Search Results (68)

Background: Coronary bifurcation lesions represent one of the most technically demanding scenarios in coronary artery disease (CAD), associated with higher procedural complexity, restenosis, and periprocedural complications. Recent advances in coronary computed tomography angiography (CCTA) have markedly improved its ability to visualize complex coronary anatomy, assess plaque morphology, and guide revascularization. Objectives: This review summarizes (1) technological advances in CCTA over the last decade, (2) its role in evaluating bifurcation stenosis, (3) assessment of plaque morphology and distribution, (4) quantification of bifurcation geometry, and (5) emerging evidence supporting its application in revascularization planning and guidance. Findings: Modern wide-detector and dual-source CT systems, iterative and deep-learning reconstruction algorithms, and photon-counting CT (PCCT) have significantly improved temporal and spatial resolution, reduced blooming artifacts, and lowered radiation dose. CCTA now reliably quantifies bifurcation stenosis and plaque distribution, characterizes high-risk plaque features, and accurately measures bifurcation angles. The integration of CT-derived fractional flow reserve (FFR-CT) and artificial intelligence (AI)-based plaque quantification further strengthens its diagnostic and prognostic performance. CCTA-derived bifurcation scores and 3D modelling support procedural strategy selection, stent sizing, and side-branch (SB) protection. Conclusions: CCTA has evolved into a comprehensive tool for non-invasive diagnosis, physiological assessment, and pre-procedural planning of bifurcation disease. With the advent of PCCT and AI-enhanced quantitative tools, CCTA is poised to become a central component of revascularization decision-making in complex coronary bifurcations. Full article

The linearization of wideband power amplifiers is critical for modern communication systems, yet modeling their severe nonlinearities and dynamic memory effects presents a significant data engineering challenge. Traditional polynomial models suffer from the curse of dimensionality, whereas deep neural networks entail high computational complexity and unstable convergence. To address these limitations, this paper proposes a novel physics-informed lightweight architecture, termed DPD-BLS, which integrates a block-oriented time delay structure with the Broad Learning System. Recognizing the distinct physical behaviors of radio frequency signals, the proposed model initially extracts temporal memory features and structurally decouples the signal magnitude and phase. To overcome the precision constraints of purely stochastic mapping in standard broad learning, we introduce an attentive dual-stream mapping module. This bifurcated architecture combines frozen random nodes for expansive state-space exploration with adaptive learnable nodes for precise error compensation, dynamically aggregating the most effective basis functions. Furthermore, an adaptive gating mechanism is incorporated to regulate nonlinear feature fusion, ensuring robust training stability. Comprehensive experiments demonstrate that the DPD-BLS achieves superior linearization performance while maintaining strict structural simplicity, offering a highly efficient data modeling paradigm for real-time edge deployment. Full article

This paper proposes a diffusive predator–prey model with double time delays and prey refuge, incorporating an additive Allee effect. First, we analyze the stability of boundary equilibrium, and the impact of Allee effect on the stability at boundary equilibrium is explored. Then we study the mechanisms by which the prey refuge influences the non-spatial system at positive equilibrium, revealing that under varying prey refuge coefficients, the system can exhibit stability, periodic oscillations or extinction. Subsequently, the occurrence conditions for the Hopf bifurcation are analyzed in a delayed system, and the direction and stability of Hopf bifurcation are obtained via the reaction–diffusion normal form theory. Finally, numerical simulations are carried out to verify our theoretical findings. Under the combined effect of maturation delay and digestion delay, spatio-temporal steady patterns and periodic patterns are observed in a reaction–diffusion system. Moreover, studies reveal that the Allee effect and prey refuge profoundly influence the stability in predator–prey systems. Full article

Thisarticle develops and analyzes a fractional-order Leslie–Gower prey–predator–parasite system incorporating two discrete delays and nonlocal spatial diffusion. The model’s central novelty lies in the simultaneous integration of three biologically realistic features that have not previously been combined: (i) fractional-order memory effects via a Caputo derivative of order $\alpha \in (0,1]$, (ii) two distinct biological delays—an infection transmission delay ${\tau }_1$ and a predator handling delay ${\tau }_2$—and (iii) nonlocal spatial dispersal modeled through fractional Laplacian operators ${(-\Delta )}^{\gamma /2}$. This triple integration enables the model to capture long-range temporal memory, delayed biological responses, and nonlocal spatial interactions simultaneously, offering insights into dynamics that are challenging to capture with classical integer-order or single-delay formulations. The fractional Laplacian generalizes classical diffusion by allowing long-range dispersal events (Lévy flights), where individuals can occasionally move over large distances with heavy-tailed step-size distributions—a phenomenon observed in many animal movement patterns but absent from standard diffusion models. We provide rigorous proofs of solution existence, uniqueness, non-negativity, and boundedness in both temporal and spatiotemporal settings. Local asymptotic stability conditions are derived for all feasible equilibrium states via characteristic equation analysis. The coexistence equilibrium undergoes a Hopf bifurcation when either delay crosses a critical threshold, with fractional order $\alpha$ modulating the bifurcation point and post-bifurcation oscillation frequency. A Lyapunov functional demonstrates global asymptotic stability of the infection-free equilibrium under biologically interpretable conditions. Turing instability analysis reveals conditions for spontaneous pattern formation, with the fractional exponent $\gamma$ controlling pattern wavelength and correlation length. Numerical simulations validate theoretical predictions, including spatial patterns, traveling waves, and chaos. To bridge theory with potential applications, we outline a statistical framework for parameter estimation and uncertainty quantification, suggesting that $\beta$, $\alpha$, and ${\tau }_1$ may be priority targets for parameter estimation. Full article

In flexible manufacturing systems, the composite mobile manipulator (CMM) is subject to nonlinear inertial disturbances arising from the dynamic coupling between the mobile platform and the robotic arm. These disturbances significantly impair positioning precision during grasping tasks. This paper addresses the dynamic decoupling of multi-body nonlinear inertial disturbances within CMM systems. Departing from the conventional “stop-then-plan” serial execution paradigm, we propose a full-cycle spatiotemporally coupled trajectory optimization method. The operation cycle is bifurcated into two synergistic stages: “dynamic calibration” and “static execution.” The dynamic calibration trajectory is pre-planned and executed synchronously during platform movement to actively compensate for inertial-induced pose deviations. Concurrently, the static execution trajectory is optimized and then triggered immediately upon platform standstill, ensuring a seamless and precise transition to the “Grasping Pose”. It is worth noting that the temporal characteristic central to this framework lies in the concurrent execution of static trajectory optimization and platform transit: by the time the platform reaches its destination, the pre-planned trajectory is already available for immediate triggering, achieving zero task-switching wait time at the planning layer. The term “zero-latency” here does not imply a fixed-cycle real-time response at the control layer, but rather the complete elimination of decision latency afforded by the parallel planning architecture. This framework eliminates computational latency, markedly enhancing operational efficiency. Key innovations include two novel constraints. First, the Adaptive Task-space Bounded Search Constraint (ATBSC) framework restricts optimization to a geometry-inspired search region, thereby enhancing search efficiency and ensuring controllable deviations. Second, the Multi-Rigid-Body Coupling Constraint (MRBCC) system explicitly models inertial transmission across motion phases to suppress pose fluctuations. The proposed framework is developed and validated within an obstacle-free workspace. In simulation-based validation on a UR10 6 degree-of-freedom manipulator model, experimental results indicate that ATBSC increases valid solution density to 84.7% and reduces average deviation by 72.8%. Furthermore, under the tested conditions, MRBCC mitigates end-effector position errors by 79.7–81.0% with a 97.5% constraint satisfaction rate. The improved Cuckoo Search algorithm (ICSA), serving as the solver component of the proposed framework, achieves an 11.9% lower fitness value and a 13.1% faster convergence rate compared to the standard Cuckoo Search algorithm in the tested scenarios, suggesting its effectiveness as a reliable solver for the constrained multi-objective trajectory optimisation problem. Full article

This study presents a comprehensive investigation of exact fractional wave solutions and bifurcation analysis for the (3 + 1)-dimensional extended shallow water wave (3D-eSWW) equation with $\beta$-derivative, which models nonlinear wave phenomena in fluid dynamics and coastal engineering. Leveraging the flexibility of the fractional derivative, the model provides a more generalized and adaptable framework for describing shallow water wave propagation. The Modified Extended Direct Algebraic Method (MEDAM) is systematically employed to derive a broad spectrum of novel exact analytical solutions. These include the following: dark solitary waves, singular solitons, singular periodic waves, periodic solutions expressed via trigonometric and Jacobi elliptic functions, polynomial solutions, hyperbolic wave patterns, combined dark–singular structures, combined hyperbolic–linear waves, and exponential-type wave profiles. Each solution family is presented with explicit parameter constraints that ensure both mathematical consistency and physical relevance, thereby offering a robust classification of wave regimes under diverse conditions. A thorough bifurcation analysis is conducted on the reduced dynamical system to examine parametric dependence and stability transitions. Critical bifurcation thresholds are identified, and distinct solution branches are mapped in the parameter space spanned by wave numbers, nonlinear coefficients, external forcing, and the fractional order $\beta$. The analysis reveals how solution dynamics undergo qualitative transitions—such as the emergence of solitary waves from periodic patterns or the appearance of singular structures—driven by the interplay of nonlinearity, dispersion, and fractional-order effects. These insights are crucial for understanding wave stability, predictability, and the onset of extreme events in shallow water contexts. Graphical representations of selected solutions validate the analytical results and illustrate the influence of $\beta$ on wave morphology, propagation, and stability. The simulations demonstrate that varying the fractional order can significantly alter wave profiles, highlighting the role of fractional calculus in capturing complex real-world behaviors. This work demonstrates the efficacy of the MEDAM technique in handling high-dimensional fractional nonlinear PDEs and provides a systematic framework for predicting and classifying wave regimes in real-world shallow water environments. The findings not only enrich the solution inventory of the 3D-eSWW equation but also advance the analytical toolkit for studying complex spatio-temporal dynamics in fractional mathematical physics and fluid mechanics. Ultimately, this research contributes to the development of more accurate models for coastal protection, tsunami forecasting, and marine engineering applications. Full article

The present work provides a detailed discussion of the dynamical behavior of the delay-induced model of cumulative concentration of hot pollutants, including the contribution of the time-delay parameter to the system’s stability. Analytical results indicate that time delay is a bifurcation mechanism that leads to a critical threshold, at which a steady state loses asymptotic stability and a Hopf bifurcation occurs. The directional analysis is carried out to further explain the behavior of the system in the neighborhood of this transition, and this offers some understanding of the nature and stability of the resulting periodic solutions, as well as the qualitative evolution. Numerical simulations are done on representative parameter values to support the theoretical results. Comprehensively, the findings reveal the strong dependence of the accumulation processes of pollutants on the effects of time delays and the significance of considering the temporal lags in environmental modeling. The study provides a viable analytical and numerical system of interpreting transitions caused by delays in pollutant concentration systems. Full article

The problem of analyzing the mechanisms of variability in population dynamics caused by the combined influence of the Allee effect, immigration and random fluctuations is addressed. In this study, we explore such a multi-factorial problem based on a Ricker-type population model. For the deterministic version of the model, the transformations of system dynamic regimes caused by changes in parameters of growth rate and intensity of immigration are determined using bifurcation analysis. For the randomly forced population model, the phenomena of stochastic excitement and noise-induced temporal extinction are revealed and investigated. The parametric study of these effects uses statistical data obtained from direct numerical modeling as well as an analytical approach based on the stochastic sensitivity technique and the confidence interval method. Full article

This study investigates the temporal and latitudinal variability of the ionosphere over the Indian longitude region during the intense geomagnetic storm from 1 to 3 June 2025, using GNSS receiver observations and magnetometer recordings, along with space-based measurements from in situ Swarm satellite, COSMIC-2 radio occultation, GUVI/TIMED-derived O/N₂ ratios, and model-derived electric fields. This particular event is relatively new and is characterized by the bifurcated variation with two distinct main phases separated by a short-lived recovery phase. The results revealed distinct features associated with the geomagnetic storm, including positive and negative ionospheric phases, thermospheric compositional changes, and the latitudinal propagation of disturbances. On 1 June, the observed strong positive ionospheric storm was driven by Prompt Penetration Electric Fields (PPEFs) and equatorward neutral winds, which triggered the upliftment of F-region plasma to higher altitudes through the enhanced equatorial fountain effect, leading to an unusually long-lasting Total Electron Content (TEC) enhancement from day to night. The analysis also revealed the distinct latitudinal behaviour, exhibiting the clear poleward extension of the Equatorial Ionization Anomaly (EIA) crest and significant TEC enhancements (~150–200% of the quiet day values) from low to mid latitudes as compared to the equatorial location through an efficient plasma redistribution. Conversely, pronounced negative ionospheric storm effect at almost all latitudinal locations on 2 June confirms complex and unusual storm-time dynamics, with inhibited upward plasma drifts due to the presence of Disturbance Dynamo Electric Fields (DDEFs), while the thermospheric O/N₂ ratio caused an extensive decrease in electron density over the Indian region. Minor negative storm noticed on 3 June coincides with the storm recovery period, reflecting prolonged disturbance dynamo effects and gradual recovery in thermospheric conditions. Overall, the current study highlights the strong sensitivity of the regional ionosphere to prevailing coupled electrodynamic-thermospheric forcing during the June 2025 geomagnetic storm that has not yet been reported for this event over the Indian longitude sector. Moreover, the findings from this study underscore peculiar storm-time behaviour of summer solstice ionosphere over the Indian longitude sector, driven by complex coupled processes which could be incorporated into ionospheric models and forecasting frameworks. Full article

Objective: Evaluate the efficacy and safety following intravitreal anti-vascular endothelial growth factor (anti-VEGF) therapy in patients with diabetic macular oedema (DME). Methods: To evaluate retinal microvascular remodelling and photoreceptor metrics using adaptive optics (AO) alongside systemic vascular status assessed by brachial/aortic hemodynamic and carotid ultrasound. We conducted a single-centre longitudinal study including twenty-one patients with DME. The following four diagnostic visits were performed: baseline (V1, no anti-VEGF treatment), 2–3 months (V2), 6–8 months (V3), and 12–14 months (V4). Adaptive optics (rtx1) measured foveal cone number (N) and regularity (Reg) within a standardised 80 × 80 µm window, and superior temporal retinal arteriole morphology after the first bifurcation (vessel diameter [VD], lumen diameter [LD], wall thickness [WT], wall-to-lumen ratio [WLR], and wall cross-sectional area [WCSA]). SphygmoCor provided peripheral (brachial) and central (aortic) pressures, augmentation pressure (AP), augmentation index (AIx), and carotid–femoral pulse wave velocity (PWV and PWVHR heart rate adjusted). Carotid ultrasound assessed intima–media thickness (IMT), carotid lumen diameter (CLD), and IMT/CLD ratio (IMTLR) 2 mm proximal to the bifurcation in diastole. Visual acuity (Visus), intraocular pressure (IOP), and central retinal thickness (CRT) were obtained at each visit. Results: In the treated eye (TE), WLR showed a significant overall change (Friedman p = 0.007), with a modest V4 vs. V1 increase (Wilcoxon p = 0.045); LD also varied across visits (Friedman p = 0.034). Cone metrics improved as follows: Reg increased over time (Friedman p = 0.019), with a significant rise at V4 vs. V1 (p = 0.018), and cone number increased at V3 vs. V1 (p = 0.012). Functional/structural outcomes improved as follows: visual acuity increased at V3 (p = 0.009) and V4 (p = 0.028), while CRT decreased at V3 (p = 0.002) and V4 (p = 0.030); IOP remained stable compared to V1. Systemic hemodynamics was largely unchanged; small fluctuations in DBP and cDBP across V1–V4 were observed (Friedman p = 0.034 and p = 0.022, respectively), whereas AIx, AP, PWV, and PWVHR showed no significant trends. Carotid IMT, CLD, and IMTLR did not change significantly across visits, supporting systemic vascular safety. Conclusions: Intravitreal anti-VEGF therapy in DME was associated with improvements in photoreceptor organisation and macular structure/function, with AO-derived arteriolar remodelling detectable over time, and no adverse changes in large-artery structure. These findings support ocular efficacy and systemic vascular safety; confirmation in larger cohorts is warranted. Full article

This study introduces a nonlinear fractional bi-susceptible model for COVID-19 using the Atangana–Baleanu derivative in Caputo sense (ABC). The fractional framework captures nonlocal effects and temporal decay, offering a realistic presentation of persistent infection cycles and delayed recovery. Within this setting, we investigate multiple transmission modes, determine the major risk factors, and analyze the long-term dynamics of the disease. Analytical results are obtained at equilibrium states, and fundamental properties of the model are validated. Numerical simulations based on the Toufik–Atangana method further endorse the theoretical results and emphasize the effectiveness of the ABC derivative. Bifurcation analysis illustrates that adjusting time-invariant treatment and awareness efforts can accelerate pandemic control. Sensitivity analysis identifies the most significant parameters, which are used to construct an optimal control problem to determine effective disease control strategies. The numerical results reveal that the proposed control interventions minimize both infection levels and associated costs. Overall, this research work demonstrates the modeling strength of the ABC derivative by integrating fractional calculus, bifurcation theory, and optimal control for efficient epidemic management. Full article

Indirect fear effects profoundly influence predator–prey dynamics by reducing prey reproduction. Whereas previous studies have investigated fear effects or self-diffusion separately in Leslie–Gower models, the novelty of this work lies in their simultaneous incorporation into a modified Leslie–Gower predator–prey system with Allee effect, leading to previously unreported bifurcations and spatiotemporal pattern selection. The temporal system exhibits up to six equilibria and undergoes a codimension-2 Bogdanov–Takens bifurcation. In the spatial extension, Turing instability is triggered when the predator diffusion coefficient exceeds a critical threshold. Using weak nonlinear multiple-scale analysis, amplitude equations are derived, and their stability analysis classifies stationary patterns into spots, stripes, and spot–stripe mixtures depending on the distance from the Turing onset. Numerical simulations confirm that low, moderate, and high predator diffusivity, respectively, favour spotted, mixed, and striped prey distributions. These results emphasise the critical role of fear-mediated indirect interactions and diffusion in driving spatial heterogeneity and ecosystem stability. Full article

As conventional computing architectures face fundamental physical limitations and the von Neumann bottleneck constrains computational efficiency, neuromorphic systems have emerged as a promising paradigm for next-generation information processing. Memristive neurons, particularly third-order circuits operating near the edge of chaos, exhibit rich neuromorphic dynamics that closely mimic biological neural activities but present significant prediction challenges due to their complex nonlinear behavior. Current approaches typically require complete system state measurements, which is often impractical in real-world neuromorphic hardware implementations where only partial state information is accessible. This paper addresses this critical limitation by proposing an innovative hybrid machine learning framework that integrates a Modified Next-Generation Reservoir Computing (MNGRC) with XGBoost regression. The core novelty lies in its dual-path prediction architecture designed specifically for partial state observability scenarios. The primary path employs NGRC to capture and forecast the system’s temporal dynamics using available state variables and input stimuli, while the secondary path leverages XGBoost as an efficient state estimator to infer unobserved state variables from minimal measurements. This strategic combination enables accurate prediction of diverse neuromorphic patterns with significantly reduced sensor requirements. Experimentally, the framework demonstrates its capability to identify and predict the complex spectrum of neuromorphic behaviors exhibited by the third-order memristive neuron. This includes accurately capturing all 18 distinct neuronal patterns, which are theoretically grounded in Hopf bifurcation analysis near the edge of chaos. Additionally, the framework successfully addresses the inverse problem of input stimulus reconstruction. By achieving accurate prediction of complex dynamics from limited states, our approach represents a key breakthrough, where full state access is often impossible, thereby addressing a critical challenge in edge AI and brain-inspired computing. Full article

Fisheries worldwide exhibit puzzling boom-and-bust cycles despite regulatory efforts, raising questions about what drives these oscillations. We investigate whether temporal delays in monitoring and decision-making contribute to system instability. Our model uses delay differential equations to track an exploiting population and its renewable resources, incorporating two distinct delays: one for perceiving resource status (${\tau }_2$) and another for implementing management responses (${\tau }_1$). We establish the existence, uniqueness, and positivity of solutions, then analyze equilibrium stability through linearization and Lyapunov–Razumikhin functions. The characteristic equation reveals Hopf bifurcations at critical delay thresholds. Numerical simulations across 1600 parameter combinations using MATLAB R2023b’s DDE23 algorithm quantify these transitions. The results show a critical threshold near 1.64 years (20 months): below this value, systems converge to a stable equilibrium, while above it, persistent oscillations emerge within 20–26 year periods. Unexpectedly, one large delay destabilizes less than two moderate delays summing to the same total, contradicting uniform improvement strategies. Convergence to limit cycles requires roughly 40 years, exceeding typical management horizons and potentially masking true system dynamics. The critical threshold lies within realistic administrative timescales, suggesting that institutional delays may substantially contribute to observed population fluctuations. These findings indicate that accelerating either monitoring or decision processes rather than providing modest improvements to both could better stabilize exploited resources. Full article

Background and Objectives: The roles of final kissing balloon inflation (FKBI) in single stenting remain controversial, with no prior studies evaluating its impact on angiographic restenosis from the perspective of coronary microcirculation. This study aimed to investigate whether FKBI reduced angiographic restenosis in patients treated with single stenting in the left main (LM)–left anterior descending (LAD) after propensity score matching (PSM) to balance baseline characteristics, including the pre-procedural angiography-derived index of microcirculatory resistance (AMR). Additionally, it aimed to demonstrate the temporal changes in AMR (pre-procedure, post-procedure, and follow-up) and their impact on angiographic restenosis. Materials and Methods: AMR was calculated based on coronary angiography from a single view of the LM–left circumflex (LCX), pre- and post-procedure and during follow-up. Long-term angiographic restenosis was assessed using percent diameter stenosis (DS%). Results: A total of 197 patients underwent the simple crossover, and 70 underwent FKBI, while 61 pairs were matched after the PSM. The long-term DS% in the LM and LAD was lower in the FKBI group after the PSM. The long-term AMR demonstrated an increase in the simple crossover group but stability in the FKBI group. The long-term AMR was lower post-FKBI regardless of the PSM. The pre-procedural AMR was a positive predictor of long-term LAD angiographic restenosis in the simple crossover group, but it did not show any correlation in the FKBI group. Conclusions: After PSM involving pre-procedural AMR, FKBI could reduce long-term angiographic restenosis in the LM and LAD following left main distal bifurcation single stenting and exhibited lower long-term AMR compared to the simple crossover group. The pre-procedural AMR predicted the future LAD progression in the simple crossover group, yet FKBI seemed to nullify the association. Full article