This research paper investigates discrete predator-prey dynamics with two logistic maps. The study extensively examines various aspects of the system\'s behavior. Firstly, it thoroughly investigates the existence and stability of fixed points within the system. We explores the emergence of transcritical bifurcations, period-doubling bifurcations, and Neimark-Sacker bifurcations that arise from coexisting positive fixed points. By employing central bifurcation theory and bifurcation theory techniques. Chaotic behavior is analyzed using Marotto\'s approach. The OGY feedback control method is implemented to control chaos. Theoretical findings are validated through numerical simulations.

Compression systems based electromechanical actuators require a good understanding of their dynamics for a better performance. This paper deals with the study of the nonlinear dynamics of an electromechanical system with two rotating arms subjected to a sinusoidal excitation for fluid compression purposes. The physical model integrating two balloons to be compressed by the arms alternately is presented and the mathematical equations traducing their dynamics are established. We emphasize on the influence of some control parameters namely the supply voltage, the discontinuity position and the viscoelastic ratio on the behaviour of the angular displacement of the arms. The study is also done by neglecting the inductance in the electrical part of the system. It is obtained that while the arms exhibit periodic motion during regular movement, compression of the balloons induces a shift to multi-periodic or chaotic dynamics, occasionally reverting to periodicity. Experimental and numerical simulation results demonstrate good agreement, with the R-system approximating more experimental outcomes than the RL-system. These findings hold significant implications for various environmental applications utilizing pump technology.

Fractional-order (FO) chaotic systems exhibit random sequences of significantly greater complexity when compared to integer-order systems. This feature makes FO chaotic systems more secure against various attacks in image cryptosystems. In this study, the dynamical characteristics of the FO Sprott K chaotic system are thoroughly investigated by phase planes, bifurcation diagrams, and Lyapunov exponential spectrums to be utilized in biometric iris image encryption. It is proven with the numerical studies the Sprott K system demonstrates chaotic behaviour when the order of the system is selected as 0.9. Afterward, the introduced FO Sprott K chaotic system-based biometric iris image encryption design is carried out in the study. According to the results of the statistical and attack analyses of the encryption design, the secure transmission of biometric iris images is successful using the proposed encryption design. Thus, the FO Sprott K chaotic system can be employed effectively in chaos-based encryption applications.

Prenatal MRI plays an essential role in the evaluation of the head and neck. This article overviews technical considerations and both isolated and syndromic anomalies of the fetal calvarium, globes and orbits, ears, maxilla, mandible, and neck.

Cavity optomechanics provides a powerful platform for observing many interesting classical and quantum nonlinear phenomena due to the radiation-pressure coupling between its optical and mechanical modes. In particular, the chaos induced by optomechanical nonlinearity has been of great concern because of its importance both in fundamental physics and potential applications ranging from secret information processing to optical communications. This review focuses on the chaotic dynamics in optomechanical systems. The basic theory of general nonlinear dynamics and the fundamental properties of chaos are introduced. Several nonlinear dynamical effects in optomechanical systems are demonstrated. Moreover, recent remarkable theoretical and experimental efforts in manipulating optomechanical chaotic motions are addressed. Future perspectives of chaos in hybrid systems are also discussed.

OBJECTIVE: It is known that long-term stress leads to trauma and very often to depression. Usually, the diagnosis of depression is dealt with by psychiatrists who, based on conversations and questions, diagnose the patient\'s illness and condition. Unfortunately, this diagnosis is not always reliable. To prevent the development of disease, it is necessary to detect illness in a timely manner. One of the indications of the possibility of the onset of disease is a disturbance in the level of hormones in the body, especially cortisol. The purpose of this study was to develop a mathematical model for cortisol variation resulting from stress which would be useful in making conclusions about depressive states.
METHODS: Rapid changes in cortisol concentration, according to ultradian rhythms, which are much faster than the daily circadian rhythm, is modelled as a truly nonlinear oscillator. The mathematical model contains two coupled first order differential equations. The stress is modeled as a pulsating action, described with a periodic trigonometric function, and cortisol production as a cubic nonlinear one. Three models for cortisol variation are considered: 1) the pure nonlinear model, 2) the periodically excited system, 3) and the chaotic system. The results from the study are supported with experimental measurements.
RESULTS: Without stress, cortisol variation is of an oscillatory type with a constant steady-state amplitude. Intensive stress causes a resonant phenomenon in cortisol oscillatory variation. The occasion is short and is usually without consequences. For long stress periods deterministic chaos occurs which permanently changes the levels of cortisol. This phenomenon is an indicator of depression. Results from the suggested models are compared with experimentally obtained ones and good quantitative agreement is obtained.
CONCLUSIONS: The nonlinear oscillator is a good model for indication of depression. The model provides not only general conclusions, but also individual ones, if personal characteristics are taken into consideration. Response of the model depends not only on the input data related to stress, but also on the system parameters that specify each individual. Findings obtained from this study have implications for the medical diagnosis and treatment of depression.

OBJECTIVE: Airway glottic insufficiency, or glottal gap, may lead to a breathy voice quality. It is hypothesized that a glottal gap may be a source of nonlinearity in speech production. This study aims to gain a chaotic and acoustic profile of glottal gap voice provided by phonation of excised larynges subjected to the insertion of a metal shim in the posterior glottis.
METHODS: Nonrandomized quasi-experimental study.
METHODS: Posterior glottal gap varied from 0 to 3.5 mm in 0.5 mm intervals. Each treatment was investigated independently in a sample population of eight excised canine larynges. Phonation of the larynges for each treatment was recorded and analyzed for the cepstral peak prominence (CPP), harmonics-to-noise ratio (HNR), and correlation dimension.
RESULTS: Kruskal-Wallis rank-sum tests yielded significant differences across shim groups for all parameters. Dunn-Bonferroni post-hoc tests revealed that the control group differed significantly from the 1.5, 2, 2.5, 3, and 3.5 mm groups for all metrics. Moreover, Kendall correlation tests indicated a moderately positive correlation between glottal gap size and correlation dimension, a moderately negative correlation between glottal gap size and CPP and between glottal gap size and the HNR.
CONCLUSIONS: Glottic insufficiency provides a source of nonlinearity in phonation. Nonlinear dynamic analysis provides quantitative insight into glottal gap voice. This study encourages future studies to further evaluate the relationship between glottal gap and correlation dimension.

This paper describes a novel 4-D hyperchaotic system with a high level of complexity. It can produce chaotic, hyperchaotic, periodic, and quasi-periodic behaviors by adjusting its parameters. The study showed that the new system experienced the famous dynamical property of multistability. It can exhibit different coexisting attractors for the same parameter values. Furthermore, by using Lyapunov exponents, bifurcation diagram, equilibrium points\' stability, dissipativity, and phase plots, the study was able to investigate the dynamical features of the proposed system. The mathematical model\'s feasibility is proved by applying the corresponding electronic circuit using Multisim software. The study also reveals an interesting and special feature of the system\'s offset boosting control. Therefore, the new 4D system is very desirable to use in Chaos-based applications due to its hyperchaotic behavior, multistability, offset boosting property, and easily implementable electronic circuit. Then, the study presents a voice encryption scheme that employs the characteristics of the proposed hyperchaotic system to encrypt a voice signal. The new encryption system is implemented on MATLAB (R2023) to simulate the research findings. Numerous tests are used to measure the efficiency of the developed encryption system against attacks, such as histogram analysis, percent residual deviation (PRD), signal-to-noise ratio (SNR), correlation coefficient (cc), key sensitivity, and NIST randomness test. The simulation findings show how effective our proposed encryption system is and how resilient it is to different cryptographic assaults.

Memristors are of great theoretical and practical significance for chaotic dynamics research of brain-like neural networks due to their excellent physical properties such as brain synapse-like memorability and nonlinearity, especially crucial for the promotion of AI big models, cloud computing, and intelligent systems in the artificial intelligence field. In this paper, we introduce memristors as self-connecting synapses into a four-dimensional Hopfield neural network, constructing a central cyclic memristive neural network (CCMNN), and achieving its effective control. The model adopts a central loop topology and exhibits a variety of complex dynamic behaviors such as chaos, bifurcation, and homogeneous and heterogeneous coexisting attractors. The complex dynamic behaviors of the CCMNN are investigated in depth numerically by equilibrium point stability analysis as well as phase trajectory maps, bifurcation maps, time-domain maps, and LEs. It is found that with the variation of the internal parameters of the memristor, asymmetric heterogeneous attractor coexistence phenomena appear under different initial conditions, including the multi-stable coexistence behaviors of periodic-periodic, periodic-stable point, periodic-chaotic, and stable point-chaotic. In addition, by adjusting the structural parameters, a wide range of amplitude control can be realized without changing the chaotic state of the system. Finally, based on the CCMNN model, an adaptive synchronization controller is designed to achieve finite-time synchronization control, and its application prospect in simple secure communication is discussed. A microcontroller-based hardware circuit and NIST test are conducted to verify the correctness of the numerical results and theoretical analysis.

Three-phase induction motors are widely used in various industrial sectors and are responsible for a significant portion of the total electrical energy consumed. To ensure their efficient operation, it is necessary to apply control systems with specific algorithms able to estimate rotation speed accurately and with an adequate response time. However, the angular speed sensors used in induction motors are generally expensive and unreliable, and they may be unsuitable for use in hostile environments. This paper presents an algorithm for speed estimation in three-phase induction motors using the chaotic variable of maximum density. The technique used in this work analyzes the current signals from the motor power supply without invasive sensors on its structure. The results show that speed estimation is achieved with a response time lower than that obtained by classical techniques based on the Fourier Transform. This technique allows for the provision of motor shaft speed values when operated under variable load.