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This paper aims to introduce a novel algorithm to solve initial/boundary value problems of high-order ordinary differential equations (ODEs) and high-order system of ordinary differential equations (SODEs).

The proposed method is based on Hermite polynomials and extreme learning machine (ELM) algorithm. The Hermite polynomials are chosen as basis function of hidden neurons. The approximate solution and its derivatives are expressed by utilizing Hermite network. The model function is designed to automatically meet the initial or boundary conditions. The network parameters are obtained by solving a system of linear equations using the ELM algorithm.

To demonstrate the effectiveness of the proposed method, a variety of differential equations are selected and their numerical solutions are obtained by utilizing the Hermite extreme learning machine (H-ELM) algorithm. Experiments on the common and random data sets indicate that the H-ELM model achieves much higher accuracy, lower complexity but stronger generalization ability than existed methods. The proposed H-ELM algorithm could be a good tool to solve higher order linear ODEs and higher order linear SODEs.

The H-ELM algorithm is developed for solving higher order linear ODEs and higher order linear SODEs; this method has higher numerical accuracy and stronger superiority compared with other existing methods.

With the development of deep learning-based analytical techniques, increased research has focused on fatigue data analysis methods based on deep learning, which are gaining in popularity. However, the application of deep neural networks in the material science domain is mainly inhibited by data availability. In this paper, to overcome the difficulty of multifactor fatigue life prediction with small data sets,

A multiple neural network ensemble (MNNE) is used, and an MNNE with a general and flexible explicit function is developed to accurately quantify the complicated relationships hidden in multivariable data sets. Moreover, a variational autoencoder-based data generator is trained with small sample sets to expand the size of the training data set. A comparative study involving the proposed method and traditional models is performed. In addition, a filtering rule based on the R2 score is proposed and applied in the training process of the MNNE, and this approach has a beneficial effect on the prediction accuracy and generalization ability.

A comparative study involving the proposed method and traditional models is performed. The comparative experiment confirms that the use of hybrid data can improve the accuracy and generalization ability of the deep neural network and that the MNNE outperforms support vector machines, multilayer perceptron and deep neural network models based on the goodness of fit and robustness in the small sample case.

The experimental results imply that the proposed algorithm is a sophisticated and promising multivariate method for predicting the contact fatigue life of a coating when data availability is limited.

A data generated model based on variational autoencoder was used to make up lack of data. An MNNE method was proposed to apply in the small data case of fatigue life prediction.

The purpose of this paper is to study a feasible visualization of large-size three-dimension (3D) color models which are beyond the maximum print size of newest paper-based 3D printer used 3D cutting-bonding frame (3D-CBF) and evaluate the effects of cutting angle and layout method on printing time of designed models.

Sixteen models, including cuboid model, cylinder model, hole model and sphere model with different shape features, were divided into two symmetric parts and printed by the Mcor IRIS HD 3D printer. Before printing, two sub-parts were rearranged in one of three layout methods. Nine scaled sizes of original models were printed to find the quantitative relationship between printing time and scale values in each type. For the 0.3 times of original models, six cutting angles were evaluated in detail.

The correlation function about colorization time and printed pages was proposed. Based on 3D-CBF, the correlation between printing time and scale size is statistically defined. Optimization parameters of designed parts visualization about cutting angel and layout method were found, even if their statistical results were difficult to model their effects on printing time of specimens.

The research is comparative and limited to the special models and used procedures.

The paper provides a feasible visualization and printing speed optimization methods for the further industrialization of 3D paper-based printing technology in cultural creative field.

Exploring the intrinsic connection between the ecological environment and the digital economy and empirically testing how the level of digital economic development affects the ecological environment. Using the entropy weighting method to analyze the weights of the indicators in the digital economic development level and ecological environment system to explore the factors that have the greatest impact on the ecological environment in the indicator system of the digital economic development level so as to deepen the theoretical understanding of the relationship between the level of development of the digital economy and the ecological environment. Explore the regional heterogeneity of the level of development of the digital economy to promote the healthy development of China’s ecological environment proving the difference in the level of development of the digital economy in the east west and central regions of China and the difference in the effect on the ecological environment.

Based on the panel data of 30 provinces in China from 2013 to 2021 this paper fits the index system of digital economy development level with three factors. A digital infrastructure digital industry and digital application combines environmental pollution and energy consumption to construct ecological environment indicators and explored the impact of digital economy development level on the ecological environment by using the entropy weight method and the random effect model.

The findings indicate that the degree of digital economic development has a positive and significant impact on promoting the healthy development of the ecological environment, in which the digital industry has the greatest impact on the ecological environment. Meanwhile, the improvement of industrial structure also has a positive effect on the improvement of the ecological environment, whereas the level of human capital inhibits the healthy development of the ecological environment, and the governmental support fails to effectively and significantly promote the improvement of the ecological environment. Furthermore, the empirical research indicates that the level of digital economy development has obvious regional heterogeneity on the healthy development of the ecological environment: the eastern and central regions have a significant effect, while the western region has a less significant effect.

Although domestic and foreign scholars and experts have conducted sufficient studies on the ecological environment and the development level of digital economy respectively, there are few studies on the empirical analysis of the positive significance and regional heterogeneity of the impact of the development level of digital economy on the ecological environment, which can be supplemented and referred to in this study. At the same time, it also provides intellectual support for our country to achieve high-quality development of digital economy and efficient governance of ecological environment.

The purpose of this paper is to enhance the characteristics of TiO₂ nanoparticles (NPs). Because these NPs stick together easily and are difficult to distribute evenly, they cannot be used extensively in lubricating oils. Altering TiO₂ was recommended as an alternate way for making NPs simpler to disperse.

Through dielectric barrier discharge plasma (DBDP)-assisted ball mill diagnostics and modeling of molecular dynamics, TiO₂@PEG-400 NPs were produced using the DBDP-assisted ball mill. The NPs’ microstructure was examined using FESEM, TEM, XRD, FT-IR and TG-DSC. Using the CFT-1 reciprocating friction tester, the tribological properties of TiO₂@PEG-400 NPs as base oil additives were studied. EDS and XPS were used to examine the surface wear of the friction pair.

Tribological properties of the modified NPs are vastly superior to those of the original NPs, and the lipophilicity value of TiO₂ NPs was improved by 200%. It was determined through tribological testing that TiO₂@PEG-400’s exceptional performance might be attributable to a chemical reaction film made up of TiO₂, Fe₂O₃, iron oxide and other organic chemicals.

This work describes an approach for preventing the aggregation of TiO₂ NPs by coating their surface with PEG-400. In addition, the prepared NPs can enhance the tribological performance of lubricating oil. This low-cost, high-performance lubricant additive has tremendous promise for usage in marine engines to minimize operating costs while preserving navigational safety.

Previously, the effect of pore-forming agents on the properties of pore size and morphology was studied. In this paper, we determine the optimal combination of parameters by tensile strength and perform tribological tests with optimal combination of parameters.

In this paper, porous polyimide (PI) materials were fabricated using vacuum hot molding technology. The orthogonal experiment was designed to test the mechanical properties of porous PI materials with the process parameters and the content of pore-forming agent as the changing factors. The porous PI oil-bearing materials were obtained by vacuum immersion, and tribological test were carried out.

The results showed that porous PI oil-bearing materials are suitable for low-speed and low-load conditions. The actual value of the friction coefficient basically match with the theoretical value of the regression analysis, and the errors of the friction coefficient are within 10% and 3%, respectively, which proves that the method used in the study is feasible for the friction coefficient prediction.

In this paper, we have produced a new porous oil-bearing material with good tribological properties. This study can effectively predict the friction coefficient of PI porous material.

The safe operation of the metro power transformer directly relates to the safety and efficiency of the entire metro system. Through voiceprint technology, the sounds emitted by the transformer can be monitored in real-time, thereby achieving real-time monitoring of the transformer’s operational status. However, the environment surrounding power transformers is filled with various interfering sounds that intertwine with both the normal operational voiceprints and faulty voiceprints of the transformer, severely impacting the accuracy and reliability of voiceprint identification. Therefore, effective preprocessing steps are required to identify and separate the sound signals of transformer operation, which is a prerequisite for subsequent analysis.

This paper proposes an Adaptive Threshold Repeating Pattern Extraction Technique (REPET) algorithm to separate and denoise the transformer operation sound signals. By analyzing the Short-Time Fourier Transform (STFT) amplitude spectrum, the algorithm identifies and utilizes the repeating periodic structures within the signal to automatically adjust the threshold, effectively distinguishing and extracting stable background signals from transient foreground events. The REPET algorithm first calculates the autocorrelation matrix of the signal to determine the repeating period, then constructs a repeating segment model. Through comparison with the amplitude spectrum of the original signal, repeating patterns are extracted and a soft time-frequency mask is generated.

After adaptive thresholding processing, the target signal is separated. Experiments conducted on mixed sounds to separate background sounds from foreground sounds using this algorithm and comparing the results with those obtained using the FastICA algorithm demonstrate that the Adaptive Threshold REPET method achieves good separation effects.

A REPET method with adaptive threshold is proposed, which adopts the dynamic threshold adjustment mechanism, adaptively calculates the threshold for blind source separation and improves the adaptability and robustness of the algorithm to the statistical characteristics of the signal. It also lays the foundation for transformer fault detection based on acoustic fingerprinting.

Existing research has generally viewed that temporal leadership has positive impacts on employees but ignores its potential drawbacks. This study aims to develop a model to explore its possible negative impacts on employees, drawing upon social information processing theory.

This study conducts a multi-wave and multisource survey to test the model, and the authors test the hypotheses with multi-level analysis using Mplus 7.4 and R package for Monte Carlo.

Results suggest that temporal leadership induces employee work alienation, thus leading to employee silence. Furthermore, shared temporal cognitions moderate both the relationship between temporal leadership and work alienation and the indirect effect of temporal leadership on employee silence via work alienation.

Taken together, this study reveals the potential dark side of temporal leadership and provides a more comprehensive and dialectical research perspective for temporal leadership literature.

The purpose of this paper is to explore the influencing mechanism of coaching leadership (CL) on employee voice behavior (VB) based on cognitive-affective system theory of personality (CAPS). Specifically, the study intends to build a model of psychological security (PS) and openness to change (OC) that mediate the relationship between CL and employee VB at an individual-level and group-level from cognitive-affective dual perspective.

CL, employee VB, PS and OC were assessed in an empirical study based on a supervisor–subordinate dyads sample of 287 employees and 72 team leaders from enterprises in Southern China.

From CAPS theory perspective, the authors found that CL promotes employee VB and that PS and OC mediate the relationship between CL and VB.

Results underscore the importance of encouraging managers to engage in CL behaviors, which are conductive to enhancing employee PS and OC thereby improving employee VB. These results also highlight the significance of managerial attention to a secure voice atmosphere and the improvement of employees’ affective commitment to organizational change.

The research findings provide a significant contribution to the literature in that it shows PS and OC as crucial dual mediating mechanism through which CL influences VB. Moreover, this paper is one of the few studies answering the call to examine the effect of leadership at multiple levels.

The purpose of this paper is to illustrate the importance of redesigning, reusing, remanufacturing, recovering, recycling and reducing (6R) to sustainable manufacturing and discuss the general procedure to reconfigure robots. Two critical challenges in adopting industrial robots in small and medium-sized enterprise (SMEs) are flexibility and cost, as the number of tasks of the same type can be limited because of the size of an SME. The challenges can be alleviated by 6R. The 6R processes allow a robot to adopt new tasks, increase its utilization rate and reduce unit costs of products.

There is no shortcut to implement sustainable manufacturing. All of the manufacturing resources in a system should be planned optimally to reduce waste and maximize the utilization rates of resources. In this paper, modularization and reconfiguration are emphasized to implement 6R processes in sustainable manufacturing; robots are especially taken into consideration as core functional modules in the system. Modular architecture makes it feasible to integrate robots with low-cost customized modules for various tasks for the high utilization rates. A case study is provided to show the feasibility.

Finding the ways to reuse manufacturing resources could bring significant competitiveness to an SME, in the sense that sophisticated machines and tools, such as robots, can be highly utilized even in a manufacturing environment with low or medium product volumes. The concepts of modularization and 6R processes can be synergized to achieve this goal.

The authors propose the strategy to enhance the utilization rates of core manufacturing resources using modular architecture and 6R practice. The axiomatic design theory can be applied as the theoretical fundamental to guide the 6R processes; however, a universal solution in the implementation is not available. The solutions have to be tailored to specific SMEs, and the solutions should vary with respect to time.

To operate a sustainable manufacturing system, a continuous design effort is required to reconfigure existing resources and enhance their capabilities to fulfill new tasks in the dynamic environment.

The authors focus on the importance of sustainable manufacturing to modern society, and they achieve this goal by reusing robots as system components in different applications.

Sustainable manufacturing has attracted a great deal of attention, although the operable guidance for system implementation is scarce. The presented work has thrown some light in this research area. The 6R concept has been introduced in a modular system to maximize the utilizations of critical manufacturing resources. It is particularly advantageous for SMEs to adopt sophisticated robots cost-effectively.