pH value of the uranium solution. The 2.5%25 carbon aerogel with carbonization temperature of 350 °C has excellent adsorption performance when the initial concentration of uranium solution is 32 ppm at pH 7.5.

As a new type of lightweight nano-porous amorphous carbon material, this carbon aerogel has many excellent properties.

This work presents a simple, low cost and controllable strategy for functionally graded programming of novel carbon aerogel. This carbon aerogel has great potential for application in various fields such as uranium recovery, wastewater treatment, sound absorption and shock absorption.

The purpose of this paper is twofold: first, to explore how China uses a social credit system as part of its “data-driven authoritarianism” policy; and second, to investigate how datafication, which is a method to legitimize data collection, and dataveillance, which is continuous surveillance through the use of data, offer the Chinese state a legitimate method of monitoring, surveilling and controlling citizens, businesses and society. Taken together, China’s social credit system is analyzed as an integrated tool for datafication, dataveillance and data-driven authoritarianism.

This study combines the personal narratives of 22 Chinese citizens with policy analyses, online discussions and media reports. The stories were collected using a scenario-based story completion method to understand the participants’ perceptions of the recently introduced social credit system in China.

China’s new social credit system, which turns both online and offline behaviors into a credit score through smartphone apps, creates a “new normal” way of life for Chinese citizens. This data-driven authoritarianism uses data and technology to enhance citizen surveillance. Interactions between individuals, technologies and information emerge from understanding the system as one that provides social goods, using technologies, and raising concerns of privacy, security and collectivity. An integrated critical perspective that incorporates the concepts of datafication and dataveillance enhances a general understanding of how data-driven authoritarianism develops through the social credit system.

This study builds upon an ongoing debate and an emerging body of literature on datafication, dataveillance and digital sociology while filling empirical gaps in the study of the global South. The Chinese social credit system has growing recognition and importance as both a governing tool and a part of everyday datafication and dataveillance processes. Thus, these phenomena necessitate discussion of its consequences for, and applications by, the Chinese state and businesses, as well as affected individuals’ efforts to adapt to the system.

The construction industry is an industry with a high incidence of safety accidents, and the interactions of unsafe behaviors of construction workers are the main cause of accidents. The neglect of the interactions may lead to serious underestimation of safety risks. This research aims to analyze the cascading vulnerability of unsafe behaviors of construction workers from the perspective of network modeling.

An unsafe behavior network of construction workers and a cascading vulnerability analysis model were established based on 296 actual accident cases. The cascading vulnerability of each unsafe behavior was analyzed based on the degree attack strategy.

Complex network with 85 unsafe behavior nodes is established based on the collected accidents in total. The results showed that storing in improper location, does not wear a safety helmet, working with illness and working after drinking are unsafe behaviors with high cascading vulnerability. Coupling analysis revealed that differentiated management strategies of unsafe behaviors should be applied. Besides, more focus should be put on high cascading vulnerability behaviors.

This research proposed a method to construct the cascading failure model of unsafe behavior for individual construction workers. The key parameters of the cascading failure model of unsafe behaviors of construction workers were determined, which could provide a reference for the research of cascading failure of unsafe behaviors. Additionally, a dynamic vulnerability research framework based on complex network theory was proposed to analyze the cascading vulnerability of unsafe behaviors. The research synthesized the results of dynamic and static analysis and found the key control nodes to systematically control unsafe construction behaviors.