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Value creation in engineering projects has received considerable research attention. However, buyers’ expectations regarding project value do not always translate into the actions of project team members (actors). Drawing upon resource orchestration theory to explore the micro-foundations of project value creation (PVC), we investigate how suppliers organize project team members to create project value that meets buyers’ expectations.

We adopted two instructive engineering projects as comparative cases to analyze patterns in actor interactions in PVC.

The results reveal “social interaction-based value creation” and “structural interaction-based value creation,” which explain the PVC process from the collective-level buyer’s expectations and supplier’s value orientation, to individual-level actor interactions (mobilizing, connecting, and networking), knowledge orchestration activities (structuring, bundling, and leveraging), and finally the value creation process (identifying, configuring, and generating) in project teams to generate collective-level project benefits and values.

We propose a macro-approach to micro-foundations, with a focus on the buyer-expected PVC process on the supplier side. Furthermore, we explain how suppliers organize individual actions and orchestrate the individual knowledge through two patterns of individual-level actor interactions for collective-level PVC.

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.

This study aims to develop a novel self-powered monitoring system that uses radio frequency (RF) energy harvesting and ultra-low-power management technologies for real-time condition monitoring of switch rails.

The system is designed for integration within the jump wire holes of switch rails, ensuring structural integrity and aesthetic appeal. It features a highly efficient energy harvesting mechanism combined with optimized power management for wireless sensor nodes. An on-board antenna captures ambient RF energy, managed by high-efficiency circuits to ensure stable wireless sensor operation. An ultra-low-power system-on-chip is used to acquire and transmit multimodal data on vibration and temperature from the switch rails. The data collection is enhanced through a two-threshold approach, adapting to harvested energy levels for self-energy balancing.

Testing revealed that the energy harvesting subsystem operated stably at distances up to 2.9 m from the RF source, charging a 200 µF capacitor to 4.2 V in just 220 s. The monitoring subsystem’s average power consumption is in the low microwatt range. Continuous operation over 30 days in real conditions resulted in only a 5 mV reduction in battery voltage, indicating successful self-powered operation and validating long-term reliability in unattended scenarios.

This research presents an innovative solution, integrating RF energy harvesting with ultra-low-power technology, which addresses the power and stability challenges faced by traditional monitoring systems.

The aim was not to perform a systematic review but firstly to search in PubMed, Science Direct, Scopus and Web of Science databases on the papers published in the last five years using tools for reviewing the statement of preferred information item for systematic reviews without focusing on a randomized analysis and secondly to perform a bibliometric analysis on the properties of films and coatings added of tocopherol for food packaging.

On January 24, 2022, information was sought on the properties of films and coatings added of tocopherol for use as food packaging published in PubMed, Science Direct, Scopus and Web of Science databases. Further analysis was performed using bibliometric indicators with the VOSviewer tool.

The searches returned 33 studies concerning the properties of films and coatings added of tocopherol for food packaging, which were analyzed together for a better understanding of the results. Data analysis using the VOSviewer tool allowed a better visualization and exploration of these words and the development of maps that showed the main links between the publications.

In the area of food science and technology, the development of polymers capable of promoting the extension of the shelf life of food products is sought, so the knowledge of the properties is vital for this research area since combining a biodegradable polymeric material with a natural antioxidant active is of great interest for modern society since they associate environmental preservation with food preservation.

Linear addition is commonly used to generate ensemble forecasts for decomposition ensemble models but traditionally treats individual modes with equal weights for simplicity. Using Taiwan air passenger flow as an empirical case, this study examines whether incorporating weighting for individual single-mode forecasts assessed by grey relational analysis into linear addition can improve the accuracy of the decomposition ensemble models used to forecast air passenger demand.

Data series are decomposed into several single modes by empirical mode decomposition, and then different artificial intelligence methods are applied to individually forecast these decomposed modes. By incorporating the correlation between each forecasted mode series and the original time series into linear addition for ensemble learning, a genetic algorithm is applied to optimally synthesize individual single-mode forecasts to obtain the ensemble forecasts.

The empirical results in terms of level and directional forecasting accuracy showed that the proposed decomposition ensemble models with linear addition using grey relational analysis improved the forecasting accuracy of air passenger demand for different forecasting horizons.

Accurately forecasting air passenger demand is beneficial for both policymakers and practitioners in the aviation industry when making operational plans.

In light of the significance of improving the accuracy of decomposition ensemble models for forecasting air passenger demand, this research contributes to the development of a weighting scheme using grey relational analysis to generate ensemble forecasts.

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.

The rural revitalization is a significant indicator of social development in China. Despite the crucial role that local governments play in coordinating urban and rural development, rural areas continue to face limited investment opportunities and scholarship on the impact of local government competition on urban–rural income disparity is scant, especially lacking empirical research from the spatial perspective. The authors contribute to closing the knowledge gap by exploring the spatial spillover effect of local government competition using the sample of China.

This study evaluated the urban–rural income inequality using the Theil index and constructed the spatial Durbin model to analyze the data by the method of maximum likelihood estimation.

The findings from the Theil index showed that the income gap is significant between city and countryside in China, with being wider in the western region compared to the eastern region. The evidence from the spatial Durbin model revealed that, in the same province, local government competition widens this gap, while competition from neighboring regions prompts a reduction in this gap in this province.

The authors recommended some policy implications, such as supporting the development in underdeveloped regions and improving the promotion evaluation criteria for local officials.

This article is original in exploring the spatial effects of local government competition on the urban–rural income gap. In addition, instead of using a single proxy to measure the urban–rural income gap, in the current paper, the Theil index for evaluating the disparity is incorporated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2023-0582

This paper examines the evolving landscape of accounting and finance research on the Chinese capital market, building on a previous study published at Abacus in 2018.

By incorporating data from 1999 to 2023, our analysis offers a detailed examination of shifts in academic focus, methodological advancements and thematic expansions over the last quarter-century.

The study reveals a substantial increase in accounting and finance publications related to the Chinese capital market in both Tier 1 and Asia-Pacific journals. The dynamic growth of the Chinese capital market during this period reflects profound economic transformations, characterized by technological innovations, sustainability commitments and regulatory reforms.

We conclude that the globally important Chinese capital market has attracted increasing academic attention, significantly advancing the understanding of accounting and finance research in China’s capital market.