Complexity

Currently, soft robots alone cannot obtain the same operating speed as rigid robots, while rigid robots are not safe enough for human-robot interaction. To address this problem, this paper describes a hybrid robot system that combines both rigid and flexible systems for unknown domain exploration. The system consists of a four-wheeled robot chassis and a cylindrical pneumatic soft actuator, and finally, a computer is used to coordinate and control both. The hardware of the robot system is designed, a bending motion model is proposed, and SOFA framework is used to carry out finite element simulation (FEM) to verify the reasonableness of the design; linear motion speeds of up to 0.5 m/s, higher than the existing soft robots investigated, were verified experimentally separately after carrying the new module, and steering ability was retained; and the robot carrying the navigation module is verified to have a certain map building and localization function through the construction of the simultaneous localization and mapping (SLAM) experimental platform. The hybrid robot introduced in this paper can move quickly on flat terrain and can use its soft part to avoid wear and tear.

The uncertainty of parameters will have a significant impact on slope stability, where sensitivity analysis is a commonly used method in uncertainty research. However, traditional sensitivity analysis method costs much computation time. When calculating the sensitivity index of one parameter, all other parameters are taken as fixed values, and the uncertainty of all parameters cannot be considered simultaneously. Therefore, the variance-based and the moment-independent global sensitivity analysis (GSA) methods are both introduced to determine the influence of geotechnical parameters on slope stability in this study. To solve the importance index of GSA, the least angle regression algorithm, the kernel density estimation, and orthogonal polynomial estimation methods are developed to obtain variance-based importance index and the moment-independent importance index, respectively. The proposed methods allow all variables to change simultaneously within their variation range and have high computational efficiency. The results are in good at with those obtained by the variance-based Monte Carlo simulation method, which is considered as the exact solution forobtaining the importance index. The influence of the correlation between the shear strength parameters (c and φ) on the importance index is also studied, which indicates that the negative correlation will have a great impact on the importance index, which in turn affects the safety assessment of slope. Three engineering cases have been studied for engineering application, and the compared results indicate that the impact of the geotechnical parameters uncertainty on the safety factor (Fs) and failure probability () are different. Therefore, the approaches based on GSA which can integrate the Fs with will be a promising approach for slope stability evaluation.

In this article, we explore the utilization of the Caputo derivative and the Riemann–Liouville (R–L) fractional integral to analyze the optimal auxiliary function method for approximating fractional nonlinear long waves. Approximate long wave equation with a distinct dispersion relation offers the most accurate description of shallow water wave properties. Various methods, including the Adomian decomposition technique, the variational iteration method, the optimum homotopy asymptotic method, and the new iterative technique, have been employed and compared to those obtained using the fractional-order approximate long wave equation. The results of our study indicate that the optimal auxiliary function method is highly successful and practically simple, achieving better and more rapid convergence after just one repetition. This method is recognized as an efficient approach, demonstrating high precision in solving intriguing and intricate problems. Furthermore, it proves to be more time and resource efficient than other relevant analytical techniques, leading to significant savings in both volume and time. Compared to the Adomian decomposition technique, the new iterative technique, the variational iteration method, and the optimum homotopy asymptotic method, the suggested technique is extremely accurate computationally. It is also easy to analyze and solve fractionally linked nonlinear complex phenomena that arise in science and technology. We present the numerical and graphical findings that support these conclusions.

Systems are designed to perform specific task by giving certain input which produces the required output in an orderly manner known as process. The input, output, and the state variables should be known that will help in interacting with the system. The relation between these variables can be brought out by building a model that resembles or expresses the original performance of the system. The parameters of the model are estimated using the least squares approximation, maximum likelihood, maximum log-likelihood, and Bayesian parameter estimation methods by utilizing the experimental data from the multiprocess station. The selected parameters are converted to nine different transfer function models that represent the given dynamic system. The models framed are analyzed by the criterion curve technique using seven criterion functions evaluating the fitness of the model. Order of the model is found from Hankel matrix representation methods such as singular value decomposition and determinant method. Response of the models is compared with the original response to choose the best fit model by calculating ISE standard. All the above methods are used to model the system without physical and theoretical laws which is known as system identification.

The existing watermarking algorithms make it difficult to balance the invisibility and robustness of the watermark. This paper proposes a robust image watermarking method based on discrete wavelet transform (DWT), singular value decomposition (SVD), and chaotic maps. This method is a semiblind watermarking method. First, a chaotic logistic-tent map is introduced, employing an extensive chaotic parameter domain. This map is amalgamated with Arnold’s transformation to encrypt the watermark image, thereby bolstering the security of the watermark information. Subsequently, the frequency domain is obtained by applying DWT to the carrier image. Embedding watermarks in the frequency domain ensures the invisibility of the watermark, with a preference for a high-frequency subband after the DWT of the carrier image for enhanced watermark robustness. SVD is then applied to both the high-frequency subband of the carrier image after DWT and the encrypted watermark image. The final step involves embedding the singular values of the encrypted watermark image into the carrier image’s singular values, thereby completing the watermark information embedding process. In simulation experiments, an invisibility test was conducted on various carrier images, yielding peak signal-to-noise ratio (PSNR) values consistently exceeding 43, and structural similarity (SSIM) close to 1. Robustness testing against various types of attacks resulted in normalized correlation (NC) values consistently surpassing 0.9, with bit error rate (BER) values approaching 0. In conclusion, the proposed algorithm satisfies imperceptibility requirements while demonstrating formidable robustness.

This article applies quantitative methods from complex network analysis to investigate and compare the organization of L1 and L2 lexical-semantic networks. Forty-eight English learners with Chinese as their native language completed a semantic fluency task, first in English and then in Chinese, based on which two lexical-semantic networks were constructed. Comparison at the global level found that the L1 lexical-semantic network displays more prominent small-world and scale-free features and a clearer modular structure in comparison with its L2 counterpart. Locally, although the two lexical-semantic networks share most of their central words, they differ remarkably in their composition and the connection pattern of their peripheral words. Specifically, L1 peripheral words are likely to connect with each other to form local modules while L2 peripheral words tend to connect with central words. Moreover, word centrality was found to be closely related to time of generation, generation frequency, and accuracy in fluency tasks, and such tendency is more obvious in L1 than in L2. The findings demonstrate the advantages of quantitative analysis granted by network science in the investigation of mental lexicon and provide insights for lexical representation research and classroom vocabulary instructions.