Complexity

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.

This study elucidates the factors impacting China’s apple trade, its efficiency, and opportunities for increased revenue. This study adopts a stochastic frontier gravity model on China’s apple exports data, covering the period from 1997 to 2022 across 38 significant trading partners. The finding demonstrates that the economic growth of China and its trading partners substantially boosts apple export volumes, with a positive correlation between GDP growth and export flows. The research also highlights the deterrent effect of geographical distance on exports and reveals a complex negative relationship between the per capita GDP of importing nations and export efficiency, shedding light on the nuanced nature of trade dynamics. Furthermore, the study finds that the appreciation of China’s currency plays a crucial role in enhancing export efficiency by lowering transaction costs. Meanwhile, increased agricultural land in importing countries presents competitive challenges, impacting export performance negatively. Geographical proximity and infrastructural features, such as shared borders and lack of access to seaport, are identified as significant factors in export efficiency. The analysis unveils considerable untapped export potential in various countries, suggesting a strategic avenue for market expansion. To optimize China’s apple export strategy, policymakers are advised to consider currency management, negotiate trade agreements that mitigate distance and per capita GDP effects, and target markets with high untapped potential, thereby facilitating sustainable growth in China’s apple export sector.

Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking to make accurate stops at logistics stations, such as loading stations, or to pick up or drop off trolleys, pallets, or racks. This paper proposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-differential AGV. The control strategy combines conventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). The LPC is used for trajectory tracking while the PID is used for speed control of the robot. The stability of the controller is demonstrated for any differentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for several trajectories, the LPC outperforms it in all cases.

The indexing work of complex products and systems (CoPS) is to determine a comprehensive set of indicators to ensure seamless integration of CoPS modules. Despite its pivotal role as a pioneering phase in CoPS development, indexing work has garnered relatively scant research attention. This paper explores CoPS indexing work through a network game model that considers a main manufacturer and several suppliers. The primary aim is to discern the key influencing factors affecting CoPS indexing work and elucidate the influence mechanism. Several interesting conclusions have been drawn: (1) subjective, environmental, and structural factors as the three key aspects influencing the CoPS indexing work network; (2) Subjective factors directly influence the agencies’ selection of the optimal effort level for collaborative work, whereas environmental and structural factors indirectly impact their choice by affecting their network centrality; (3) the environmental factors within the indexing work network dictate the rate at which interagent interactions diminish with distance. To demonstrate and validate the research’s findings, an examination of the indexing process of the China Lanxin High-speed Railway is conducted. This study offers new insights into CoPS indexing work, providing both theoretical references and practical suggestions for project teams to improve collaborative efficiency.