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Linear projects often involve lengthy construction periods, necessitating dynamic adjustments to the plan. Completely rescheduling remaining activities every time can lead to unnecessary time and cost wastage and significant deviations in resource supply. To address these issues, this paper proposes a dynamic scheduling method designed to effectively manage both time and cost during construction projects.

Determining the rescheduling frequency through a hybrid driving strategy and buffer mechanism, introducing rolling window technology to determine the scope of local rescheduling and constructing a local rescheduling model under the constraints of time and cost deviation with the objective of minimizing the cost. Combined decision-making for construction and rushing modes constrained by multiple construction scenarios. Opposite learning is introduced to optimize the hybrid algorithm solution.

Arithmetic examples and cases confirm the model’s feasibility and applicability. The results indicate that (1) continuous rescheduling throughout project construction is essential and effective and (2) a well-structured buffer mechanism can prevent redundant rescheduling and enhance overall control of cost and schedule deviations.

This study introduces an innovative dynamic scheduling framework for linear engineering, offering a method for effectively controlling schedule deviations during construction. The developed model enhances rescheduling efficiency and introduces a combined quantization strategy to increase the model’s applicability to linear engineering. This model emerges as a promising decision support tool, facilitating the implementation of sustainable construction scheduling practices.

This paper aims to propose a new ultrasonic detection method for stainless steel weld defects based on complex synergetic convolutional calculation to solve two problems in the ultrasonic detection of austenitic stainless steel weld defects. These include ignoring the nonlinear information of the imaginary part in the complex domain of the signal and the correlation information between the amplitude of the real part and phase of the imaginary part and subjective dependence of diagnosis model parameters.

An ultrasonic detection method for weld defects based on complex synergetic convolution calculation is proposed in this paper to address the above issues. By mapping low-density, 1D detection samples to a complex domain space with high representation richness, a multi-scale and multilevel complex synergetic convolution calculation model (CSCC) is designed to match and transform samples to mine amplitude changes, phase shifts, weak phase angle changes and amplitude-phase correlation features deeply from the detection signal. This study proposed an Elite Sine-Cosine Sobol Sampling Dung Beetle Optimization Algorithm, and the detection model CSCC achieves global adaptive matching of key hyperparameters in 19 dimensions. Finally, a regulative complex synergetic convolutional calculation model is constructed to achieve high-performance detection of weld defects.

Through experiments on a self-developed Taiyuan intelligent detection and information processing weld defect dataset, the results show that the method achieves a detection accuracy of 92% for five types of weld defects: cracks, porosity, slag inclusion and unfused and unwelded components, which represent an average improvement of 11% relative to comparable models. This method is also validated with the PhysioNet electrocardiogram public dataset, which achieves better detection performance relative to the other models.

This method provides a theoretical basis and technical reference for developing and applying intelligent, efficient and accurate ultrasonic defects detection technology.

The marine environment presents a great challenge to the anticorrosion properties of organic coatings applied on equipment. Since the compactness of coatings is critical in marine environments, a novel nepheline-epoxy resin (N-EP) composite was introduced into organic coatings to improve the interfacial compatibility between the pigments and the binder. The purpose of this study is to evaluate the effectiveness of the N-EP composite in enhancing the corrosion resistance of the coatings in marine conditions.

These composite particles were prepared via the mechanical ball milling method at thermofield-assisted, leading to chemical bonding between inorganic nepheline and epoxy resin, the agglomeration of particles was avoided by this method. Fourier transform infrared spectroscopy, transmission electron microscope, particle size distribution, sedimentation and thermogravimetric-differential thermal analysis were used to verify the feasibility of thermal field-assisted mechanochemistry for achieving a direct reaction between epoxy resin and nepheline powder, as well as to determine the optimal reaction conditions. Additionally, water absorption tests, Electrochemical impedance spectroscopy and scanning electron microscope were conducted to assess the anticorrosive properties of the modified nepheline coatings.

The results further indicated that N-EP improved the barrier performance and mechanical properties of the coating. For example, after modified, the tensile strength of coating had increased from 41.96 ± 0.05–63.14 ± 0.05 MPa. This can be attributed to the less defective N-EP/binder interface and the uniform dispersion of N-EP in the coating. The optimal preparation conditions (500 r/min of ball grinding speed and 6 h of ball grinding time) for the composites were also studied for a superior corrosion resistance of the coating.

Thermofield-assisted mechanochemistry enables direct reactions between epoxy resin and nepheline powder, enhancing the dispersion stability and interfacial compatibility of N-EP. This modification improves coating compactness, reduces porosity and enhances corrosion resistance by strengthening the labyrinth effect on water diffusion.

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.

This paper aims to develop an optimal maintenance and spare parts policy for an urban micro wind power system, focusing on two urban micro wind farms (UMWF). The reliability and efficiency of these systems are sought to be enhanced by considering the relationship between urban wind parameters and wind turbine degradation.

A proportional hazards (PH) model is utilized to describe how urban wind conditions impact turbine degradation. The maintenance strategy includes preventive maintenance (PM), corrective maintenance (CM) and opportunistic maintenance (OM). A multi-objective optimization algorithm is developed to optimize the joint policy of OM plans and spare parts resource allocation.

The proposed maintenance and spare parts policy effectively balances the trade-offs between PM, CM and OM strategies. Numerical experiments demonstrate that the policy improves the reliability of UMWF, reducing downtime and maintenance costs while ensuring the availability of spare parts when needed. The results show a significant enhancement in system performance compared to traditional maintenance approaches.

A novel maintenance policy and spare parts management approach for urban micro wind power systems is proposed. A multi-objective optimization algorithm is developed to optimize the OM schedule and maintenance spare parts resource management strategy for wind farms in urban wind environments.

This study aims to examine the relationship between various types of literacy on one hand and identification of misinformation and dissemination of such information on the other, in search for better countermeasures against misinformation.

Based on data from a large-scale survey, models are constructed and analyzed to assess the relationships of literacy with both the identification of inaccuracies and dissemination behavior.

Regarding the identification of misinformation, individuals with high critical thinking attitudes (subjective literacy) are less likely to recognize misinformation, while other objective literacies do not have a significant relationship. Regarding dissemination behavior, individuals with high information literacy, media literacy and critical thinking scores tend not to disseminate misinformation, whereas those with high critical thinking attitudes are more likely to disseminate such information.

First, it quantitatively elucidates the relationships various literacies have with the accuracy judgment and dissemination behavior of misinformation. This highlights the effectiveness of objective indicators of literacies and the need for caution regarding subjective literacy – i.e. self-confidence in their own literacy. Second, it provides a cross-disciplinary analysis of the relationships, covering not only oft-studied politics and health care but also various other fields, thereby identifying comprehensive literacy strategies against misinformation. Third, it addresses differences in dissemination methods and offers insights into more practical countermeasures.

This study aims to investigate the temperature rise characteristics of vibrating rolling bearings under the influence of the polarization force of unbalanced eccentric blocks. A thermal-fluid-solid mechanics coupled finite element model is established to analyze the effects of different loads and rotational speeds on bearing temperature to prevent overheating, wear and thermal damage.

A thermal-fluid-solid mechanics coupled finite element model of the vibrating rolling bearing is developed based on the principles of heat transfer. Finite element analysis software is used to conduct numerical simulations and study the temperature distribution of the bearing system under different loads and speeds. The model’s accuracy is verified by experimentally measuring the actual temperature of the bearing under the same working conditions.

This study successfully established a thermal-fluid-solid mechanics coupled finite element model of a vibrating rolling bearing, verifying its accuracy and reliability. The research results provide an essential reference for optimizing bearing design, preventing overheating and extending service life.

By analyzing the temperature rise characteristics under various load and rotational speed conditions, the law governing the internal temperature distribution of bearings is revealed. This finding offers a theoretical foundation for comprehending the thermal behavior of bearings.

This study offers a scientific foundation for the maintenance and fault diagnosis of shaker rolling bearings, aiding in the timely identification and resolution of thermal damage issues. Through the optimization of bearing design and usage conditions, the equipment’s lifespan can be prolonged, maintenance expenses can be minimized and production efficiency can be enhanced.

A thermal-fluid-solid mechanics coupled finite element model of a vibrating rolling bearing was established, considering the interaction of multiple physical fields. The influence of the polarization force from the unbalanced eccentric block on the bearing temperature is analyzed in detail, which is close to the actual working conditions.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0396/

Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.

Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.

Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.

Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.

This study aims to explore the risk spillover effects among different sectors of the Chinese stock market after the outbreak of COVID-19 from both Internet sentiment and price fluctuations.

The authors develop four indicators used for risk contagion analysis, including Internet investors and news sentiments constructed by the FinBERT model, together with realized and jump volatilities yielded by high-frequency data. The authors also apply the time-varying parameter vector autoregressive (TVP-VAR) model-based and the tail-based connectedness framework to investigate the interdependence of tail risk during catastrophic events.

The empirical analysis provides meaningful results related to the COVID-19 pandemic, stock market conditions and tail behavior. The results show that after the outbreak of COVID-19, the connectivity between risk spillovers in China's stock market has grown, indicating the increased instability of the connected system and enhanced connectivity in the tail. The changes in network structure during COVID-19 pandemic are not only reflected by the increased spillover connectivity but also by the closer relationships between some industries. The authors also found that major public events could significantly impact total connectedness. In addition, spillovers and network structures vary with market conditions and tend to exhibit a highly connected network structure during extreme market status.

The results confirm the connectivity between sentiments and volatilities spillovers in China's stock market, especially in the tails. The conclusion further expands the practical application and theoretical framework of behavioral finance and also lays a theoretical basis for investors to focus on the practical application of volatility prediction and risk management across stock sectors.