Increase Of Noise Intensity Research Articles

Real-Time monitoring of sound levels during school sports events

Introduction: This study examines how impulsive noise impacts athlete performance and spectator behavior at school sports events, using 60 students divided into control and experimental groups to assess effects on reaction time, accuracy, and stress. Objective: to determine the extent to which impulsive noise impacts athletic performance and spectator reactions during school sports events. Methodology: participants were exposed to varying levels of noise during athletic tasks to measure reaction times, accuracy, and stress indicators. the study utilized controlled auditory environments to simulate real-world sports event conditions. Results: the findings indicate that elevated noise levels significantly impair athletic performance, with a strong correlation observed between increased noise intensity and heightened spectator enthusiasm. additionally, elevated noise was found to exacerbate stress in athletes, leading to reduced focus and performance consistency. Discussion: contrasting these results with existing research, the negative impact of noise on performance aligns with studies emphasizing the importance of optimal auditory conditions for sports activities. however, the increase in spectator enthusiasm suggests a complex interaction between environment and spectator behavior. Conclusions: implementing effective interventions, such as controlled cheering and sound monitoring technology, can create an environment that balances spectator excitement with the support needed for athlete concentration and performance, ultimately fostering a healthier and more supportive experience for all participants.

Synchronization resilience of coupled fluctuating-damping oscillators in small-world weighted complex networks

The mechanisms of synergy in complex networks have garnered significant attention across scientific disciplines. In this paper, we present a model of coupled oscillators with damping fluctuations within a small-world weighted complex network. We analyze the system’s asymptotic synchronization to derive conditions for asymptotic stability and evaluate the steady-state response. Our numerical simulations highlight the substantial impact of network weight heterogeneity and scale on asymptotic synchronization. We also examine asymptotic synchronization resilience under various removal strategies, revealing that increased noise intensity and lower switching rates reduce resilience. Notably, the removal of strong links poses the greatest vulnerability, while weak links have minimal impact. Interestingly, enhancing weight heterogeneity and scale can improve resilience in certain cases. Our results further show that heterogeneity accelerates synchronization speed, indicating a non-monotonic relationship with network scale. Ultimately, we confirm our theoretical findings and reveal intriguing generalized stochastic resonance (GSR) phenomena.

Noise-sensitivity during sleep in non-sedated mechanically ventilated patients is associated with weaning duration: a polysomnographic study.

Poor sleep is a major concern in intensive care units (ICUs), particularly in mechanically ventilated patients, because it is associated with longer duration of the weaning phase and higher mortality. High noise levels in ICUs are frequently reported by patients as one of the most disturbing sleep-disrupting factors but would be responsible for less than 20% of arousals. This suggests major inter-individual variability in noise sensitivity. Our objectives were to define and assess noise sensitivity in mechanically ventilated patients and to explore its association with sleep duration, sleep quality and weaning duration. We retrospectively re-analyzed polysomnographies (PSGs) recorded in 29 non-sedated patients, mechanically ventilated for at least 24h and difficult to wean (i.e. ≥ 1 spontaneous breathing trial failure). All the arousals were identified on all the PSGs. We calculated mean noise level and identified all noise peaks (an abrupt increase of noise intensity of more than 10 decibels (dBA)) preceding each arousal. Each 21-second period preceding each arousal was divided into seven 3-second bins. We built a pre-event time histogram for each PSG by counting the total number of noise peaks in each bin. If the total number of noise peaks in one bin exceeded the average of the seven bins plus 2 SD, we considered that there was a significant relationship between the number of noise peaks in this bin and the arousal. The patient was then considered as noise-sensitive. Presence of atypical sleep, proportion of sleep stages, and weaning duration were assessed. Nineteen out of 29 patients (66%) were noise-sensitive. Duration of weaning from ventilator was significantly longer in noise-insensitive patients (median [interquartile range] 2 [1-2] versus 5 [2-8] days; p < 0.01). Proportion of N1, N2, N3 sleep stages and rapid eye movement sleep were similar in noise-sensitive and noise-insensitive patients. In contrast, the proportion of patients displaying atypical sleep was higher in noise-insensitive patients. Our results report for the first time that most ICU patients were noise-sensitive. Lower noise sensitivity was associated with atypical sleep and could reflect lower brain reactivity to environment.

Suppressed possibilistic fuzzy c-means clustering based on shadow sets for noisy data with imbalanced sizes

Possibilistic fuzzy c-means clustering (PFCM) algorithm generates fewer overlapping clustering centers than the possibilistic c-means clustering (PCM) algorithm and possesses better noise immunity than fuzzy c-means clustering (FCM) algorithm. However, with the increasing noise intensity and number of clusters, PFCM still faces the problem of getting partially overlapping clustering centers or mislocated centers in noise regions. Moreover, the sample-size imbalance increasingly intensifies the difficulty of positioning centers of small clusters. To solve the above problems, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets (S-SPFCM) is proposed by introducing the shadow set theory and the "suppressed competitive learning" strategy. Firstly, KL divergence is introduced in the objective function of PFCM to increase the anti-noise robustness of fuzzy memberships against long-range noise and outliers. Secondly, to reduce the number of overlapping centers caused by possibilistic memberships, the shadow set theory is introduced to divide each class adaptively into three regions (core, shadow, and exclusion regions) by an uncertainty balance method. The suppressed competitive learning method is extended by modifying the memberships of points within the three regions, thus artificially guiding the iterative track of clustering centers. Meanwhile, to further reduce the influence of imbalanced sizes, a scheme to reset mislocated centers in the core and shadow regions is also designed. In addition, to improve the segmentation performance of S-SPFCM for noisy images, a suppressed possibilistic fuzzy c-means clustering algorithm based on shadow sets and local information (SL-SPFCM) is also proposed. The SL-SPFCM first improves the Euclidean distance by a distance filtering scheme. Then SL-SPFCM introduces the local median membership of each pixel into the KL divergence of the objective function. Finally, experiments on synthetic datasets and color images which are characteristic of imbalanced sizes and noise injection demonstrate the proposed S-SPFCM and SL-SPFCM algorithms achieve smaller center deviations and higher clustering accuracy compared with several state-of-the-art clustering algorithms.

Auditory Noise Facilitates Lower Visual Reaction Times in Humans.

Noise is commonly seen as a disturbance but can influence any system it interacts with. This influence may not always be desirable, but sometimes it can improve the system's performance. For example, stochastic resonance is a phenomenon where adding the right amount of noise to a weak signal makes it easier to detect. This is known as sub-threshold detection. This sub-threshold detection's natural fingerprint is the fact that the threshold values follow an inverse U-shaped curve as the noise intensity increases. The minimum threshold value is the point of maximum sensitivity and represents the optimal point that divides the dynamics in two. Below that point, we can find the beneficial noise branch, where the noise can facilitate better detection. Above that point, the common detrimental noise concept can be found: adding noise hinders signal detection. The nervous system controls the movements and bodily functions in the human body. By reducing the sensory thresholds, we can improve the balance of these functions. Additionally, researchers have wondered if noise could be applied to different senses or motor mechanisms to enhance our abilities. In this work, noise is used to improve human reaction times. We tested the hypothesis that visual reaction times decrease significantly when the subject's perception is in the beneficial noise branch and closer to the optimal point than outside of this condition. Auditory noise was introduced in 101 human subjects using an interface capable of searching for the right amount of noise to place the subject in the beneficial noise branch close to the optimal point. When comparing the results, the reaction times decreased when the subjects were at the optimal point compared to when the subjects were outside of such conditions. These results reveal the possibility of using this approach to enhance human performance in tasks requiring faster reaction times, such as sports.

Modeling uncertainty: the impact of noise in T cell differentiation

BackgroundThe regulatory mechanisms guiding CD4 T cell differentiation are complex and are further influenced by intrinsic cell variability along with that of microenvironmental cues, such as cytokine and nutrient availability.ObjectiveThis study aims to expand our understanding of CD4 T cell differentiation by examining the influence of intrinsic noise on cell fate.MethodologyA model based on a complex regulatory network of early signaling events involved in CD4 T cell activation and differentiation was described in terms of a set of stochastic differential equation to assess the effect of noise intensity on differentiation efficiency to the Th1, Th2, Th17, Treg, and TFH effector phenotypes under defined cytokine and nutrient conditions.ResultsThe increase of noise intensity decreases differentiation efficiencies. In a microenvironment of Th1-inducing cytokines and optimal nutrient conditions, noise levels of 3%, 5% and 10% render Th1 differentiation efficiencies of 0.87, 0.76 and 0.62, respectively, underscoring the sensitivity of the network to random variations. Further increments of noise reveal that the network is relatively stable until noise levels of 20%, where the resulting cell phenotypes becomes heterogeneous. Notably, Treg differentiation showed the highest robustness to noise perturbations. A combined Th1-Th2 cytokine environment with optimal nutrient levels induces a dominant Th1 phenotype; however, removal of glutamine shifts the balance towards the Th2 phenotype at all noise levels, with an efficiency similar to that obtained under Th2-only cytokine conditions. Similarly, combinations of Th1/Treg and Treg/Th17-inducing cytokines along with the removal of either tryptophan or oxygen shift the dominant Th1 and Treg phenotypes towards Treg and Th17 respectively. Model results are consistent with differentiation efficiency patterns obtained under well-controlled experimental settings reported in the literature.ConclusionThe stochastic CD4 T cell mathematical model presented here demonstrates a noise-dependent modulation of T cell differentiation induced by cytokines and nutrient availability. Modeling results can be explained by the network topology, which assures that the system will arrive at stable states of cell functionality despite variable levels of biological intrinsic noise. Moreover, the model provides insights into the robustness of the T cell differentiation process.

Personal Protective Equipment Compliance In Preventing Hearing Loss In Agro-Industrial Area

Background: In Indonesia the impact of noise that causes hearing loss is still high compared to other countries in Southeast Asia. Noise can cause hearing loss in workers if the scale of the noise is high and occurs continuously. It can also trigger a decline in health that will have an impact on the performance of workers. Objective: to Analysis of PPE Compliance in Preventing Hearing Loss in Agro-Industrial Area. Methods: The research in this study used the literature review method. This search strategy uses several databases such as Pubmed, ResearchGate, and Google Scholar. This search use keywords PPE, Hearing Loss, and Agro-industrial. Results: Increasing noise intensity, duration of work, and duration of exposure can cause hearing loss because the inner hair cells and supporting cells are damaged. The longer the working period, the greater the noise exposure it will receive. The use of PPE can effectively prevent noise-induced hearing loss. Conclusion: Workers exposed to noise are highly susceptible to hearing loss. It depends on the intensity of the noise, the duration of exposure, the sensitivity of the individual or the worker itself and the frequency of the noise. Noise can be prevented, one of which is by using personal protective equipment when working. In this case, nurses can educate related to the use of PPE to prevent hearing loss.

The diffusion behaviour of coupled particle rings driven by self-propelled particles in a two-dimensional reflection channel

Investigated the diffusion behavior of self-propelled coupled particle rings in a two-dimensional channel considering particle collisions. The channel geometry and noise regulation play crucial roles in directing transport within the system. Observed a significant alteration in the diffusion behavior of the particle rings at specific stages of the collision process, accompanied by corresponding changes in the diffusion coefficient. As the modulation phase shift increases, the mean square displacement (MSD) of the particle rings displays periodic fluctuations. The binding force between the particle rings partially restricts the growth of the MSD. An increase in white noise intensity enhances the diffusion behavior. The impact of self-propulsion speed is influenced by the modulation parameters. The sign of the modulation parameter dictates the correlation of the self-propulsion speed. Furthermore, the number of particle rings in the channel introduces a complex effect on the diffusion behavior.

Noise Enhances Excitability of a Neuronal Population with Heterogeneous Excitatory Neurons

AbstractCortical excitability is sensitive to noise perturbations despite homeostatic regulation. However, modeling the effect of noise in a heterogeneous neuronal population is a mathematical challenge. Using a mean‐field model of a randomly connected excitatory population, we investigated how noise affects excitability at the resting state. Stable equilibrium analysis revealed that increasing noise intensity enhances excitability, indexed by synaptic conductance. Further, this enhancement disappeared when excitability is sufficiently high due to strong mean input current, but not influenced by the connection probability of the population. Finally, the mechanism of noise‐induced enhancement was explained by eigenvalues moving toward the imaginary axis. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Bankruptcy mechanism based on accumulated payoff and age promotes cooperation in coevolutionary games

When individuals or companies are unable to meet their financial obligations, they may undergo the process of bankruptcy and go out of business. At the same time, new companies may arise. In this work, we propose a coevolutionary game model incorporated with bankruptcy. In the model, each agent represents a company. Two factors, accumulated payoff and age, are taken into account to determine its bankrupt probability. We assume two possible bankrupt mechanisms, procedural bankruptcy and age-dependent bankruptcy. Through numerical simulations, we show that the bankruptcy can effectively promote cooperation. Moreover, we find the non-monotonic behavior of the cooperation level with the increase of noise intensity in procedural bankruptcy. By investigating the strategy patterns and the distributions of the bankruptcy probabilities for cooperators and defectors, we provide explanations for the promotion of cooperation and the optimization of the cooperation level. This work highlights the positive effects of bankruptcy mechanism on cooperation in the real business world.

The transport of self-propelled ellipsoidal particles confined in 2D smooth corrugated channel

Directed transport of self-propelled ellipsoidal particles confined in a smooth corrugated channel with asymmetric potential and Gaussian colored noise is investigated. Effects of the channel, potential and colored noise on the system are discussed. Large noise intensity in the x-axis direction inhibits the transport in [Formula: see text] and [Formula: see text] directions. The directed transport speed [Formula: see text] has a maximum with increasing noise intensity in the y-axis direction. Proper size of the bottleneck is good for the directed transport of the ellipsoidal particles, but large or small size of bottleneck inhibits this directed transport. The transport reverse phenomenon appears with increasing load and self-propelled speed. Confined spherical particle is easier to produce directed transport than confined needlelike ellipsoid particle.

Influence of coupling topology and noise on the possibility of frequency tuning in ensembles of FitzHugh–Nagumo oscillators

Purpose. The study focuses on analyzing spike activity and synchronization in ensembles of FitzHugh-Nagumo neurons, both with and without external noise excitation. These networks can induce oscillations at different frequencies, depending on the excitability parameter of individual elements and the coupling strength between them. Additionally, variations in these parameters can lead to synchronization among the elements. The research investigates the dynamics of both a single-layer network, which includes a common element, and a three-layer network with an intermediate neuron-hub layer. Methods. To analyze the dynamics of the networks under investigation, we calculate the time-averaged spike frequencies of all elements. These frequencies are then averaged for each outer layer and compared with the frequency of the central element, as well as with each other in the case of a multilayer network. In order to assess the impact of coupling strength on the spike activity and synchronization of the network elements, we construct frequency distributions and frequency difference distributions in a plane of coupling strength coefficients. Results. It has been shown that small single-layer and three-layer networks of identical oscillators (FitzHugh-Nagumo neurons), with a simple coupling topology, can exhibit different spike activity in different parts of the system. In this case, the neurons transition to a self-oscillatory mode due to repulsive coupling between the elements. The research has established that in a single-layer network, a ring of elements can synchronize in frequency with the central element within a specific range of coupling strength values. In a three-layer system, layer synchronization can also be observed. Weak noise has minimal impact on the synchronization boundaries of all three layers, in terms of coupling parameters. However, as the noise intensity increases, this area decreases. At the same time, the noise leads to the emergence of a new synchronization region in which relay synchronization of the layers is observed in the absence of synchronization with the hub. Conclusion. The study explored the potential of exciting oscillations and achieving synchronization in single-layer and three-layer networks of coupled FitzHugh-Nagumo oscillators. The coupling strength between the elements varied in order to investigate its impact. Although the study only provided a broad understanding of the spike activity of excitable neurons in the two network models examined, it adequately demonstrated the crucial role of coupling in the spiking activity of these neurons.

Colored noise in river level oscillations as triggering factor for unstable dynamics in a landslide model with displacement delay

In the present paper we examine the effect of the noise in river level oscillation on the landslide dynamics. The analysis is conducted in several phases. In the first phase, we analyze the multi-annual level oscillation of the Kolubara and the Ibar river (Serbia). Based on the observed dataset, we suggest a deterministic model for the river level oscillation with the additional contribution of the noise part, which we confirm to have the properties of colored noise. In the second phase of the research, we introduce the influence of the river-level oscillation, with the included effect of colored noise in the spring-block delay model of landslide dynamics. Results of the research indicate conditions under which the effect of river noise has both stabilizing and destabilizing effects on the landslide dynamics. The effect of noise intensity D and correlation time ε is systematically analyzed in interaction with delayed interaction, spring stiffness and friction parameters. It is determined that the landslide dynamics is sensitive to the change of noise intensity and that the increase of noise intensity leads to onset of unstable landslide dynamics. On the other hand, results obtained indicate that the examined model of landslide dynamics is rather robust towards the change of correlation time ε. Interaction of this parameter and some of the friction parameters leads to stabilization of landslide dynamics, which confirms the importance of the influence of the noise color in river level oscillations on the landslide dynamics.

Noise intensity modulates the responses of mantled howler monkeys to anthropophony.

Anthropogenic noise is a major global pollutant but its effects on primates are poorly understood, limiting our ability to develop mitigation actions that favor their welfare and conservation. In this study, we used an experimental approach to determine the impact of variation in noise intensity on mantled howler monkeys (Alouatta palliata). We conducted the study at Los Tuxtlas (México), where we studied the physiological stress (proxied via fecal glucocorticoid metabolites, fGCM) and behavioral responses of 16 males. We played back chainsaw noise at two intensities (40 and 80 dB) and used days in which groups were not exposed to noise as matched controls. With increased noise intensity fGCM increased, vigilance and vocalizations were longer, and vigilance, vocalizations, and flight occurred quicker. Physiological and behavioral responses occurred even after low-intensity noise playbacks (i.e., 40 dB). Therefore, noise intensity is a significant factor explaining the responses of mantled howler monkeys to anthropogenic noise. These results imply that management actions aimed at eradicating anthropogenic noise are required for the conservation and welfare of mantled howler monkeys at Los Tuxtlas.

PERFORMANCE ANALYSIS OF WIRELESS COMPUTER NETWORKS IN CONDITIONS OF HIGH INTERFERENCE INTENSITY

Context. The decrease in the probability of successful frame transmission in the infrastructure domain of IEEE 802.11 DCF wireless network is caused both by the influence of the collision intensity and by the impact of external interference in the radio path. Using the Markov chain approach as a baseline, we explicitly expressed the dependence of the network throughput on the number of operating stations, bit error rate (BER), and the frame fragmentation factor.&#x0D; Objective. The purpose of this article is to study the influence of interference intensity on the throughput of a wireless network domain in a wide range of the number of operating stations when transmitting frames of various lengths in the absence and with the use of the fragmentation mechanism.&#x0D; Method. The performed mathematical modelling showed, that in the range of increased and high noise intensity (BER = 10–5 – 10–4), a decrease in the length of the frame data field from the standard length of 12000 bits to 3000 bits is accompanied by a decrease in the throughput for all values of the number of competing stations. At the same time, it must be noted that as the amount of the frame data decreases, the throughput becomes less susceptible to an increase in the noise intensity. Qualitatively different results are obtained in the region of very high interference intensity (BER = 2·10–4). A significant increase in the probability of frame transmission in this region observed with a decrease in the standard length of the frame data field by 2–3 times, made it possible to increase the throughput compared to the original one. This effect is especially pronounced when the length is halved.&#x0D; Results. The study of the standard frame transmitting process, but with a fragmented data field, showed that if for BER = 5·10–5 and less with an increase in fragmentation factor, the throughput values decrease, in the entire range of the number of stations due to the predominant increase in overhead costs, then in the region of high (BER = 10–4) and very high noise intensity (BER = 2·10–4) we have the opposite effect. To the greatest extent, the throughput increases when the frame data is transmitted in two equal fragments. We have made a comparison of the network throughput determined by simply reducing the length of the frame data field and using fragmentation of a standard frame. The comparison showed that the use of the fragmentation mechanism is more beneficial both when throughput is stabilized under conditions of increased noise intensity and when the throughput is increased under conditions of high and very high noise intensity.&#x0D; Conclusions. In this article, a mathematical model has been modified for direct calculation of the wireless network throughput. Using this model, we studied the changes in throughput over a wide range of BER and a number of operation stations for various values of the transmitted frame fragmentation factor. The conditions for increasing the throughput are determined.

Impact of noise on the instability of spiral waves in stochastic 2D mathematical models of human atrial fibrillation.

Sustained spiral waves, also known as rotors, are pivotal mechanisms in persistent atrial fibrillation (AF). Stochasticity is inevitable in nonlinear biological systems such as the heart; however, it is unclear how noise affects the instability of spiral waves in human AF. This study presents a stochastic two-dimensional mathematical model of human AF and explores how Gaussian white noise affects the instability of spiral waves. In homogeneous tissue models, Gaussian white noise may lead to spiral-wave meandering and wavefront break-up. As the noise intensity increases, the spatial dispersion of phase singularity (PS) points increases. This finding indicates the potential AF-protective effects of cardiac system stochasticity by destabilizing the rotors. By contrast, Gaussian white noise is unlikely to affect the spiral-wave instability in the presence of localized scar or fibrosis regions. The PS points are located at the boundary or inside the scar/fibrosis regions. Localized scar or fibrosis may play a pivotal role in stabilizing spiral waves regardless of the presence of noise. This study suggests that fibrosis and scars are essential for stabilizing the rotors in stochastic mathematical models of AF. Further patient-derived realistic modeling studies are required to confirm the role of scar/fibrosis in AF pathophysiology.

The exact solutions of the variable-order fractional stochastic Ginzburg–Landau equation along with analysis of bifurcation and chaotic behaviors

This article explores the exact solutions of the variable order fractional derivative of the stochastic Ginzburg–Landau equation (GLE) using the G′G2-expansion method with the assistance of Matlab R2021a software. The paper presents three key aspects that contribute to its novelty: (1) Our study introduces and examines the variable order fractional derivative of the stochastic Ginzburg–Landau equation (VOFDSGLE) for the first time, demonstrating our best cognitive effort. We successfully obtain a significant number of exact solutions and provide illustrative examples and visual representations of the VOFDSGLE. These obtained solutions have the potential to offer enhanced availability and practicality in understanding the mechanisms behind the complex physical phenomena observed in various fields. (2) Additionally, we investigate the phase portraits of the variable-order fractional stochastic Ginzburg–Landau equation under a specific condition. We analyze the associated sensitivity and chaotic behaviors, which have not been examined in prior studies on stochastic Ginzburg–Landau equation (Mohammed et al., 2021; Alhojilan and Ahmed, 2023). This exploration adds a unique contribution to the existing literature and offers valuable insights into the dynamics of the system. (3) Our numerical observations underscore the significant impact of multiplicative noise on the solutions. The progressive degradation of patterns and the tendency towards planar surfaces indicate the disruption caused by increasing noise intensity. Moreover, the convergence of solution magnitudes to zero underlines the stabilizing influence of the multiplicative noise. Furthermore, we advance the existing knowledge regarding the response of the stochastic GLE model to small noise, offering valuable insights into the system’s transition behaviors in the energy landscape.

Noise-induced dynamics and photon statistics in bimodal quantum-dot micropillar lasers

Emission characteristics of quantum-dot micropillar lasers (QDMLs) are located at the intersection of nanophotonics and nonlinear dynamics, which provides an ideal platform for studying the optical interface between classical and quantum systems. In this work, a noise-induced bimodal QDML with orthogonal dual-mode outputs is modeled, and nonlinear dynamics, stochastic mode jumping and quantum statistics with the variation of stochastic noise intensity are investigated. Noise-induced effects lead to the emergence of two intensity bifurcation points for the strong and the weak mode, and the maximum output power of the strong mode becomes larger as the noise intensity increases. The anti-correlation of the two modes reaches the maximum at the second intensity bifurcation point. The dual-mode stochastic jumping frequency and effective bandwidth can exceed 100 GHz and 30 GHz under the noise-induced effect. Moreover, the noise-induced photon correlations of both modes simultaneously exhibit super-thermal bunching effects () in the low injection current region. The -value of the strong mode can reach over 6 in the high injection current region. Photon bunching () of both modes is observed over a wide range of noise intensities and injection currents. In the presence of the noise-induced effect, the photon number distribution of the strong or the weak mode is a mixture of Bose–Einstein and Poisson distributions. As the noise intensity increases, the photon number distribution of the strong mode is dominated by the Bose–Einstein distribution, and the proportion of the Poisson distribution is increased in the high injection current region, while that of the weak mode is reduced. Our results contribute to the development preparation of super-bunching quantum integrated light sources for improving the spatiotemporal resolution of quantum sensing measurements and enhancing the security of optical communication.

Stochastic control of spiking activity bump expansion: Monotonic and resonant phenomena.

We consider spatially localized spiking activity patterns, so-called bumps, in ensembles of bistable spiking oscillators. The bistability consists in the coexistence of self-sustained spiking dynamics and a quiescent steady-state regime. We show numerically that the processes of growth or contraction of such patterns can be controlled by varying the intensity of multiplicative noise. In particular, the effect of noise is monotonic in an ensemble of coupled Hindmarsh-Rose oscillators. On the other hand, in another model proposed by Semenov et al. [Semenov et al., Phys. Rev. E 93, 052210 (2016)], a resonant noise effect is observed. In that model, stabilization of activity bump expansion is achieved at an appropriate noise level, and the noise effect reverses with a further increase in noise intensity. Moreover, we show the constructive role of nonlocal coupling that allows us to save domains and fronts being totally destroyed due to the action of noise in the case of local coupling.

Effects of noise on the critical points of Turing instability in complex ecosystems.

Noise is ubiquitous in natural and artificial systems. In a noisy environment, the interactions among nodes may fluctuate randomly, leading to more complicated interactions. In this paper we focus on the effects of noise and network topology on the Turing pattern of ecological networks with activator-inhibitor structure, which may be interpreted as prey-predator interactions. Based on the stability theory of stochastic differential equations, a sufficient condition for the uniform state is derived. The analytical results indicate that noise is beneficial for the uniform state. When the ratio between the diffusion coefficients of the predator and prey increases, the ecosystems can exhibit a transition from a uniform stable state to a Turing pattern, while when the ratio decreases, the ecosystems transit from a Turing pattern to a uniform stable state. There are two crucial critical points in Turing patterns, forward and backward. We find that both forward and backward critical points increase as the noise intensity increases. This means that noise favors a stable homogeneous state compared to a state with a heterogeneous pattern, which is consistent with the analytical results. In addition, noise can weaken the hysteresis phenomenon and even eliminate it in some cases. Furthermore, we report that network topology plays an important role in modulating the uniform state of ecosystems, such as the size of prey-predator systems, the network connectivity, and the strength of interaction.