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The existing Nonlinear Dynamic Vibration Absorbers (NLDVAs) have the disadvantages of complex structure, high cost, high installation space requirements and difficulty in miniaturization. And most of the NLDVAs have not been applied to reality. To address the above issues, a novel Triple-magnet Magnetic Dynamic Vibration Absorber (TMDVA) with tunable stiffness, only composed of triple cylindrical permanent magnets and an acrylic tube, is designed, modeled and tested in this paper.

(1) A novel TMDVA is designed. (2) Theoretical and experimental methods. (3) Equivalent dynamics model.

It is found that adjusting the magnet distance can effectively optimize the vibration reduction effect of the TMDVA under different resonance conditions. When the resonance frequency of the cantilever changes, the magnet distance of the TMDVA with a high vibration reduction effect shows an approximately linear relationship with the resonance frequency of the cantilever which is convenient for the design optimization of the TMDVA.

Both the simulation and experimental results prove that the TMDVA can effectively reduce the vibration of the cantilever even if the resonance frequency of the cantilever changes, which shows the strong robustness of the TMDVA. Given all that, the TMDVA has potential application value in the passive vibration reduction of engineering structures.

The purpose is to predict the distribution of the residual pretightening force of the bolt group under the action of any initial pretightening force, and to achieve the final residual pretightening force as the target to solve the initial pretightening force value to be applied.

Based on the finite element method and the elastic interaction theory between bolt group, this paper establishes a prediction model for the residual pretightening force distribution of bolt group for one-step pretightening and multi-step pretightening of gasketless flange connection systems. In addition, using the general modeling method given in this paper, the prediction model of residual pretightening force of long plate bolt connection system is established, and compared with reference, which fully proves the effectiveness and universality of the general prediction model of residual pretightening force of bolt group.

The appropriate pretightening sequence, increasing the number of pretightening steps and variable amplitude loading can effectively reduce the influence of elastic interaction and improve the uniformity of residual pretightening force of the bolt group. And the selection of material, number of bolts and connected thickness of bolt connection system also has a great influence on the distribution of residual pretightening force of bolt groups.

The general prediction model for the residual pretightening force of bolt group of connecting structural components considering elastic interaction given in this paper can provide a reference for the design and optimization of the bolt assembly process of the rotor system and the casing system in aero-engine and the prediction of the performance of the connecting system.

This study aims to discuss the influences of surface severe plastic deformation (S²PD) on the electrochemical corrosion, pitting corrosion, intergranular corrosion, stress corrosion cracking of aluminum (Al) alloys and attempt to correlate the microstructural/compositional changes with the performances.

This study provides a novel gradient design of structure/composition caused by S²PD for the purpose of enhancing Al alloys’ corrosion resistance.

S²PD has a significant effect on corrosion behavior of Al alloys through tuning the grain size, residual stress, composition, grain boundary phase and second phase particle distribution.

Although Al alloys are known to form a protective Al₂O₃ film, corrosion is a major challenge for the longevity of Al structures across numerous industries, especially for the infrastructures made of high-strength Al alloys. Traditional strategies of improving corrosion resistance of Al alloys heavily relied on alloying and coatings. In this review, gradient design of structure/composition caused by S²PD provides a novel strategy for corrosion protection of Al alloys, especially in the enhancement of localized corrosion resistance.

This study aims to clarify influencing factors of overcapacity of new energy enterprises in China and accurately predict whether these enterprises have overcapacity.

Based on relevant data including the experience and evidence from the capital market in China, the research establishes a generic univariate selection-comparative machine learning model to study relevant factors that affect overcapacity of new energy enterprises from five dimensions. These include the governmental intervention, market demand, corporate finance, corporate governance and corporate decision. Moreover, the bridging approach is used to strengthen findings from quantitative studies via the results from qualitative studies.

The authors' results show that the overcapacity of new energy enterprises in China is brought out by the combined effect of governmental intervention corporate governance and corporate decision. Governmental interventions increase the overcapacity risk of new energy enterprises mainly by distorting investment behaviors of enterprises. Corporate decision and corporate governance factors affect the overcapacity mainly by regulating the degree of overconfidence of the management team and the agency cost. Among the eight comparable integrated models, generic univariate selection-bagging exhibits the optimal comprehensive generalization performance and its area under the receiver operating characteristic curve Area under curve (AUC) accuracy precision and recall are 0.719, 0.960, 0.975 and 0.983, respectively.

The proposed integrated model analyzes causes and predicts presence of overcapacity of new energy enterprises to help governments to formulate appropriate strategies to deal with overcapacity and new energy enterprises to optimize resource allocation. Ten main features which affect the overcapacity of new energy enterprises in China are identified through generic univariate selection model. Through the bridging approach, the impact of the main features on the overcapacity of new energy enterprises and the mechanism of the influence are analyzed.

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.

A community demonstrates the unique qualities and relationships between its members that distinguish it from other communities within a network. Network analysis relies heavily on community detection. Despite the traditional spectral clustering and statistical inference methods, deep learning techniques for community detection have grown in popularity due to their ease of processing high-dimensional network data. Graph convolutional neural networks (GCNNs) have received much attention recently and have developed into a potential and ubiquitous method for directly detecting communities on graphs. Inspired by the promising results of graph convolutional networks (GCNs) in analyzing graph structure data, a novel community graph convolutional network (CommunityGCN) as a semi-supervised node classification model has been proposed and compared with recent baseline methods graph attention network (GAT), GCN-based technique for unsupervised community detection and Markov random fields combined with graph convolutional network (MRFasGCN).

This work presents the method for identifying communities that combines the notion of node classification via message passing with the architecture of a semi-supervised graph neural network. Six benchmark datasets, namely, Cora, CiteSeer, ACM, Karate, IMDB and Facebook, have been used in the experimentation.

In the first set of experiments, the scaled normalized average matrix of all neighbor's features including the node itself was obtained, followed by obtaining the weighted average matrix of low-dimensional nodes. In the second set of experiments, the average weighted matrix was forwarded to the GCN with two layers and the activation function for predicting the node class was applied. The results demonstrate that node classification with GCN can improve the performance of identifying communities on graph datasets.

The experiment reveals that the CommunityGCN approach has given better results with accuracy, normalized mutual information, F1 and modularity scores of 91.26, 79.9, 92.58 and 70.5 per cent, respectively, for detecting communities in the graph network, which is much greater than the range of 55.7–87.07 per cent reported in previous literature. Thus, it has been concluded that the GCN with node classification models has improved the accuracy.

1. Explore the important role of sole actual controller in the innovation decision of the firm and the different effects of the ownership of sole actual controller on innovation; 2. Explore whether the role played by sole actual controllers varies in different types of firms; 3. Explore the important role of cooperative culture in the internal governance of firms and whether sole actual controller firms feel a rejection effect on cooperative culture.

The authors collect data on Shanghai and Shenzhen A-share listed companies from 2011 to 2021 to analyze the role of the sole actual controller on innovation investment, as well as the moderating effect of cooperative culture in corporate annual reports using natural language processing.

The authors find that sole actual controllers promote corporate innovation investment and that concentrated equity inhibits corporate innovation investment, while dispersed equity concentration promotes it. In addition, cooperative culture has a nonlinear moderating effect on the relationship between SACs and innovation.

On the one hand, this study focuses chiefly on the decision-making behavior of top managers, such as the SACs and shareholders, and does not account for the role of bottom-level employees or professional R&D teams in innovation. On the other hand, although this study discusses the moderating role of corporate cooperative culture, it is limited to internal cooperative culture; cooperative culture should also consider external cooperation, such as cooperation between companies or between companies and universities.

First, companies should actively implement the SAC model and scientifically select a truly compassionate and visionary SAC as the dominant person in the company. Second, the Chinese government needs to standardize the identification of actual controllers, who should not be a shareholder of the company. Third, policymakers should promote the reform of the mixed system of enterprises, optimize the shareholding structure of firms, make executives an important part of corporate governance. Fourth, cooperation culture is a good start, though firms should avoid letting it become a “double-edged sword” of the management mode of the SAC.

First, existing studies do not address the impact of SACs on innovation from the perspective of SACs, who have most influence the firm's decision-making. Focusing on the SAC's decision-making style has sufficient practical implications for future corporate innovation planning. This study used the natural language processing (NLP) module in ChatGPT to analyze the culture of cooperation in corporate annual reports. Currently, corporate culture is an obstacle to the study of corporate governance because of its obscurity and difficulty of quantification. The authors adopted a PSM (propensity score matching) approach to eliminate the endogeneity of the data, which makes the results more scientific.

Flexibility and efficiency are dual attributes of the organizational structure that are crucial for project-driven enterprises to achieve sustainable development in a dynamic environment. However, there is a lack of research on the patterns by which the dual attributes of a project-driven enterprise’s organizational structure affect business model innovation. Employing organizational theory, this study aims to assess the mediating mechanisms and dynamic capabilities through which the dual attributes of the organizational structure influence business model innovation in project-driven enterprises.

Data were collected from 242 employees from four project-driven companies across 26 cities (e.g. Beijing, Tianjin, Guangzhou and Shenzhen) in China. Structural equation modeling revealed the relationship between organizational structure’s dual attributes and business model innovation.

The findings show that the dual attributes (flexibility and efficiency) of the organizational structure have positive impacts on business model innovation. Moreover, dynamic capabilities mediate the relationship between the dual attributes and business model innovation in project-driven enterprises.

This study provides contributions to innovation research in the context of project-driven enterprises by revealing the influence of organizational structure on business model innovation through the firms’ dynamic capabilities. Such knowledge can enable managers of project-driven enterprises to develop effective interventions to promote business model innovation.

To extend the reuse method and rate of straw biomass, this paper investigated the effect of lignin synthetic phenolic resin (LPF) on the rheological properties of asphalt binder.

Four LPFs with 25%, 50%, 75% and 100% substitution rates were prepared by replacing phenol with lignin in synthetic resins and using it as a modifier to prepare a bio-asphalt binder. Temperature sweep tests were conducted to evaluate aging resistance and temperature sensitivity of the bio-asphalt binder. The rutting resistance of the bio-asphalt binder was evaluated by frequency sweeps and multiple stress creep recovery (MSCR) test. Linear amplitude sweep (LAS) tests were conducted to evaluate the fatigue resistance of the bio-asphalt binder. A master curve was constructed to further analyze the rheological properties of the bio-asphalt binder at different frequencies. The low-temperature cracking resistance of the binder was evaluated by G-R parameters, critical temperatures and ΔTc. Fourier transform infrared spectroscopy (FTIR) was performed to investigate the changes in the functional groups of the binder before and after aging.

The results indicated that adding LPF could improve the high-temperature rutting resistance, fatigue resistance, aging resistance of asphalt and the binders are less affected by temperature. Additionally, LPF slightly prohibited the low-temperature performance of the asphalt binder, which, however, was significantly lower than the base asphalt degradation during aging. Compared with base asphalt binders, the bio-asphalt binder showed no new absorption peaks generated after adding LPF, identifying that the improved asphalt binder performance by LPF was a mainly physical modification.

The main objective of this paper is to further improve the substitution rate (i.e. the mass substitution ratio of lignin to phenol) of lignin and broaden the application of biomass resins, thus realizing resource sustainability.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.