Search Results (311)

Typically, chaotic sources are indirectly assessed through ciphertext-level metrics that cannot isolate the contribution of the source itself, or are rarely complemented with a comprehensive set of analyses specifically tailored to digital chaotic generators. Here, we propose a novel deterministic pseudorandom source based on a delta-kicked forced oscillator (DKO) and comprehensively test its discrete output in the exact binary format consumed by downstream ciphers. The architecture separates key-dependent parameter derivation, nonlinear evolution, transient removal, and digital output generation. An explicit 22-symbol external key is deterministically mapped to the internal DKO parameters, separating user-key capacity from numerical parameter precision. The operating regime is characterized by phase-space analysis, bifurcation diagrams, temporal evolution, and Lyapunov exponents, while statistical quality is evaluated using NIST SP 800-22, DIEHARDER, and TestU01 SmallCrush, Crush, and BigCrush, complemented by byte-distribution, autocorrelation, complexity, bit-plane, key-sensitivity, multi-key, and finite-precision analyses. The generator satisfied all applicable NIST criteria and all 15, 144, and 160 SmallCrush, Crush, and BigCrush statistics, respectively, while DIEHARDER reported no failures. Complementary analyses showed near-uniform distribution, negligible statistical memory, strong key sensitivity, consistent multi-key behavior, and no degradation under double precision. These results support the generator as a statistically well-validated and numerically robust candidate for chaos-based cryptographic architectures and provide a reference for future comparative evaluations. Full article

Alpine forest–grassland ecotones represent sensitive transition zones where drastic shifts occur in vegetation, soil properties and biotic communities. Nevertheless, how soil microbial community composition, inter-taxon interactions and community assembly processes respond to grassland–ecotone–forest transitions remains poorly understood. In this study, we investigated the shifts in soil bacterial and fungal communities along an alpine forest–grassland ecotone, and explored key edaphic drivers as well as underlying assembly mechanisms. Our results showed that bacterial α-diversity in forest soils was significantly higher than that in grassland soils, with 15.44% higher Chao1 index and 4.73% higher Shannon index. Adonis analysis revealed clear community compositional differentiation across grassland, ecotone and forest habitats, with the greatest divergence between grassland and forest, and the highest similarity between ecotone and forest. Species turnover dominated the β-diversity variation in both bacterial and fungal communities along the gradient. Bacterial network complexity, connectivity and cohesion reached maximum values in the ecotone, suggesting intensified potential interspecific interactions and elevated niche overlap for bacterial taxa within transitional habitats. Neutral community model and NST analyses indicated that bacterial community assembly was primarily governed by stochastic dispersal processes. By contrast, fungal communities were controlled by deterministic environmental filtering. Along the grassland-to-forest transition, shifts in host plant traits, mycorrhizal symbiosis and litter inputs intensified habitat filtering, resulting in the continuous simplification of fungal network structure, decreased cohesion and rising modularity. Soil pH, electrical conductivity and total phosphorus acted as core edaphic determinants for bacterial communities, whereas fungal communities were additionally sensitive to ammonium nitrogen. The ecotone functioned as an important ecological buffer zone: stochastic dispersal promoted complex and stable bacterial co-occurrence networks, while deterministic filtering reshaped fungal community structure and inter-taxon associations. This work highlights the divergent assembly strategies of bacteria and fungi across alpine ecotone gradients, and provides insights for understanding microbial community shifts and biotic stability in mountain ecotone ecosystems. Full article

We present a dynamical systems analysis of ion-acoustic waves in a forced plasma system, focusing on chaos, multistability, and stochastic sensitivity as functions of the Mach number. Both deterministic and stochastic versions of a reduced third-order nonlinear oscillator are studied in the subsonic, sonic, and supersonic regimes. Three-dimensional attractor reconstruction reveals a transition from periodic islands in the subsonic regime to fully developed chaos at supersonic Mach numbers. Basin geometry analysis identifies the Mach number as a structural bifurcation parameter, while Monte Carlo basin stability confirms genuine multistability. An analytical framework is developed, including fixed-point stability analysis, a center-manifold reduction with its stochastic extension, a stability map, and effective-potential analysis, all identifying a pitchfork bifurcation at the sonic threshold. Rigorous error bounds for the forced versus unforced solutions are derived. Poincaré maps, entropy measures, recurrence plots, surrogate testing, and the Kaplan–Yorke dimension confirm the increasing dynamical complexity with Mach number, and a parameter-space bifurcation analysis identifies the sonic threshold as the organizing center of the transition to chaos. Stochastic comparisons show that additive noise produces large-amplitude deviations, and the final-state distributions of the position and velocity variables are not Gaussian. Full article

Predator–prey interactions are governed not only by direct consumption but also by behavioral responses of prey to the perceived risk of predation. In this work, we formulate a discrete-time predator–prey model in which predator-induced fear modifies prey reproduction through an exponential suppression mechanism, while predator consumption follows a Holling type-II functional response. The resulting map combines Ricker-type prey growth, density-dependent predation, and a non-consumptive effect associated with predator presence. We establish fundamental dynamical properties of the system by proving positivity, boundedness, and persistence under appropriate parameter restrictions. The existence of biologically meaningful equilibria is determined, and their local behavior is characterized using the Jacobian matrix and Jury stability criteria. To examine the influence of ecological parameters on the dynamics, we employ a global sensitivity analysis based on the Partial Rank Correlation Coefficient (PRCC) approach. The analysis identifies the parameters that most strongly affect prey and predator abundance. Numerical investigations reveal a wide range of dynamical regimes, including stable coexistence, periodic oscillations, higher-period attractors, quasi-periodic motion, and chaos. In particular, flip and Neimark–Sacker bifurcations are observed as ecological parameters vary. Basin-of-attraction computations and two-parameter iso-spike diagrams further demonstrate the presence of multistability and strong dependence on parameter combinations and initial conditions. Finally, period-doubling control and pole-placement techniques are employed to suppress undesirable chaotic oscillations and recover stable coexistence. The results emphasize that predator-induced fear can substantially modify population fluctuations and may act as an important mechanism regulating coexistence and complex dynamics in discrete ecological systems. Full article

High shares of renewable generation make intraday dispatch sensitive to forecast error, reserve deliverability, and credible equipment outages. This paper presents a Polynomial Chaos Expansion (PCE)-guided reliability-aware reserve policy that coordinates thermal reserve, withheld wind-power headroom, and fast-start backup. The PCE output shapes the hourly probabilistic reserve band, whereas loss-of-load probability (LOLP) and expected energy not supplied (EENS) are independently evaluated on held-out scenarios. A paired six-policy ablation separates the contributions of PCE shaping, wind headroom, and fast-start backup. The results show that wind headroom without firm backup is not equivalent to thermal reserve, whereas adding fast-start backup restores reliability and yields the least-cost reliable policy; relative to the feasible PCE thermal-only policy, the complete joint policy reduces held-out cost by 7.59% while retaining a comparable LOLP. In a posterior unit-outage trajectory, the joint policy markedly reduces unserved energy through fast-start deployment and available wind headroom. A separate exact-dual Benders benchmark reaches the same objective as the single-cut benchmark with fewer iterations and less recorded CPU time. Full article

Existing chaos-based image ciphers may suffer from limited dynamical complexity in low-dimensional maps and insufficient diversification of encryption-control sequences across different encryption instances. To address these limitations, a one-dimensional logarithmic chaotic map and a plaintext-dependent encryption scheme with dynamic DNA point mutation are presented. The map combines logarithmic stretching with modular folding and exhibits persistent complex dynamics over the investigated parameter interval. Compared with the logistic map and the one-dimensional Cosine Logistic compound map (1DCLC), it shows a broader positive-Lyapunov-exponent region and more stable normalized permutation entropy, while derived binary sequences satisfy the adopted NIST SP 800-22 criteria. The encryption architecture integrates block-reversal permutation, dynamic DNA encoding, involutive point mutation, and chaotic XOR masking. A SHA-512 plaintext digest, a fresh 128-bit public nonce, and a 256-bit master key are processed through HMAC-SHA-256 and HKDF-SHA-256 to derive the chaotic parameters. Experiments on standard grayscale images show near-uniform ciphertext distributions, negligible adjacent-pixel correlations, near-maximal entropy, differential characteristics close to theoretical references, and pronounced one-bit key sensitivity. The MATLAB implementation achieves encryption and decryption throughputs of approximately 1 MB/s. Full article

Dripping faucets exhibit chaotic dynamics with order–chaos transitions and multi-field coupling. This study built a physics-informed neural network (PINN) model using high-speed camera and numerical simulation data. The model incorporates physical constraints and employs multi-dimensional verification to simulate chaos and ensure consistency with the real system. Strategies including yargeted preprocessing, feature extraction, and physics enhancement alleviate the scarcity and poor quality of chaotic data. The PINN with an adapted network structure significantly improves prediction accuracy and generalization, boosting computational efficiency by over 100 times. More precisely, the inference speedup relative to FEM reached ~3800× under comparable resolution; this refers to prediction (inference) time, not training time. Long short-term memory (LSTM) performed best in predicting chaotic regions, achieving a 0.92 correlation between predicted and actual maximum Lyapunov exponents. A multi-step prediction strategy, physics-constrained loss function, and comprehensive verification framework ensure long-term prediction accuracy and physical consistency. Out-of-distribution validation on unseen fluids (diethylene glycol, glycerol–diethylene glycol) and extrapolated flow rates confirmed that the PINN retained 87–91% accuracy under OOD conditions, versus 71–78% for the LSTM baseline. A systematic sensitivity analysis further demonstrated that the loss-function weighting coefficients occupied a robust near-optimal plateau. The model comparison is fully quantitative, with all metrics reported alongside 95% bootstrap confidence intervals and a small-data learning curve demonstrating the PINN’s advantage in data-scarce regimes. Deep learning-based chaos identification and Lyapunov exponent estimation accurately capture the system’s chaotic characteristics. Computational optimization, hybrid precision training, distributed strategies, and model compression enhanced the simulation efficiency and indicate the feasibility of deployment. This study demonstrates that machine learning can effectively reveal the nonlinear dynamic behavior of dripping faucet systems, offering a novel approach for complex fluid dynamics and related applications. Full article

Halo-gravity traction (HGT) is widely used as a preoperative treatment for severe pediatric spinal deformities by gradually applying traction forces through a cranial fixation system. Although the clinical effectiveness of HGT has been extensively documented, uncertainty associated with the structural response and operational behavior of mobile HGT systems has received comparatively little attention. This study presents a preliminary coupled clinical–structural uncertainty quantification framework for engineering evaluation of a mobile halo-gravity traction wheelchair. Reduced-order structural and clinical surrogate models are combined with Latin Hypercube Sampling, bounded beta-distributed input variables, third-order Polynomial Chaos Expansion, and Sobol sensitivity analysis. Sixteen uncertain patient, operational, geometric, fabrication, and halo-interface parameters are propagated through models of combined beam–column loading, elastic buckling, welded-joint loading, scenario-based wheelchair tipping stability, halo-pin load concentration, traction delivery, and representative clinical response. Thirty-two alternative beta-distribution shape cases are examined to assess sensitivity to the assumed marginal distribution shapes. Within the investigated bounds and reduced-order modeling assumptions, the computational screening model did not predict loss of the prescribed structural margins. The results identify the parameters governing the different response modes and demonstrate how uncertainty in mobility-related acceleration, support geometry, traction loading, fabrication efficiency, and halo-pin load transfer can be evaluated within a unified framework. Because the model has not yet been validated through dedicated prototype testing or higher-fidelity full-system simulation, the results should be interpreted as uncertainty-informed engineering screening rather than as experimental verification or clinical certification of device safety. Full article

In this paper, the nonlinear Nurshuak–Tolkunay–Myrkakulov (NTM-III) equation is analyzed using the methods of dynamical systems theory and exact solution techniques. The NTM-III model is reduced to a second-order nonlinear ordinary differential equation by using appropriate reductions, then written as a planar Hamiltonian dynamical system. Analytical study of the equilibrium points, bifurcation structures, and stability properties is conducted using Jacobian matrices and eigenvalue theory. Different parameter combinations are shown in various phase portraits, with the saddle and center equilibrium states present and their stability indicated. An external periodic perturbation is added to the system to study complex nonlinear dynamics. Using phase portraits, time-series analysis, return maps, Lyapunov exponents, sensitivity analysis, and multistability diagnostics, the resulting forced dynamical model is investigated. Chaotic behavior, strong dependence on initial conditions, and multiple coexisting attractors for the same set of parameters are illustrated through numerical simulation. In addition, a set of exact analytical solutions, including trigonometric, hyperbolic, and exponential wave structures, is obtained using the Multivariate Generalized Exponential Rational Integral Function (MGERIF) method. The solutions obtained display interesting nonlinear wave interactions, multi-peakon formations, and localized propagation patterns. The findings show the complex relationship among bifurcation, chaos, multistability, and nonlinear wave propagation in the NTM-III equation and provide new insight into the equation’s mathematical and physical properties. Full article

Airborne bacteria exhibit high taxonomic diversity and a well-documented capacity to influence human health through pathogenic infection, allergenic sensitization, and immunomodulatory or commensal exposure. However, characterization of the airborne bacterial community structure, and particularly the seasonal dynamics, size-resolved distribution, and functional potential of predicted potential pathogens in Qingdao, a coastal megacity on the Yellow Sea, remains limited. Results revealed pronounced seasonal variation in community structure: Staphylococcus, Lactobacillus, Escherichia–Shigella, and Klebsiella were consistently dominant across all samples. LEfSe analysis identified Staphylococcus as significantly enriched in autumn, supporting its utility as a robust seasonal biomarker (LDA > 3.5, p < 0.05). This genus is predominantly associated with human sources and comprises opportunistic pathogens, thereby posing a potential risk to human health. Significant compositional divergence between summer and autumn communities was observed as a higher beta deviation index, whereas there were no statistically significant differences between particle size groups. The Chao and Shannon indices were consistently higher in PM₁₀ than in PM_2.5 across all seasons; however, this size-associated difference reached statistical significance only for autumn PM₁₀ (p < 0.05), indicating a seasonally amplified effect of coarse particulate matter on microbial richness and evenness. These analyses provide new insight into the total airborne bacterial and potential pathogens in an urban coastal environment. Full article

We apply the free energy principle to free markets by treating the Market as a random dynamical system with an attracting set, i.e., some characteristic states. This licenses a normal form for stochastic dynamics that inherits from the Helmholtz–Hodge decomposition. Equipped with this functional form—and a suitable parameterization—one can create a generative model of fluctuations in the value of assets and accompanying indicator variables. This affords the opportunity for prospective (ex ante) prediction, scenario modelling and forecasting that could, in principle, be applied to any complex dynamical system exhibiting stochastic chaos. Here, we illustrate the application to portfolio management—in the context of financial services—and use the (posterior) predictive densities over future paths to evaluate the expected free energy that underwrites active inference. In this application, active inference reduces to risk-sensitive control, which can be used to model the optimal decision-making of an agent or investor. In this setting, an investor is characterized by their prior preferences for a high rate of return under drawdown constraints. Using numerical studies and historical financial data, we quantify the improvement in portfolio management, relative to baseline policies. Full article

Fractional memory can substantially alter the period-doubling route to chaos, but the joint effects of fractional order q and memory length M on successive bifurcations in the finite-memory Grünwald–Letnikov (GL) fractional logistic map remain unclear. We formulate the period-doubling bifurcations of its exact periodic orbits as an exact Floquet problem. An algebraically equivalent periodic–antiperiodic phase reduction lowers the nonlinear system from $mM+M+1$ to $2m+1$ unknowns, making very long memory lengths computationally accessible. The reduced formulation reproduces an independently derived fixed-point bifurcation boundary with a maximum absolute discrepancy of $2.67\times {10}^{-15}$ and is further confirmed for successive doublings of period-4, period-8, and period-16 orbits. We compute the first seven cascade levels for orbit periods $m=1,2,4,8,16,32,64$ and memory lengths up to $M=2\times {10}^6$. Increasing M shifts these bifurcation points toward smaller values of the bifurcation parameter, with markedly stronger shifts at lower q. The dependence on q is nonmonotone: decreasing q first shifts the bifurcations toward smaller values and then reverses this trend. For $m=1,8,32$, the turnover locations move from $q\approx 0.46–0.49$ at $M=100$ to $q\approx 0.24–0.25$ at $M=2\times {10}^6$. The $O\left(M^{-q}\right)$ decay of the GL memory tail explains the stronger memory-length effect at low q, while the positive near-classical sensitivities and the fixed-memory $q\to 0^{+}$ limit explain the turnover. These results provide an exact and scalable framework for quantifying how finite fractional memory reorganizes the period-doubling cascade. Full article

As key drivers of soil biogeochemical cycles, soil microbial communities play essential roles in maintaining soil fertility, nutrient cycling, and plant growth. In this study, high-throughput sequencing of 16S rRNA and ITS genes was used to investigate the diversity, the composition, and the driving factors of bacterial and fungal communities in bulk soils across four soil groups collected from different vegetation covers (forest soil (FS), soybean field (PGS), and two Atractylodes japonica cultivation soils (ALO and ALR)) under identical climatic conditions. The results showed that the bacterial α-diversity remained stable across all the vegetation types, whereas the fungal α-diversity and richness were more sensitive to the vegetation type, with the PGS generally exhibiting lower Shannon and Chao1 indices. The β-diversity analysis revealed significant differences in the microbial community structure among the vegetation types, with a stronger effect on fungi (R² = 0.737, p = 0.001) than on bacteria (R² = 0.493, p = 0.001). At the phylum and genus levels, the fungal communities displayed more pronounced shifts than the bacterial communities, which remained relatively stable. A redundancy analysis indicated that the soil chemical properties significantly shaped the microbial community structure (p = 0.002). The microbial communities in the A. japonica soils (ALO and ALR) were primarily driven by pH, available phosphorus, and available potassium, while the FS and PGS communities were more strongly influenced by soil organic carbon, total nitrogen, and nitrogen forms (NH₄⁺-N and NO₃⁻-N). The Spearman correlation and functional prediction analyses further confirmed that the key soil factors differentially regulated the abundance and ecological functions of the dominant microbial taxa. These findings demonstrate the vegetation-specific assembly of soil microbial communities and highlight the distinct edaphic drivers associated with A. japonica cultivation, providing a scientific basis for soil health management and the sustainable cultivation of this medicinal plant. Full article

Direct-acting antivirals (DAAs) cure most chronic hepatitis C virus (HCV) infections, yet whether the gut microbiota returns toward a healthy state after viral clearance remains uncertain. We systematically reviewed DAA-era adult HCV studies using sequencing-based fecal microbiota assessment (PROSPERO CRD420261374682). Longitudinal alpha-diversity change was pooled by REML random-effects meta-analysis with Hartung–Knapp adjustment, and compositional/functional findings were synthesized narratively. Seven studies met qualitative criteria and five longitudinal reports were extractable. Because participant overlap between two Thai reports could not be excluded, the primary conservative non-overlap analysis of four reports (180 paired observations) gave Hedges’ g = 0.13 (95% CI −0.53 to 0.80; I² ≈ 89%); the five-report sensitivity estimate was directionally positive but imprecise (g = 0.35, 95% CI −0.38 to 1.07). Recovery concentrated in richness (Chao1), whereas evenness-weighted diversity (Shannon, Hill) barely moved; beneficial taxa such as Faecalibacterium and Blautia increased after SVR. These findings are consistent with uneven, richness-led microbial recovery, but the overall certainty of the pooled evidence is very low (GRADE); functional and clinical recovery remain insufficiently characterized and undemonstrated. Full article

Culicoides biting midges are important veterinary vectors, yet reported distributional patterns in Türkiye may reflect both ecological structure and uneven sampling. We synthesized 58 eligible Türkiye-specific reports (50 database-derived and eight citation-traced), consolidated them into 43 study/data-source families, and compiled 1164 taxon records representing 62 taxa. The all-explicit province dataset included 33 provinces and 45 taxa, whereas the primary province dataset included 18 provinces and 43 taxa. National taxonomic sources supported 72 species within the eligible evidence chain and 74 under a later taxonomic interpretation. Reported richness was strongly associated with sampling effort (Poisson coefficient = 0.579, 95% CI: 0.385–0.773), whereas biogeographical region was not significant after effort was accounted for (likelihood-ratio p = 0.914). Jaccard PERMANOVA identified an exploratory regional compositional pattern (F = 4.388, R² = 0.369, p = 0.001; PERMDISP p = 0.325), with turnover accounting for approximately 78% of mean between-region dissimilarity. Most richness diagnostics ranged from 54 to 64 taxa, whereas Chao2 was unstable (69.6; 95% CI: 49.5–152.0). Vector-related evidence remained insufficient for species–pathogen risk modeling. Overall, the synthesis is consistent with a Wallacean shortfall strongly associated with uneven sampling and supports standardized, biogeographically balanced surveillance to test transition-zone structure, climate-sensitive phenology and redistribution, and integrated vector-host-pathogen hypotheses under comparable sampling effort. Full article