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Damage of engineering structures is a nonlinear evolutionary process that spans across both material and structural levels, from mesoscale to macroscale. This paper aims to provide a comprehensive review of damage analysis methods at both the material and structural levels.

This study provides an overview of multiscale damage analysis of engineering structures, including its definition and significance. Current status of damage analysis at both material and structural levels is investigated, by reviewing damage models and prediction methods from single-scale to multiscale perspectives. The discussion of prediction methods includes both model-based simulation approaches and data-driven techniques, emphasizing their roles and applications. Finally, summarize the main findings and discuss potential future research directions in this field.

In the material level, damage research primarily focuses on the degradation of material properties at the macroscale using continuum damage mechanics (CDM). In contrast, at the mesoscale, damage research involves analyzing material behavior in the meso-structural domain, focusing on defects like microcracks and void growth. In structural-level damage analysis, the macroscale is typically divided into component and structural scales. The component scale examines damage progression in individual structural elements, such as beams and columns, often using detailed finite element or mesoscale models. The structural scale evaluates the global behavior of the entire structure, typically using simplified models like beam or shell elements.

To achieve realistic simulations, it is essential to include as many mesoscale details as possible. However, this results in significant computational demands. To balance accuracy and efficiency, multiscale methods are employed. These methods are categorized into hierarchical approaches, where different scales are processed sequentially, and concurrent approaches, where multiple scales are solved simultaneously to capture complex interactions across scales.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

This paper aims to study the influence of different seal structure parameters and working conditions on the air-oil two-phase flow characteristics and leakage characteristics of the seal cavity in the bearing cavity of the aero-engine spindle bearing tester.

In this paper, the VOF method and RNG k-ε turbulence model are used to explore the flow characteristics and leakage characteristics of the labyrinth seal cavity of an aero-engine spindle bearing tester under the condition of air-oil two-phase flow.

The distribution of the lubricating oil is related to the sealing clearance and the air-oil ratio. The amount of oil leakage increases with increasing of sealing chamber clearance, air-oil ratio and inlet velocity and decreases with increasing curvature and speed. The amount of air leakage increases with sealing clearance and inlet velocity.

In comparison to the pure air-phase flow field, the air-oil two-phase flow field can more accurately simulate the lubricating oil flow in the sealing chamber.

Opportunistic and delayed maintenances are increasingly becoming important strategies for sustainable maintenance practices since they increase the lifetime of complex systems like aircrafts and heavy equipment. The objective of the current study is to quantify the optimal time window for adopting these strategies.

The current study considers the trade-offs between different costs involved in the opportunistic and delayed maintenances (of equipment) like the fixed cost of scheduled maintenances, the opportunistic rewards that may be earned and the cost of premature parts replacement. The probability of the opportunistic maintenance has been quantified under two different scenarios – Mission Reliability and Renewal Process. In the case of delayed maintenance, the cost of the delayed maintenance is also considered. The study uses optimization techniques to find the optimal maintenance time windows and also derive useful insights.

Apart from finding the optimal time window for the maintenance activities the study also shows that opportunistic maintenance is beneficial provided the opportunistic reward is significantly large; the cost of conducting scheduled maintenance in the pre-determined slot is significantly large. Similarly, the opportunistic maintenance may not be beneficial if the pre-mature equipment parts replacement cost is significantly high. The optimal opportunistic maintenance time is increasing function of Weibull failure rate parameter “beta” and decreasing function of Weibull failure rate parameter “theta.” In the case of optimal delayed maintenance time, these relationships reverse.

To the best of our knowledge, very few studies exist that have used mission reliability to study opportunistic maintenance or considered the different cost trade-offs comprehensively.

Drawing from the conservation of resources theory and organisational justice theory, this study examined the direct and indirect (via career resilience) effects of organisational career growth on work engagement. We further examined the boundary condition of procedure justice in the relationship between organisational career growth, career resilience and work engagement.

We employed time-lagged data collected in three waves from 431 employees working in the public health sector. Hierarchical regression analysis and Hayes Process Macro were used to test the study hypotheses.

The findings revealed that organisational career growth has a significant positive direct and indirect influence on work engagement in the presence of career resilience. Also, the boundary condition of procedural justice was significant for employees who perceived procedural justice to be high as opposed to low.

Organisational career growth has a positive influence on employee work engagement. Therefore, public health organisations need to prioritise employee career growth by creating an enabling environment that will help employees' career prospects and mitigate employees’ perception of low procedural justice.

The originality of this study is in empirically establishing career resilience as an underlying mechanism in the relationship between organisational career growth and work engagement while considering the interactive effect of procedural justice. Additionally, the originality of this paper is demonstrated by empirically establishing that a perceived high level of procedural justice helps healthcare employees improve their work engagement, thus deepening our understanding of work engagement amongst health professionals.

This paper aims to present the design of a micro-electro-mechanical system (MEMS)-based three-dimensional combined vector hydrophone tailored for unmanned underwater vehicles (UUVs). The proposed design addresses the left-right ambiguity inherent in conventional MEMS hydrophones and enhances acoustic sensing capabilities to support improved UUV performance in underwater environments.

A novel MEMS-based three-dimensional vector hydrophone (M3DH) is proposed, integrating a highly sensitive MEMS chip with a piezoelectric ceramic-based scalar channel. A theoretical model of the hydrophone’s packaging was developed, and its acoustic performance was analyzed through COMSOL Multiphysics 6.2 simulations. Experimental validation of the hydrophone’s sensitivity and directional characteristics was conducted in a standing wave tank.

The MEMS-based three-dimensional combined hydrophone (M3DH) achieved a triaxial vector channel sensitivity of −175.6 dB at 800 Hz (re 1 V/µPa) and a scalar channel sensitivity of −186.3 dB (0 dB = 1 V/µPa). In addition, at 500 Hz, the vector channel exhibited a distinct “8”-shaped directivity pattern, whereas the scalar channel maintained a circular omnidirectional response. The hydrophone demonstrated excellent acoustic performance in three-dimensional space, effectively providing comprehensive acoustic information for small underwater platforms.

This research addresses the left-right ambiguity issue in MEMS hydrophones by presenting an MEMS-based three-dimensional combined hydrophone designed for integration into UUVs, offering an innovative solution to enhance underwater acoustic sensing capabilities in small platforms.

This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

This study aims to contribute to the existing institutional literature by examining the impact of regional social trust on foreign firm performance.

In this study, 10,286 annual observations of foreign firms in China from 2008 to 2020 are used to test the research hypotheses using an ordinary least square regression model.

Social trust can improve foreign firm performance by reducing operating costs and increasing risk-taking capacity, while formal institutions and economic policy uncertainty strengthen the positive relationship between social trust and foreign firm performance. Moreover, the results of the heterogeneity test show that the positive relationship is only manifested in the samples from the eastern region and is more pronounced in the samples of small- and medium-sized foreign firms and non-manufacturing foreign firms.

This study makes a theoretical contribution to the literature on institutional theory and foreign firm performance, as well as providing practical guidance to foreign firms on how to improve their performance.

This study aims to investigate the effect of top management team (TMT) stability on outward foreign direct investment (OFDI) of Chinese firms and the moderating effects of state ownership and managerial ownership on this relationship.

An empirical analysis based on the ordinary least square regression model is conducted using Chinese A-share listed firms that engaged in OFDI from 2008 to 2021.

TMT stability has a positive effect on firms’ OFDI. Moreover, state ownership significantly strengthens the positive relationship between TMT stability and OFDI, while managerial ownership weakens this positive relationship.

The findings help firms to effectively retain TMT talents and promote the smooth internationalization of firms, thereby enhancing their long-term development capabilities and competitive advantages.

This study expands the investigation of the factors influencing OFDI at the micro level of the TMT, providing valuable decision-making insights for firms.