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This paper studies the location-inventory problem (LIP) in pallet pooling systems to improve resource utilization and save logistics costs, which is a new extension of the classical LIP and also an application of the LIP in pallet pooling systems.

A mixed-integer linear programming is established, considering the location problem of pallet pooling centers (PPCs) with multi-level capacity, multi-period inventory management and bi-directional logistics. Owing to the computational complexity of the problem, a hybrid genetic algorithm (GA) is then proposed, where three local searching strategies are designed to improve the problem-solving efficiency. Lastly, numerical experiments are carried out to validate the feasibility of the established model and the efficiency of the proposed algorithm.

The results of numerical experiments show that (1) the proposed model can obtain the integrated optimal solution of the location problem and inventory management, which is better than the two-stage model and the model with single-level capacity; (2) the total cost and network structure are sensitive to the number of PPCs, the unit inventory cost, the proportion of repairable pallets and the fixed transportation cost and (3) the proposed hybrid GA shows good performance in terms of solution quality and computational time.

The established model extends the classical LIP by considering more practical factors, and the proposed algorithm provides support for solving large-scale problems. In addition, this study can also offer valuable decision support for managers in pallet pooling systems.

The purpose of this study is to evaluate the effect of graphene, basalt flakes and their synergy on the corrosion resistance of zinc-rich coatings. As the important heavy-duty anticorrosion coatings, zinc-rich coatings provided cathodic protection for the substrate. However, to ensure cathodic protection, a large number of zinc powder made the penetration resistance known as the weakness of zinc-rich coatings. Therefore, graphene and basalt flakes were introduced into zinc-rich coatings to coordinate its cathodic protection and shielding performance.

Three kinds of coatings were prepared; they were graphene modified zinc-rich coatings, basalt flakes modified zinc-rich coatings and graphene-basalt flakes modified zinc-rich coatings. The anticorrosion behavior of painted steel was studied by using the electrochemical impedance spectroscopy (EIS) technique in chloride solutions. The equivalent circuit methods were used for EIS analysis to obtain the electrode process structure of the coated steel system. Simultaneously, the corrosion resistance of the three coatings was evaluated by water resistance test, salt water resistance test and salt spray test.

The study found that the addition of a small amount of graphene and basalt flakes significantly improved the anticorrosion performance of coatings by enhancing their shielding ability against corrosive media and increasing the resistance of the electrochemical reaction. The modified coatings exhibited higher water resistance, salt water resistance and salt spray resistance. The graphene-basalt flakes modified zinc-rich coatings demonstrated the best anticorrosion effect. The presence of basalt scales and graphene oxide in the coatings significantly reduced the water content and slowed down the water penetration rate in the coatings, thus prolonging the coating life and improving anticorrosion effects. The modification of zinc-rich coatings with graphene and basalt flakes improved the utilization rate of zinc powder and the shielding property of coatings against corrosive media, thus strengthening the protective effect on steel structures and prolonging the service life of anticorrosion coatings.

The significance of developing graphene-basalt flakes modified zinc-rich coatings lies in their potential to offer superior performance in corrosive environments, leading to prolonged service life of metallic structures, reduced maintenance costs and a safer working environment. Furthermore, such coatings can be used in various industrial applications, including bridges, pipelines and offshore structures, among others.

Construction safety resilience is gradually gaining attention in the field of engineering construction as a new management concept and way to improve safety performance. However, how to cope with the dilemma of the unclear relationship of construction safety resilience elements at the practice level and promote the harmonization of construction safety goals and resilience enhancement paths has become an urgent challenge for safe construction.

This study analyzes the components of construction safety resilience elements. A relationship network model of construction safety resilience elements is developed by using the social network analysis method. The location and influence of each element in the network and the interrelationships among the elements are explored in depth.

The findings reveal a robust interconnection among the elements of safety resilience in the construction industry. Key components such as safety behavior, risk prevention and control mechanisms, disaster prevention and mitigation technologies as well as information technology, are positioned at the core of the network. Notably, safety behavior exerts the most significant influence over the other elements, serving as the linchpin of safety management in the construction industry. Moreover, the interplay among safety resilience elements in the construction sector can alter the structure of the relationship network.

This study adopts the social network approach to solve the problem that it is difficult to quantitatively analyze the elements of construction safety resilience and their interrelationships and to clarify the interactions among the core elements, which can help to further assist the construction project manager to continuously optimize safety resilience and improve construction safety.

This paper contends that data generated by research on supervision are often taken as authentic data. Through an examination of studies that use audio/visual recordings to investigate supervision, the paper both promotes and problematises the recording of supervision meetings as a useful technique for doctoral supervision research. This paper aims to encourage a critical evaluation of methodological choices in research on supervision, and both promotes and problematises the practice of recording supervision meetings to enhance nuance in research on supervision practices.

This paper reviews how prior studies have adopted different research methods to construct the space of supervision, and how the chosen methods have been justified. The paper draws on data from an empirical study which included interviews with supervisors in China, based on recordings of their supervision meetings.

Presenting a single case with one participant to explore the recording and interview process in detail, this study demonstrates how hearing the supervision meeting can present a multi-faceted picture of supervision practice. This multi-faceted picture underpins the alternative understanding of authentic data that this study unpacks.

Drawing on the tradition of poststructuralist critiques of traditional research methodology, this study is presented as a methodological paper, with a core aim of interrogating and problematising methodological decisions taken in studies of doctoral supervision. This study reviews research methods that were used in prior studies on supervision, investigating how the chosen methods were justified and how these methods affect the resultant construction of supervision.

Inter-organizational collaboration is the organizational guarantee and key link to achieve the goals of megaproject management. Project governance has always played an important role in the construction of megaprojects, but the relationship between project governance and organizational collaboration is unclear. The purpose of this study is to explore the role paths of different project governance mechanisms in influencing the collaborative behaviors of stakeholders and collaborative performance and to elucidate the mechanism of project governance on inter-organizational collaboration.

A conceptual framework was developed based on a comprehensive literature review, termed the structural equation model (SEM). The hypotheses of the model were tested based on data obtained from a questionnaire survey of 235 experts with experience in megaprojects within the construction industry in China.

The results show that project governance positively contributes to the collaborative behavior of megaproject stakeholders and the collaborative performance of the project team. Collaborative behavior acts as a partial mediator between project governance and the collaborative performance of the megaproject inter-organization alliance. The complexity of the project modulates the relationship between the governance mechanism of the project and the collaborative behavior of the stakeholders, which affects the collaborative performance of the megaproject inter-organization alliance.

The findings provide theoretical and practical implications for promoting positive collaborative behavior among stakeholders in megaproject selection and improving the collaborative performance of megaproject inter-organization alliances.

Mechanoreception is crucial for robotic planning and control applications, and for robotic fingers, mechanoreception is generally obtained through tactile sensors. As a new type of robotic finger, the soft finger also requires mechanoreception, like contact force and object stiffness. Unlike rigid fingers, soft fingers have elastic structures, meaning there is a connection between force and deformation of the soft fingers. It allows soft fingers to achieve mechanoreception without using tactile sensors. This study aims to provide a mechanoreception sensing scheme of the soft finger without any tactile sensors.

This research uses bending sensors to measure the actual bending state under force and calculates the virtual bending state under assumed no-load conditions using pressure sensors and statics model. The difference between the virtual and actual finger states is the finger deformation under load, and its product with the finger stiffness can be used to calculate the contact force. There are distinctions between the virtual and actual finger state change rates in the pressing process. The difference caused by the stiffness of different objects is different, which can be used to identify the object stiffness.

Contact force perception can achieve a detection accuracy of 0.117 N root mean square error within the range of 0–6 N contact force. The contact object stiffness perception has a detection average deviation of about 15%, and the detection standard deviation is 10% for low-stiffness objects and 20% for high-stiffness objects. It performs better at detecting the stiffness of low-stiffness objects, which is consistent with the sensory ability of human fingers.

This paper proposes a universal mechanoreception method for soft fingers that only uses indispensable bending and pressure sensors without tactile sensors. It helps to reduce the hardware complexity of soft robots. Meanwhile, the soft finger no longer needs to deploy the tactile sensor at the fingertip, which can benefit the optimization design of the fingertip structure without considering the complex sensor installation. On the other hand, this approach is no longer confined to adding components needed. It can fully use the soft robot body’s physical elasticity to convert sensor signals. Essentially, It treats the soft actuators as soft sensors.

This paper aims to investigate the improvement of Sn58Bi solder properties by Ni nanoparticle to provide theoretical support in the field of electronic packaging.

In this study, nickel nanoparticles (Ni NPs) were doped into Sn58Bi solder as a reinforcing agent to prepare a composite solder. The wettability of composite solder, melting characteristics of solder alloy, microstructure of joints, mechanical properties and intermetallics growth at the interface were investigated.

Test results show that Ni had little substantial effect on the solders alloy melting temperature yet decreased the alloy undercooling. Notably, Ni enhanced solders wettability on Cu substrates. The spreading area increased by 14% at 0.6 wt.% Ni. The Ni refined the microstructure of Sn58Bi solder joints and inhibited the coarsening of the Bi-rich phase in the solder. At the same time, it also led to the production of free intermetallic compounds, increasing growth of the intermetallic compound (IMC) layer. However, upon surpassing 0.6 wt.% Ni, (Cu, Ni)₆Sn₅ accumulated and formed irregular clumps in the matrix. The grain size of the IMC layer gradually decreased as Ni was added. In shear tests, the fracture of joints occurred mainly inside the solder matrix. Sn58Bi-0.6Ni joints displayed a 40% rise in shear strength and a 25% rise in Vickers hardness. In addition, the appearance of dimples at the fracture indicated the improved flexibility of joints.

In this paper, Ni nanoparticle with different contents were added to Sn58Bi solder to prepare a composite solder, and the properties of the composite solder were investigated from different perspectives.

Silence is a commonly seen phenomenon at the workplace. However, little is known about the cause and effect of leader silence. Drawing on the affective events theory, we develop a moderated mediation model to examine the effect of subordinates’ creative deviance on leader's authoritative silence and test the moderating effect of subordinates’ political skills.

Our research adopts a novel bottom-up perspective to investigate the subordinates’ influence on leader silence. A two-wave survey study involving 196 corporate team leaders in China was employed.

We found that leader’s workplace anxiety mediated the relationship between subordinates' creative deviance and leader's authoritative silence and subordinates’ political skills moderated the mediating effect.

Our research contributes to the leader silence literature in three folds. First, we employ the AET framework to study leader silence from the emotional perspective. Second, this research adopts a bottom-up angle to reveal the influence of subordinates’ behavior on leader silence. Third, the political skills lens offers novel explanation of why the anxious emotions triggered by followers’ creative deviance vary among leaders.

The transition to green growth goals requires the concerted efforts of the whole society. Enterprises, as important players in the market, play a key role in promoting green and sustainable development. The rise of the concept of sustainable development has enabled more enterprises to disclose environmental, social and governance (ESG) information, and ESG behaviour is regarded as a positive strategic behaviour to implement the new development concept. This paper aims to explore the influence of ESG performance on enterprise green innovation.

This study applies a fixed effect model and the regulation effect of empirical analysis to explore the influence of ESG performance on enterprise green innovation. The object of investigation is 2014–2021 Shanghai and Shenzhen A-share listed companies.

The results of an empirical analysis outline the following conclusions: (1) ESG performance has a significant effect on enterprise green innovation, mainly by easing the pressure of the financing enterprise, fitting stakeholders’ environmental protection concept and obtaining employee organizational identity that influences enterprise green innovation. (2) Government regulation positively regulates the role of ESG performance in promoting the green innovation of enterprises. (3) Heterogeneity analysis found that the strengthening role of ESG performance on the green innovation of enterprises is stronger in green invention patents, state-owned enterprises and nonheavily polluting industries.

Despite the valuable findings, this study has a few limitations. Thus, it is necessary to extend the object of investigation by adding other Asian countries, which allows for comparison analysis and allocating best practices for promoting green innovation. Besides, innovation and ESG performance depend on the quality of institutions. In this case, the future study should incorporate the indicators that reveal the quality of institutions (corruption, transparency, digitalisation, voice, accountability, etc.).

According to the above conclusions, this paper proposes suggestions at the level of enterprises, government and investors. At the enterprise level, ESG responsibility should be strengthened, ESG information should be consciously disclosed and the quality of ESG disclosure should be improved. Government departments should play the role of supervisors, improve the construction of ESG information disclosure systems and promote the formation of ESG systems. At the social level, investors should improve the ESG information status and pay more attention to the ESG performance of enterprises.

This study fills the scientific gaps in the analysis impact of ESG performance on the green innovation of enterprises. This paper contributes to the theoretical landscape of ESG efficiency by developing approaches based on two empirical models: testing the impact of enterprise ESG performance on green innovation and testing whether government regulation plays a regulatory role in the relationship between ESG performance and green innovation. Besides, this study analysed the ESG performance and green innovation within the following categories: heavy and nonheavy polluter industries; state and nonstate-owned enterprise groups.

To ensure the stable operation of ironmaking process and the quality and output of sinter, the multi-objective optimization of sintering machine batching process was carried out.

The purpose of this study is to establish a multi-objective optimization model with iron taste content and batch cost as targets, constrained by field process requirements and sinter quality standards, and to propose an improved balance optimizer algorithm (LILCEO) based on a lens imaging anti-learning mechanism and a population redundancy error correction mechanism. In this method, the lens imaging inverse learning strategy is introduced to initialize the population, improve the population diversity in the early iteration period, avoid falling into local optimal in the late iteration period and improve the population redundancy error correction mechanism to accelerate the convergence rate in the early iteration period.

By selecting nine standard test functions of BT series for simulation experiments, and comparing with NSGA-?, MOEAD, EO, LMOCSO, NMPSO and other mainstream optimization algorithms, the experimental results verify the superior performance of the improved algorithm. The results show that the algorithm can effectively reduce the cost of sintering ingredients while ensuring the iron taste of sinter, which is of great significance for the comprehensive utilization and quality assurance of sinter iron ore resources.

An optimization model with dual objectives of TFe content and raw material cost was developed taking into account the chemical composition and quality indicators required by the blast furnace as well as factors such as raw material inventory and cost constraints. This model was used to adjust and optimize the sintering raw material ratio. Addressing the limitations of existing optimization algorithms for sintering raw materials including low convergence accuracy slow speed limited initial solution production and difficulty in practical application we proposed the LILCEO algorithm. Comparative tests with NSGA-III MOEAD EO LMOCSO and NMPSO algorithms demonstrated the superiority of the proposed algorithm. Practical applications showed that the proposed method effectively overcomes many limitations of the current manual raw material ratio model providing scientific and stable decision-making guidance for sintering production operations.