Band 15, Heft 1

This work presents the development of a two-species prey and predator model in the context of global warming. Studies indicate prey’s growth rate and predator anxiety are inversely correlated. Furthermore, evidence supports as global warming increases, prey’s carrying capacity may decline. Predators are thought to be able to work together to pursue prey. Furthermore, current thinking holds that the direction of the wind may affect how much prey the predator consumes. Scholars propose that when global warming increases, predator growth rates may decline. Additionally, experts argue that the intraspecies competition among predators, which is reliant on the density of the current prey, may result in a decline in the number of predators. It is widely accepted that the rate of global warming is constant. Furthermore, the prevailing view is that both predator and prey species may be involved in the rise in global warming. It is commonly understood that several industrial and ecological measures could reduce global warming. The model’s solution’s positivity and boundedness have been examined. Various equilibrium points are assessed, and the system’s stability is examined around these places. The Hopf bifurcation around the positive equilibrium point is investigated. Via numerical simulation and testing on a virtual data set, all theoretical results are experimentally validated.

The paper illustrates traffic flow dynamics using partial differential equations derived from data using the Physics-Informed Information Criterion technique [1]. Thus, the aim is to explain the effect of various noise levels by varying the convection term and diffusion coefficients over time. First, we analyze the model using the Lie symmetry approach and also obtain an optimal system. Using these optimized systems, traffic flow densities are calculated to study the effect of noisy data on the results. In addition, the flow of traffic densities is obtained via a traveling wave. Kink-type graphical representations are obtained, which aid in traffic congestion prediction. By understanding and predicting traffic behavior under certain noise levels, this approach significantly contributes to traffic management strategy development.

This research employs two analytical techniques, the modified Kudrynshov and the modified alternative G ′ G $\left(\frac{{G}^{\prime }}{G}\right)$ -expansion method, to investigate the soliton solutions of the well-known Zoomeron (Z) model, which arises in plasma physics, nonlinear optics, and fluid dynamics. This yields various soliton outcomes with distinct dynamic patterns, including bright solitons, localized waves, singular breather waves, kink and anti-kink patterns, and multi-breather waveforms. We attach three-dimensional, density, and two-dimensional curves to highlight the visual dynamics pattern of the outcomes. After that, we investigate equilibrium points in different scenarios to verify their stability. We also present phase portraits and various chaos assessment tools, such as return maps, Lyapunov exponents, strange attractors, and multistability, to confirm the presence of chaotic patterns in the proposed model. The results of this research will have significant implications for the future of advanced non-linear phenomena.

The explosive growth of Internet of Things (IoT)-driven imaging in medicine, city surveillance, and intelligent infrastructure requires secure, timely transportation of delicate visual information with salient information, like faces and diagnostically important medical areas. Standard block ciphers, including AES, are unable to consistently retain these attributes under burst errors, partial data corruption, or focused cropping. In this paper, we introduce a lightweight substitution–permutation network (SPN) oriented encryption paradigm purpose-built for salient information secrecy in resource-limited IoT applications. We integrate permutation-driven block shuffle by chaos, recurrent neural network (RNN)-guided nonlinear static S-box generation, and bit-parity scrambling at the bit level to improve confusion–diffusion properties. We demonstrate experimental results of NPCR > 99.60 %, UACI > 33.40 %, near-zero correlation, and satisfactory key sensitivity. The technique maintains integrity of the salient region even with 50 % pixel loss, with throughput acceptable for real-time applications. Compared with previous work on lightweight approaches, we provide improved salient feature retention and lower computational complexity, and thus an ideal solution to security-critical applications of IoT-driven imaging.

The study proposes an anti-interference method based on Radio Frequency Identification (RFID) technology to solve the shortcomings of current wireless passive sensors on signal reception strength and anti-interference capability. The new method improves the signal recognition capability of RFID technology by introducing the blind equalization algorithm, and further enhances the security and anti-interference effect through frequency authentication technology. The results showed that the designed method significantly improved the anti-interference performance and transmission distance of the sensor signal, and had better stability and reliability than that of the traditional method. This designed method has significant practical significance in fields such as Industrial Internet of Things and smart cities. It can effectively improve the accuracy and reliability of sensor data, enhancing the efficiency and safety of the entire system, and laying a foundation for achieving more intelligent and efficient wireless communication systems and Internet of Things applications.

In the increasingly dominant consumer market of contemporary e-commerce, it has become urgent for enterprises to have a deeper and more comprehensive understanding of their e-commerce customers. A new method combining the K-means clustering algorithm with a backpropagation neural network has been proposed in the study. Specifically, customer data are first preprocessed through K-means clustering. Then, the initial weight distribution of the data is determined based on the clustering results to optimize the initial weight setting of the backpropagation neural network, thereby accelerating convergence during model training and effectively avoiding the problem of getting stuck in local optima. The results showed that the use of K-means clustering on customer information had a greater impact on the prediction ability of the algorithm model. The Geometric Means (G-means) and F1-values of the prediction model combining K-means clustering and backpropagation neural network were about 15 % and 31 % higher than those of the C4.5 decision tree algorithm, Support Vector Machine (SVM), and Logistic Regression algorithms, respectively. The area under the curve, accuracy, precision, and recall were 0.045, 0.037, 0.042, and 0.021 higher than those of backpropagation neural networks, respectively. The experimental results meet expectations, indicating that the proposed model is suitable for predicting customer churn in e-commerce.