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The purpose of this paper is to introduce a novel method to study the flutter coupling mechanism of the twin-fuselage aircraft, which is becoming a popular transportation vehicle recently.

A new method of flutter mode indicator is proposed based on the principle of work and power, which is realized through energy accumulation of generalized force work on generalized coordinates, based on which flutter coupling mechanism of the twin-fuselage aircraft is studied using ground vibration test and computational fluid dynamics/computational solid dynamics method.

Verification of the proposed flutter mode indicator is provided, by which the flutter mechanism of the twin fuselage is found as the horizontal tail’s torsion coupled with its bending effect and the “frequency drifting” phenomenon of twin-fuselage aircraft is explained logically, highlighting the proposed method in this paper.

This paper proposed a new method of flutter mode indicator, which has advantages in flutter modes indexes reliability, clear physical meaning and results normalization. This study found the flutter coupling mechanism of twin-fuselage aircraft, which has important guiding significance to the development of twin-fuselage aircraft.

The paper aims to study the constraint solutions of the periodic coupled operator matrix equations by the biconjugate residual algorithm. The new algorithm can solve a lot of constraint solutions including Hamiltonian solutions and symmetric solutions, as special cases. At the end of this paper, the new algorithm is applied to the pole assignment problem.

When the studied periodic coupled operator matrix equations are consistent, it is proved that constraint solutions can converge to exact solutions. It is demonstrated that the solutions of the equations can be obtained by the new algorithm with any arbitrary initial matrices without rounding error in a finite number of iterative steps. In addition, the least norm-constrained solutions can also be calculated by selecting any initial matrices when the equations of the periodic coupled operator matrix are inconsistent.

Numerical examples show that compared with some existing algorithms, the proposed method has higher convergence efficiency because less data are used in each iteration and the data is sufficient to complete an update. It not only has the best convergence accuracy but also requires the least running time for iteration, which greatly saves memory space.

Compared with previous algorithms, the main feature of this algorithm is that it can synthesize these equations together to get a coupled operator matrix equation. Although the equation of this paper contains multiple submatrix equations, the algorithm in this paper only needs to use the information of one submatrix equation in the equation of this paper in each iteration so that different constraint solutions of different (coupled) matrix equations can be studied for this class of equations. However, previous articles need to iterate on a specific constraint solution of a matrix equation separately.

The purpose of this paper is to locate those entrepreneur human capital elements which significantly influence an enterprise's growth performance, within both high‐tech and traditional enterprises, thereby helping entrepreneurs understand that human capital elements will provide different impacts within different industries.

Under some necessary research assumptions, the statistical analysis described in this paper uses data collected from a questionnaire survey and is performed under the SPSS16.0 Program.

An enterprise growth model is built from the perspectives of entrepreneur human capital. Analysis shows that human capital elements of the same entrepreneur have different impacts on the performance of business growth in different industries. The theoretical model provides a better explanation of the high‐tech enterprises' growth performance. Innovation and business growth performances from a high‐tech enterprise have greater reliance on human capital of entrepreneurs than the traditional industries.

Most current studies of the human capital of entrepreneurs focus on the analysis of background characteristics, but inadequate attention has been given to the relationship between human capital and the enterprises' growth performance, as well as to the comparative analysis of entrepreneurs' human capital in high‐tech enterprises and traditional enterprises. This paper, however, compares and analyzes such relationships between high‐tech enterprises and traditional enterprises.

The purpose of this paper is to introduce an effective method to discriminate seal inks with Raman microscopy.

Raman spectra could effectively avoid interference from the paper and give extra peak information in the inks discrimination and identification. Thus, a Renishaw invia confocal Raman microscope system was employed for ink analysis in this study. A total of 12 representative seal ink samples, widely used in seven Chinese provinces, were investigated using the latest model of Renishaw Raman microscope.

Four types of inks were identified and discriminated successfully. Popular pigments such as Pigment Scarlet Powder, Pigment Yellow 55, phthalocyanine blue, Bronze red C and PbCrO3 were all identified in these seal ink samples. The indicative peaks to identify and discriminate the inks were also summarised and tentatively interpreted.

More ink samples were needed to establish a useful library. Many other pigments used in inks were still unknown.

This method was proved to be fast, accurate and non-destructive, and it could be more easily applied in real cases than Fourier transform infrared spectroscopy.

This method can help scientists discriminate some inks, which can hardly be discriminated by other techniques. The results are useful for the ink analysis and discrimination in forensic (document examination and file source identification), polymer and pigment fields.

The purpose of this study is to modify the FDM 3D printer to print with polystyrene (PS) microspheres as the printing material, thus enabling bottom-up structural color printing and evaluating structural color printing.

This study chose a range of different heated bed temperatures to determine a suitable temperature for accelerating the self-assembly of photonic crystals and printing structural colors on various substrates. In addition, this study enhanced the structural color by doping PS microspheres with different contents of Acid Black 210 dye and evaluated the color-enhanced structural color by eye and spectrophotometer under different light sources.

The results show that the modified 3D printer can be used for structural color printing, and 50°C is determined as the heated bed temperature. There are significant differences in structural colors when printing under different color backgrounds and material substrates, and corresponding suitable substrates should be selected according to the application. The doping of PS microspheres with varying contents of dye results in different color levels of structural color. As with pigment colors, the visual perception of structural color varies when viewed under different light sources.

This paper proposes to print structural colors low-costly, analyze structural colors under substrate and light source conditions, and expand the structural color gamut by enhancing structural colors, which has positive implications for further research on structural colors as printing colors.

This paper aims to explore the impact of live streamer authenticity (LSA) on purchase intention in tourism e-commerce live streaming, with a focus on boundary conditions and underlying mechanisms.

The data collected from 451 participants were analyzed using structural equation modeling.

This paper found that four dimensions of LSA – sincerity, truthfulness endorsement, expertise and uniqueness – positively influenced purchase intention, while visibility did not. In addition, sincerity, truthfulness endorsement and uniqueness had an indirect influence on purchase intention through flow experience, while sincerity, truthfulness endorsement, expertise and uniqueness had an indirect effect through perceived trust. Furthermore, self-construal moderated the effect of sincerity and truthfulness endorsement on purchase intention, with the positive effect being stronger for the dependent self-construal.

To the best of the authors’ knowledge, it is the first study to examine LSA dimensions and their consequences. This paper not only provides a better and more detailed understanding of the complexity of LSA but also contributes to the development of authenticity theory by responding to individual authenticity studies.

本文旨在研究旅游直播电商中主播真实性对旅游购买意向的影响, 及其内在机制和边界条件。

本文采用问卷调查的方法收集451名参与者的数据。然后用结构方程模型（SEM）对收集的数据进行分析。

本文发现, 主播真实性的四个维度–真诚性、真实背书、专业性和独特性–对购买意愿有积极影响, 而主播的透明性没有影响。此外, 真诚性、真实背书和独特性通过心流体验对购买意愿产生间接影响, 此外真诚性、真实背书、专业性和独特性还通过感知信任产生间接影响。此外, 自我建构调节了真诚性和真实背书对购买意向的影响。对于依赖型自我建构, 真诚性和真实背书对购买意愿的影响更强。

本文是最早探讨主播真实性维度及其后果影响的研究之一。本文不仅对主播真实性的复杂性有了更好更详细的了解, 而且响应了对个体真实性研究的呼吁, 为真实性理论的发展做出了贡献。

Este artículo explora el impacto de la autenticidad del streamer en la intención de compra en la retransmisión en directo de comercio electrónico turístico, centrándose en las limitaciones y los procesos subyacentes.

Se analizaron los datos recopilados de 451 participantes utilizando un modelo de ecuaciones estructurales (SEM).

Este trabajo halló que cuatro dimensiones de la autenticidad de los streamers en directo -la sinceridad, el respaldo a la veracidad, la pericia y la singularidad- influían positivamente en la intención de compra, mientras que la visibilidad no lo hacía. Además, la sinceridad, el respaldo de veracidad y la singularidad influyeron indirectamente en la intención de compra a través de la experiencia de flujo, mientras que la sinceridad, el respaldo de veracidad, la pericia y la singularidad tuvieron un efecto indirecto a través de la confianza percibida. Además, la autoconstrucción moderó el efecto de la sinceridad y el respaldo de veracidad sobre la intención de compra, siendo el efecto positivo más fuerte para la autoconstrucción dependiente.

Es el primer estudio que examina las dimensiones de la autenticidad de los retransmisores en directo y sus consecuencias. Este trabajo no sólo proporciona una comprensión mejor y más detallada de la complejidad de la autenticidad de los streamers en directo, sino que también contribuye al desarrollo de la teoría de la autenticidad al responder a los estudios sobre autenticidad individual.

This paper aims to use the Monodepth method to improve the prediction speed of identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the path of the agent, which can reach the destination more quickly than the Dueling DQN algorithm. Then the path planning algorithm based on Probability Dueling DQN is combined with FastSLAM to accomplish the autonomous navigation and map the environment.

This paper proposes an active simultaneous localization and mapping (SLAM) framework for autonomous navigation under an indoor environment with static and dynamic obstacles. It integrates a path planning algorithm with visual SLAM to decrease navigation uncertainty and build an environment map.

The result shows that the proposed method offers good performance over existing Dueling DQN for navigation uncertainty under the indoor environment with different numbers and shapes of the static and dynamic obstacles in the real world field.

This paper proposes a novel active SLAM framework composed of Probability Dueling DQN that is the improved path planning algorithm based on Dueling DQN and FastSLAM. This framework is used with the Monodepth depth image prediction method with faster prediction speed to realize autonomous navigation in the indoor environment with different numbers and shapes of the static and dynamic obstacles.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

In recent years, China's marine industry has maintained rapid growth in general, and marine-related economic activities have continued to improve. The purpose of this research is to analyze the basic situation of China's marine economy development, identify the problems therein, forecast development trends and propose policy recommendations accordingly.

This research conducts a comprehensive and detailed analysis of the development of China's marine economy with rich data in diversified aspects. The current situation of China's marine economy development is analyzed from the perspective of scale and structure, and the external and internal development environment of China's marine economy is discussed. With the application of measurement and prediction method such as trend extrapolation, exponential smoothing, grey forecasting and neural network method, the future situation of China's marine economy development is forecasted.

In a complex environment where uncertainties at home and abroad have increased significantly, China's marine economy development suffers tremendous downward pressure in recent years. As China has achieved major achievements in the prevention and control of the COVID-19 epidemic, the marine economy development will gradually return to normal. It is estimated that the gross marine production value in 2022 will exceed 10 trillion yuan. China's marine economy will continue to maintain a steady growth trend in the future, and its development prospects will remain promising.

This research explores the current situation and trends of China's marine economy development and puts forward policy recommendations to promote the steady and health development of China's marine economy accordingly.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.