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The purpose of this paper is to introduce a novel method to study the flutter coupling mechanism of the twin-fuselage aircraft, which is becoming a popular transportation vehicle recently.

A new method of flutter mode indicator is proposed based on the principle of work and power, which is realized through energy accumulation of generalized force work on generalized coordinates, based on which flutter coupling mechanism of the twin-fuselage aircraft is studied using ground vibration test and computational fluid dynamics/computational solid dynamics method.

Verification of the proposed flutter mode indicator is provided, by which the flutter mechanism of the twin fuselage is found as the horizontal tail’s torsion coupled with its bending effect and the “frequency drifting” phenomenon of twin-fuselage aircraft is explained logically, highlighting the proposed method in this paper.

This paper proposed a new method of flutter mode indicator, which has advantages in flutter modes indexes reliability, clear physical meaning and results normalization. This study found the flutter coupling mechanism of twin-fuselage aircraft, which has important guiding significance to the development of twin-fuselage aircraft.

Carbon reduction in the construction supply chain can critically affect the construction industry’s transition to an environmentally sustainable one. However, implementing carbon reduction in all parties is restricted because of the poor understanding of the drivers influencing the low-carbon construction supply chain (LCCSC). The purpose of this paper is to systematically identify the drivers of LCCSC, analyze their causality, and prioritize the importance of their management.

A decision-making analysis process was developed using an integrated decision-making trial and evaluation laboratory (DEMATEL)–analytical network process (ANP). First, the hierarchical drivers of the LCCSC were identified through a literature review. The DEMATEL method was subsequently applied to analyze the interactions between the drivers, including the direction and strength of impact. Finally, the ANP analysis was used to obtain the drivers’ weights; consequently, their priorities were established.

Various factors with complex interactions drive LCCSC. With respect to their influence relationships, incentive policy, regulatory policy, consumers’ low-carbon preference, market competition, supply chain performance, and managers’ low-carbon awareness have more significant center degrees and are cause drivers. Their strong correlations and influence on other drivers should be noticed. In terms of weights in the driver system, regulatory policy, consumers’ low-carbon preference, supply chain performance, and incentive policy are the key drivers of LCCSC and require primary attention. Other drivers, such as supply chain collaboration, employee motivation, and public participation, play a minor driving role with less management priority.

Despite some contributing studies with localized perspectives, the systematic analysis of LCCSC drivers is limited, especially considering their intricate interactions. This paper establishes the LCCSC driver system, explores the influence relationships among the drivers, and determines the key drivers. Hence, it contributes to the sustainable construction supply chain domain by enabling decision-makers and practitioners to systematically understand the drivers of LCCSC and gain management implications on priority issues with limited resources.

The purpose of this paper is to improve the mechanical and tribological properties of Si₃N₄ ceramics and to make the application of Si₃N₄ ceramics as tribological materials more extensive.

Si₃N₄-based composite ceramics (SN-2L) containing nitrogen-doped graphene quantum dots (N-GQDs) were prepared by hot press sintering process through adding 2 Wt.% nanolignin as precursor to the Si₃N₄ matrix, and the dry friction and wear behaviors of Si₃N₄-based composite against TC4 disc were performed at the different loads by using pin-on-disc tester.

The friction coefficients and wear rates of SN-2L composite against TC4 were significantly lower than those of the single-phase Si₃N₄ against TC4 at the load range from 15 to 45 N. At higher load of 45 N, SN-2L/TC4 pair presented the lowest friction coefficient of 0.25, and the wear rates of the pins and discs were as low as 1.76 × 10⁻⁶ and 2.59 × 10⁻⁴mm³/N·m. The low friction and wear behavior could be attributed to the detachment of N-GQDs from the ceramic matrix to the worn surface at the load of 30 N or higher, and then an effective lubricating film containing N-GQDs, SiO₂, TiO₂ and Al₂SiO₅ formed in the worn surface. While, at the same test condition, the friction coefficient of the single-phase Si₃N₄ against TC4 was at a range from 0.45 to 0.58. The spalling and cracking morphology formed on the worn surface of single-phase Si3N4, and the wear mechanism was mainly dominated by adhesive and abrasive wear.

Overall, a high-performance green ceramic composite was prepared, and the composite had a good potential for application in engineering tribology fields (such as aerospace bearings).

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0161/

How to get a lighter and stronger anti-rolling torsion bar has become a barrier for the development of high-speed railway vehicles. The purpose of this paper is to realize the multi-objective optimization of an anti-rolling torsion bar with a Modified Non-dominated Sorting Genetic Algorithm III (MNSGA-III), which aims to obtain a better design scheme of an anti-rolling torsion bar device.

First, the Non-dominated Sorting Genetic Algorithm III (NSGA-III) uses a simulated binary crossover (SBX) operator and a polynomial mutation operator, while the MNSGA-III algorithm proposed in this paper introduces an arithmetic crossover and an adaptive mutation operator to change the crossover and mutate operator in NSGA-III. Second, two algorithms are tested by ZDT3, ZDT4 functions. Both algorithms set the same population size and evolutionary generation, and then compare the results of NSGA-III and MNSGA-III. Finally, MNSGA-III is applied to the multi-objective model of an anti-rolling torsion bar which is established by taking the mass and stiffness of the torsion bar as the optimization object. After that, it obtains the Pareto solution set by solving the multi-objective model with MNSGA-III. The only optimal solution selected from the Pareto solution set is compared with the traditional design scheme of an anti-rolling torsion bar.

The MNSGA-III converges faster than NSGA-III. Besides, MNSGA-III has better diversity of Pareto solutions than NSGA-III and is closer to the ideal Pareto frontier. Comparing with the results before the optimization, it shows that the volume of the anti-rolling torsion bar reduces by 1.6 percent and the stiffness increases by 3.3 percent. The optimized data verifies the effectiveness of this method proposed in this paper.

The simulated binary crossover operator and polynomial mutation operator of NSGA-III are changed into an arithmetic crossover operator and an adaptive mutation operator, respectively, which improves the optimization performance of the algorithm.

This paper aims to provide a review on the process of additive manufacturing of ceramic materials, focusing on partial and full melting of ceramic powder by a high-energy laser beam without the use of binders.

Selective laser sintering or melting (SLS/SLM) techniques are first introduced, followed by analysis of results from silica (SiO₂), zirconia (ZrO₂) and ceramic-reinforced metal matrix composites processed by direct laser sintering and melting.

At the current state of technology, it is still a challenge to fabricate dense ceramic components directly using SLS/SLM. Critical challenges encountered during direct laser melting of ceramic will be discussed, including deposition of ceramic powder layer, interaction between laser and powder particles, dynamic melting and consolidation mechanism of the process and the presence of residual stresses in ceramics processed via SLS/SLM.

Despite the challenges, SLS/SLM still has the potential in fabrication of ceramics. Additional research is needed to understand and establish the optimal interaction between the laser beam and ceramic powder bed for full density part fabrication. Looking into the future, other melting-based techniques for ceramic and composites are presented, along with their potential applications.

This paper aims to present a new kinematics mapping method based on dynamic equivalent points. In teleoperation, this method enables a robotic (follower) arm to mimic human (leader) arm postures and avoid obstacles in a human-like manner.

The information of the human arm is extracted based on the characteristics of human arm motion, and the concept of equivalent points is introduced. Then, an equivalent point is determined to transform the robotic arm with a nonhuman-like kinematic structure into an anthropomorphic robotic arm. Based on this equivalent point, a mapping method is developed to ensure that the two arms are similar. Finally, the similarity between the human elbow angle and robot elbow angle is further improved by using this method and an augmented Jacobian matrix with a compensation coefficient.

Numerical simulations and physical prototype experiments are conducted to verify the effectiveness and feasibility of the proposed method. In environments with obstacles, this method can adjust the position of the equivalent point in real time to avoid obstacles. In environments without obstacles, the similarity between the human elbow angle and robot elbow angle is further improved at the expense of the end-effector accuracy.

This study presents a new kinematics mapping method, which can realize the complete mapping between the human arm and heterogeneous robotic arm in teleoperation. This method is versatile and can be applied to various mechanical arms with different structures.

This chapter provides the foundation for the book. The objective of this chapter is to outline the theme of the book and to provide the context for the chapters that follow. Disaster recovery is a challenge for governments and for affected communities, families, and individuals. It is a challenge, because recovery from catastrophic disasters can be much more complicated and elusive than what can be addressed by national and international aid organizations given the time and other resources. The short literature review provides the research context, and the overview of the book describes each of the chapters briefly.

The purpose of this paper is to assesses whether supervisor justice is linked to COVID-19 negative and positive impacts directly and indirectly through the mechanisms of stress and resiliency among auxiliary police in China.

This study utilized survey data from more than 300 auxiliary police in a large Chinese provincial capital city in 2020. Structural equation modeling was conducted to analyze the direct and indirect relationships between supervisor justice and COIVD-19 impacts.

Results indicate that supervisor justice connects to COVID-19 negative impacts indirectly through stress. Supervisor justice is also indirectly related to positive impact through resiliency.

The findings' generalizability is limited due to using a nonrandom sample of officers. Officers' emotional states in the forms of stress and resiliency are important in mediating the association between supervisory justice and COVID-19 impacts.

The present study represents one of the first attempts to empirically investigate the occupational experiences of a vital group of frontline workers in Chinese policing. This study also generates evidence to support the importance of officers' emotional conditions in reducing negative COVID-19 impacts in an authoritarian country.

Electrical capacitance tomography (ECT) and electrical resistance tomography (ERT) are promising techniques for multiphase flow measurement due to their high speed, low cost, non-invasive and visualization features. There are two major difficulties in image reconstruction for ECT and ERT: the “soft-field”effect, and the ill-posedness of the inverse problem, which includes two problems: under-determined problem and the solution is not stable, i.e. is very sensitive to measurement errors and noise. This paper aims to summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide reference for further research and application.

In the past 10 years, various image reconstruction algorithms have been developed to deal with these problems, including in the field of industrial multi-phase flow measurement and biological medical diagnosis.

This paper reviews existing image reconstruction algorithms and the new algorithms proposed by the authors for electrical capacitance tomography and electrical resistance tomography in multi-phase flow measurement and biological medical diagnosis.

The authors systematically summarize and evaluate various reconstruction algorithms which have been studied and developed in the word for many years and to provide valuable reference for practical applications.

Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed.

The MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame.

The prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie.

The MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.