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In the realm of open innovation (OI) networks, coopetition—where competition and cooperation coexist—plays a pivotal role in shaping the dynamics between diverse projects. This dual relationship is crucial for the propagation of knowledge and the bolstering of the network's overall resilience. While competition drives the quality of products and services, thereby reinforcing network resilience, cooperation facilitates knowledge diffusion, which is essential for the network's robustness.

We delve into the interplay between coopetition intensity and network resilience through the lens of knowledge diffusion. Our methodology begins with a sensitivity analysis to gauge the direct effects of coopetition on resilience. This is followed by a principal component analysis to identify the key determinants of coopetition intensity among projects. Finally, we utilize linear regression and moderation analysis to explore the mediating role of knowledge diffusion in the resilience of OI networks.

Our work is grounded in network theory, which provides a robust theoretical framework for understanding project coopetition and knowledge diffusion within the OI paradigm. This research not only offers a nuanced understanding of coopetition's impact on OI network resilience but also highlights the significance of knowledge diffusion as a critical mediating variable.

1) Identifies the significant influences in project coopetition (competition and cooperation). (2) Puts the conceptual framework and calculation method of the open innovation network resilience based on the project coopetition and knowledge diffusion. (3) Explores the moderating role of knowledge diffusion in project coopetition influencing open innovation networks resilience. (4) Measures the influence of project coopetition relationship on open innovation network resilience from the perspective of knowledge diffusion. (5) Encourages project management to consider the portfolios of coopetition.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

Megaproject supply chains involve multiple layers of stakeholders, leading to complex relationships and risks. The role of social interactions within these networks is unexplored. Therefore, an analysis of construction supply chain risk management from the perspective of social networks is essential to identify related stakeholders, their relationships and the social network risk factors.

About 65 risk factors, identified from literature and interviews, informed the development of a questionnaire for the study. Online questionnaires administered in Ghana and South Africa produced 120 valid responses. Feedback from the responses was ranked and assessed to determine the overall social network risk levels using the Normalised Mean and Fuzzy synthesis analysis methods.

About 24 risk factors were identified and classified into six groups: Client/Consultant-related, Community-related, Government-related, Industry Perception-related, Supplier-related and Stakeholder Opportunism. The top five social network risks identified include bribery, supplier monopoly, incomplete design teams, poor communication and lack of collaboration.

The study provides detailed evaluations of social network risks in Africa, and the findings will help in developing strategies to mitigate supply chain disruptions caused by these challenges.

This study contributes to the literature on supply chain risk management by offering context-specific insights into the social network perspective of megaprojects in Africa, which differs from those in developed countries.

The poor capacity of prefabricated construction cost estimation is the essential reason for the low profitability of the general contractor. Therefore, this study aims to focus on the cost estimation of prefabricated construction as the research object. This research aims to enhance the accuracy of total project cost estimation for general contractors, ultimately leading to improved profitability.

This study used Vensim PLE software to establish a system dynamics model. In the modeling process, a systematic research review was used to identify cost-influencing factors; ABC classification and the analytic hierarchy process were used to score and determine the weights of influencing factors.

The total cost error obtained by the model is less than 2% compared with the actual value. It can be used to cost estimation and analysis. The analysis results indicate that there are 7 key factors, among which the prefabrication rate has the most significant impact. Furthermore, the model can provide the extreme range cost; the minimum cost can reduce by 13% from the value in the case. The factor's value can compose a cost control strategy for general contractors.

The cost of prefabricated buildings can be estimated well, and deciding the prefabrication rate is crucial. The cost can be declined by correct cost control strategies when bidding and subcontracting are in process. The strategies can follow the direction of the model.

A systemic, quantitative and qualitative analysis of cost estimation of prefabricated buildings for general contractors has been conducted. A mathematical model has been developed and validated to facilitate more effective cost-control measures.

Additive manufacturing became the most popular method as it enables the production of light-weight and high-density parts in effective way. Selective laser melting (SLM) is preferred by means of producing a component with good surface quality and near-net shape even if it has complex form. Titanium alloys have been extensively used in engineering covering a variety of sectors such as aeronautical, chemical, automotive and defense industry with its unique material properties. Therefore, the purpose of this review is to study the tribological behavior and surface integrity that reflects the thermal and mechanical performances of the fabricated parts.

This paper is focused on the tribological and surface integrity aspects of SLM-produced titanium alloy components. It is aimed to outline the effect of SLM process parameters on tribology and surface integrity first. Then, thermal, thermal heat, thermomechanical and postprocessing surface treatments such as peening, surface modification and coatings are highlighted in the light of literature review.

This work studied the effects of particle characteristics (e.g. size, shape, distributions, flowability and morphology) on tribological performance according to an extensive literature survey.

This study addresses this blind spot in existing industrial-academic knowledge and goals to determine the impact of SLM process parameters, posttreatments (especially peening operations) and particle characteristics on the SLMed Ti-based alloys, which are increasingly used in biomedical applications as well as other many applications ranging from automobile, aero, aviation, maritime, etc. This review paper is created with the intention of providing deep investigation on the important material characteristics of titanium alloy-based components, which can be useful for the several engineering sectors.

To improve the surface quality of Mg₂Si/Al composites after solution treatment, the formation mechanism of surface defects under milling machining conditions is investigated to reduce the surface roughness.

This paper analyzes the formation mechanism of surface defects on Mg₂Si/Al composites under micro-milling conditions by establishing a two-dimensional finite element simulation model. Response surface (Box–Behnken) experiments are designed to establish a prediction model for surface roughness, and an analysis of extreme variance is used to investigate the effects of milling depth (ap), spindle speed (vs) and feed rate (vf) on surface quality. NSGA-II multi-objective optimization algorithm is used to optimize the process parameters by considering surface roughness and milling efficiency. Experiments are also applied to verify the relationship between surface defects and particle damage. The effect of depth of cut on surface defects is also investigated.

There are few studies on solid solution treated Mg₂Si/Al composites. Solid solution treated Mg₂Si/Al composites have excellent material properties without changing the original shape of the material, and they are indispensable and critical materials in the fields of aerospace, energy, electronic information and energy transportation.

This study elucidates the formation mechanism of surface damage in Mg₂Si/Al composites, optimizes reasonable process parameters and provides technical guidance for its milling processing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0309/

To improve the localization accuracy of the magnetically controlled capsule endoscope (MCCE), a new localization method, based on the magnetic dipole model, is proposed, where the anti-disturbance permanent magnet (APM) is used as the source of stable magnetic field, thus reducing the interference of the geomagnetic field or the electric conductor magnetic field in the system.

The coupling magnetic force model between the APM and the capsule endoscope is established to obtain the magnetic force relationship and magnetic induction intensity. Along the three axes, magnetic induction intensity data are collected by a 3 × 3 sensor array composed of nine magnetic field intensity sensors, while the data are uploaded to the main computer by the STM32F103C8T6 control board over a ESP8266 WIFI module connection. Next, the axial magnetic induction intensity data are decoupled to obtain the measurement trajectory, whereas the error function is established based on the calculated trajectory parameters. Finally, the Levenberg–Marquardt (L-M) algorithm is used to solve the position information of the MCCE.

Experiments show that the average localization error of an MCCE in a straight and circular bend tube is 4.76 mm, whereas in a U-bend tube, it is 6.82 mm.

The optimized simulation value in the linear and bending environment is in good agreement with the experimental value, which verifies the accuracy of the MCCE localization system based on magnetic field sensor array, exhibiting good performance in localization and position tracking while providing a theoretical basis for the subsequent research.

For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one of essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale price index for the period of January 1, 2010 to January 10, 2020 is used to address this specific forecasting challenge for the Chinese market.

The nonlinear auto-regressive neural network (NAR-NN) model is the forecasting method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, are assessed to build the final specification.

With training, validation and testing root mean square errors of 5.89, 4.96 and 5.57, respectively, the final model produces reliable and accurate forecasts. Here, this paper demonstrates the applicability of the NAR-NN approach for commodity price predictions.

On the one hand, the findings may be used as independent technical price movement predictions. Conversely, they may be included in forecast combinations with forecasts derived from other models to form viewpoints of commodity price patterns for policy research.

Network configurations have been proposed as an efficient form of organisation and a promising area of research; however, a lack of conceptual clarity can be noted. The purpose of this review is to allow for a broad appreciation of network configurations and provide guidance for future studies of the concept.

A systematic literature review was conducted based on the PRISMA method; Scopus, Web of Science, PubMed and the Cochrane Library were searched for conference proceedings and journal articles describing organisational networks to integrate resources aimed at care delivery. Around 80 articles were included in the final review and analysed thematically and by use of bibliographic coupling.

The last decades have seen an increase in the frequency of articles describing networks for healthcare delivery. The most common contexts are care for multiple and/or long-term conditions. Three clusters of articles were found, corresponding to different conceptualisations of networks in healthcare: efficiency-enhancing cooperation, efficiency-enhancing integration and involvement for cocreation.

To increase conceptual clarity and allow the research on network configurations in healthcare to produce meta-learnings and guidance to practice, scholars are advised to provide ample descriptions of studied networks and relate them to established network classifications.

The current review has only included articles including networks as a key concept, which provides a focused overview of the use of network configurations but limits the insights into similar approaches not described explicitly as networks.

By exploring the impact of digital knowledge resources (DKR) on the carbon emission intensity of the pig industry (PCEI), this study aims to reveal the role of DKR in reducing PCEI.

Based on provincial panel data in China from 2011 to 2021, this study uses the entropy and Intergovernmental Panel on Climate Change coefficient methods to calculate the evaluation index system of DKR and PCEI, respectively. Empirical analysis using a panel fixed-effects model examines the influence of DKR on PCEI and its underlying mechanisms.

DKR can significantly reduce PCEI. This conclusion still holds even after undergoing endogeneity treatment and a series of robustness tests. Mechanism test results indicate that DKR can operate indirectly through the mediation mechanism of rural human capital (RHC) and pig breeding technology innovation (PTI), while environmental regulation intensity (ERI) plays a positive moderating role in the relationship between DKR and PCEI. The magnitude of the impact of DKR on PCEI depends on ERI. Further studies found that the impact of DKR on PCEI has obvious heterogeneity characteristics, and the promotion effect is more obvious in regions with good integration degrees and high development potential.

This paper divides DKR into three dimensions: digital technology knowledge (DTK), digital management knowledge (DMK) and digital application knowledge (DAK), providing a new framework for research and enriching the understanding of the relationship between DKR and PCEI. Furthermore, the research results reveal the application potential of DKR in the pig industry, particularly in terms of resource allocation efficiency. This is of great significance for promoting low-carbon development in the pig industry and provides insights for the low-carbon transformation of other industries. In addition, the study emphasizes the moderating effect of ERI on the mechanism of carbon reduction in the pig industry through DKR. This offers a new perspective for understanding the relationship between knowledge management and environmental governance, providing a reference basis for policy formulation in related fields.

This paper further enriches the role of DKR in the livestock industry. Integrating DKR with traditional industries promotes knowledge innovation, information distribution and utilization and scientific decision-making. This has significant value in promoting the development and application of carbon reduction technologies, enhancing industrial competitive advantages, and other aspects.