Search Results (478)

This study investigated the aerodynamic structures generated by transverse jet injection in supersonic flows around high-speed vehicles. The unsteady evolution of these structures was analyzed using an improved delayed detached Eddy simulation (IDDES) approach based on the Reynolds stress model (RSM). The simulations successfully reproduced experimentally observed shock systems and vortical structures. The time-averaged flow characteristics were compared with the experimental results, and good agreement was observed. The flow characteristics were analyzed, with particular emphasis on the formation of counter-rotating vortex pairs in the downstream region, as well as complex near-field phenomena, such as flow separation and shock wave/boundary layer interactions. Time-resolved spectral analysis at multiple monitoring locations revealed the presence of a global oscillation within the flow dynamics. Within these regions, pressure fluctuations in the recirculation zone lead to periodic oscillations of the upstream bow shock. This dynamic interaction modulates the instability of the windward shear layer and generates large-scale vortex structures. As these shed vortices convect downstream, they interact with the barrel shock, triggering significant oscillatory motion. To further characterize this behavior, dynamic mode decomposition (DMD) was applied to the pressure fluctuations. The analysis confirmed the presence of a coherent global oscillation mode, which was found to simultaneously govern the periodic motions of both the upstream bow shock and the barrel shock. Full article

This study presents a comprehensive analysis of climate evolution in the Porto region (Northern Portugal) using 148 years (1863–2010) of continuous meteorological data from the Serra do Pilar weather station (WMO station 08546). The research employs both traditional linear statistical methods and advanced non-linear analysis techniques, including polynomial trend fitting and multidecadal oscillation analysis, to accurately characterize long-term climate patterns. Results reveal that linear trend analysis is misleading for this dataset, as both temperature and precipitation follow parabolic (U-shaped) distributions with minima around 1910–1970. The early period (1863–1900) exhibited higher values than the recent period, contradicting linear trend interpretations. Advanced analysis shows that the mean temperature follows a parabolic pattern (R² = 0.353) with the minimum around 1935, while precipitation exhibits similar behavior (R² = 0.053) with the minimum around 1936. Multidecadal oscillations are detected with dominant periods of 46.7, 15.6, and 10.0 years for temperature, and 35.0, 17.5, and 4.5 years for precipitation. Maximum temperatures show complex oscillatory behavior with a severe drop around 1890. Seasonal analysis reveals distinct patterns across all seasons: winter (+0.065 °C/decade) and autumn (+0.059 °C/decade) show warming trends in maximum temperatures, while spring (−0.080 °C/decade) and summer (−0.079 °C/decade) demonstrate cooling trends in minimum temperatures, with no significant trends in spring (+0.012 °C/decade) and summer (+0.003 °C/decade) maximum temperatures or winter (−0.021 °C/decade) and autumn (−0.035 °C/decade) minimum temperatures. The study identifies a significant change point in mean temperature around 1980, which occurs approximately one decade earlier than the global warming acceleration typically observed in the 1990s, suggesting regional Atlantic influences may precede global patterns. Extreme event analysis indicates stable frequencies of hot days (averaging 3.6 days/year above 25.0 °C) and heavy precipitation events (averaging 1.2 days/year above 234.6 mm) throughout the study period. These findings demonstrate that the Porto region’s climate is characterized by natural multidecadal variability rather than monotonic trends, with the climate system showing oscillatory behavior typical of Atlantic-influenced coastal regions. The results contribute to understanding regional climate variability and provide essential baseline data for climate change adaptation strategies in Northern Portugal. The results align with broader patterns of natural climate variability in the Iberian Peninsula while highlighting the importance of non-linear analysis for comprehensive climate assessment. Full article

This paper presents some oscillation criteria for second-order half-linear dynamic equations defined on unbounded above arbitrary time scales. These criteria offer sufficient conditions for all solutions of the equations to display oscillatory behavior. We investigate both delay and advanced cases of these equations, and our approach encompasses a broader class of dynamic equations than previously considered in the literature. The results of this study not only generalize well-known oscillation criteria used in differential equations but also significantly broaden their applicability to arbitrary time scales. Additionally, we provide illustrative examples to demonstrate the relevance and accuracy of our findings. Full article

The concept of fuzzy modeling and fuzzy system design has opened new horizons of research in functional analysis, having a significant impact on major fields such as data science, machine learning, and so on. In this research, we use fuzzy set theory to analyze the global dynamics and oscillatory behavior of nonlinear fuzzy difference equations with exponential decay. We discuss the stability, oscillatory patterns, and convergence of solutions under different initial conditions. The exponential structure simplifies the analysis while providing a clear understanding of the system’s behavior over time. The study reveals how fuzzy parameters influence growth or decay trends, emphasizing the method’s effectiveness in handling uncertainty. Our findings advance the understanding of higher-order fuzzy difference equations and their potential applications in modeling systems with imprecise data. Using the characterization theorem, we convert a fuzzy difference equation into two crisp difference equations. The g-division technique was used to investigate local and global stability and boundedness in dynamics. We validate our theoretical results using numerical simulations. Full article

In multimodal collaborative learning, the gradient dynamics of heterogeneous modalities face significant challenges due to the curvature heterogeneity of parameter manifolds and mismatches in phase evolution. Traditional Euclidean optimization methods struggle to capture the complex interdependencies between heterogeneous modalities on non-Euclidean or geometrically inconsistent parameter manifolds. Furthermore, static alignment strategies often fail to suppress bifurcations and oscillatory behaviors in high-dimensional gradient flows, leading to unstable optimization trajectories across modalities. To address these issues, inspired by hyperbolic geometry and symplectic structures, this paper proposes the Hyperbolic Cosine-Based Symplectic Phase Alignment (HC-SPA) fusion optimization framework. The proposed approach leverages the geometric properties of hyperbolic space to coordinate gradient flows between modalities, aligns gradient update directions through a phase synchronization mechanism, and dynamically adjusts the optimization step size to adapt to manifold curvature. Experimental results on public fusion and semantic segmentation datasets demonstrate that HC-SPA significantly improves multimodal fusion performance and optimization stability, providing a new optimization perspective for complex multimodal tasks. Full article

The extremely exceptional set for the run-length function in the beta-dynamical system is investigated in this study. For any real x in $(0,1]$, the run-length function related to x that measures the maximal length of the initial digit sequence of the $\beta$-expansion of x appears consecutively among the first n digits of the $\beta$-expansion of another real number y in $(0,1]$. The extremely exceptional set consists of all real numbers y with run-length exhibiting extreme oscillatory behavior: the limit inferior of the ratio of the run-length function to the logarithm base $\beta$ of n is zero, while the limit superior of this same ratio is infinity. We prove that the Hausdorff dimension of this set is either 0 or 1, determined solely by the asymptotic scaling of the basic intervals containing x. Crucially, for all x belonging to $(0,1]$, the set is residual in $[0,1]$, which implies that its boxing dimension is 1, which generalizes some known results. Full article

In this work, we investigate the quantum coherence and purity in hydrogen atoms under dissipative dynamics, with a focus on the hyperfine structure states arising from the electron–proton spin interaction. Using the Lindblad master equation, we model the time evolution of the density matrix of the system, incorporating both the unitary dynamics driven by the hyperfine Hamiltonian and the dissipative effects due to environmental interactions. Quantum coherence is quantified using the $L_1$ norm and relative entropy measures, while purity is assessed via von Neumann entropy, for initial states, including a maximally entangled Bell state and a separable state. Our results reveal distinct dynamics: for the Bell states, both coherence and purity decay exponentially with a rate proportional to the dissipation parameter, whereas for a kind of separable state, coherence exhibits oscillatory behavior modulated via the hyperfine coupling constant, superimposed on an exponential decay, and accompanied by a steady increase in entropy. Higher dissipation rates accelerate the loss of coherence and the growth of von Neumann entropy, underscoring the environment’s role in suppressing quantum superposition and driving the system towards mixed states. These findings enhance our understanding of coherence and purity preservation in atomic systems and offer insights for quantum information applications where robustness against dissipation is critical. Full article

Background: Local anesthesia is essential for most dental procedures, but its parenteral administration is often painful. Topical anesthetics are commonly used to minimize local anesthesia pain; however, commercial formulations fail to fully prevent the discomfort of local anesthetic injection. Methods: We developed and characterized a novel lidocaine and epinephrine co-loaded liquid crystalline precursor system (LCPS) for topical anesthesia. The formulation was structurally characterized using polarized light microscopy (PLM) and small-angle X-ray scattering (SAXS). Rheological behavior was assessed through continuous and oscillatory rheological analyses. Texture profile analysis, in vitro mucoadhesive force evaluation, in vitro drug release and permeation studies, and an in vivo toxicity assay using the chicken chorioallantoic membrane (CAM) model were also conducted. Results: PLM and SAXS confirmed the transition of the LCPS from a microemulsion to a lamellar liquid crystalline structure upon contact with artificial saliva. This transition enhanced formulation consistency by over 100 times and tripled mucoadhesion strength. The LCPS also provided controlled drug release, reducing permeation flow by 93% compared to the commercial formulation. Importantly, the CAM assay indicated that the LCPS exhibited similar toxicity to the commercial product. Conclusions: The developed LCPS demonstrated promising physicochemical and biological properties for topical anesthesia, including enhanced mucoadhesion, controlled drug delivery, and acceptable biocompatibility. These findings support its potential for in vivo application and future clinical use to reduce pain during dental anesthesia procedures. Full article

The oscillatory behavior of a class of third-order hybrid-type delay differential equations—used to model various real-world phenomena in fluid dynamics, control systems, biology, and beam deflection—is investigated in this study. A novel method is proposed, whereby these complex trinomial equations are reduced to a simpler binomial form by employing solutions of the corresponding linear differential equations. Through the use of comparison techniques and integral averaging methods, new oscillation criteria are derived to ensure that all solutions exhibit oscillatory behavior. These results are shown to extend and enhance existing theories in the oscillation analysis of functional differential equations. The effectiveness and originality of the proposed approach are illustrated by means of two representative examples. Full article

This study applies Computational Fluid Dynamics (CFD) and mathematical modeling to examine uterine and umbilical arterial blood flow during pregnancy, providing a more detailed understanding of hemodynamic changes across gestation. Statistical analysis of Doppler ultrasound data from a large cohort of more than 200 pregnant women (in the second and third trimesters) reveals significant increases in the umbilical arterial peak systolic velocity ($PSV$) between the 22nd and 30th weeks, while uterine artery velocities remain relatively stable, suggesting adaptations in vascular resistance during pregnancy. By combining the Navier–Stokes equations with Doppler ultrasound-derived inlet velocity profiles, we quantify several key fluid dynamics parameters, including time-averaged wall shear stress ($TAWSS$), oscillatory shear index ($OSI$), relative residence time ($RRT$), Reynolds number ($Re$), and Dean number ($De$), evaluating laminar flow stability in the uterine artery and secondary flow patterns in the umbilical artery. Since blood exhibits shear-dependent viscosity and complex rheological behavior, modeling it as a non-Newtonian fluid is essential to accurately capture pulsatile flow dynamics and wall shear stresses in these vessels. Unlike conventional imaging techniques, CFD offers enhanced visualization of blood flow characteristics such as streamlines, velocity distributions, and instantaneous particle motion, providing insights that are not easily captured by Doppler ultrasound alone. Specifically, CFD reveals secondary flow patterns in the umbilical artery, which interact with the primary flow, a phenomenon that is challenging to observe with ultrasound. These findings refine existing hemodynamic models, provide population-specific reference values for clinical assessments, and improve our understanding of the relationship between umbilical arterial flow dynamics and fetal growth restriction, with important implications for maternal and fetal health monitoring. Full article

To investigate temporal deformation mechanisms of hydraulic asphalt concrete slope flow under evolving temperatures, this study developed a novel temperature-controlled slope flow intelligent test apparatus. Using this apparatus, slope flow tests were conducted at four temperature levels: 20 °C, 35 °C, 50 °C, and 70 °C. By applying nonlinear dynamics theory, the temporal evolution of slope flow deformation and its nonlinear mechanical characteristics under varying temperatures were thoroughly analyzed. Results indicate that the thermal stability of hydraulic asphalt concrete is synergistically governed by the phase-transition behavior between asphalt binder and aggregates. Temporal evolution of slope flow exhibits a distinct three-stage pattern as follows: rapid growth (0~12 h), where sharp temperature rise disrupts the primary skeleton of coarse aggregates; decelerated growth (12~24 h), where an embryonic secondary skeleton forms and progressively resists deformation; stabilization (>24 h), where reorganization of coarse aggregates is completed, establishing structural equilibrium. The thermal stability temperature influence factor (δ) shows a nonlinear concave growth trend with increasing test temperature. Dynamically, this process transitions sequentially through critical stability, nonlinear stability, period-doubling oscillatory stability, and unsteady states. Full article

This study focuses on investigating the asymptotic and oscillatory behavior of a new class of fourth-order nonlinear neutral differential equations. This research aims to achieve a qualitative advancement in the analysis and understanding of the relationships between the corresponding function and its derivatives. By utilizing various techniques, innovative criteria have been developed to ensure the oscillation of all solutions of the studied equations without resorting to additional constraints. Effective analytical tools are provided, contributing to a deeper theoretical understanding and expanding their application scope. The paper concludes by presenting examples that illustrate the practical impact of the results, highlighting the theoretical value of the research in the field of functional differential equations. Full article

In this work, the thermodynamics of molecular transport between two compartments connected by a nanochannel is investigated through an analysis of internal energy and entropy changes, with a focus on how these changes depend on intermolecular interaction strength. When interactions are weak, resembling gas-like behavior, entropy dominates and favors configurations in which molecules are evenly distributed between the two compartments, despite an increase in internal energy. In contrast, strong interactions, characteristic of liquid-like behavior, lead to dominant energetic contributions that favor configurations with molecules localized in a single compartment, despite entropy loss. Intermediate interaction strengths yield comparable entropic and energetic contributions that cancel each other out, resulting in oscillatory behavior between evenly distributed and localized configurations, as observed in previous work. This thermodynamic analysis reveals energy–entropy compensation, in which entropic and energetic contributions offset each other across different interaction strengths; notably, this compensatory relationship exhibits a linear trend. These findings provide insight into the thermodynamic origins of molecular transport behavior and highlight fundamental parallels between molecular transport and molecular binding, the latter being particularly relevant to molecular recognition and drug design. Full article

This paper explores the oscillatory behavior of a class of second-order hybrid-type neutral delay difference equations. A novel approach is introduced to transform these complex trinomial equations into a simpler binomial form by utilizing solutions from an associated linear difference equation. By employing comparison techniques and summation-averaging methods, we establish new oscillation criteria which guarantee that all solutions exhibit oscillatory behavior. Our findings extend to an existing oscillation theory and are applicable even to non-neutral second-order equations. A couple of examples are presented to highlight the impact and novelty of the obtained results. Full article

Background: This systematic review examines artificial intelligence (AI) applications in neuroimaging for autism spectrum disorder (ASD), addressing six research questions regarding biomarker optimization, modality integration, social function prediction, developmental trajectories, clinical translation challenges, and multimodal data enhancement for earlier detection and improved outcomes. Methods: Following PRISMA guidelines, we conducted a comprehensive literature search across 8 databases, yielding 146 studies from an initial 1872 records. These studies were systematically analyzed to address key questions regarding AI neuroimaging approaches in ASD detection and prognosis. Results: Neuroimaging combined with AI algorithms demonstrated significant potential for early ASD detection, with electroencephalography (EEG) showing promise. Machine learning classifiers achieved high diagnostic accuracy (85–99%) using features derived from neural oscillatory patterns, connectivity measures, and signal complexity metrics. Studies of infant populations have identified the 9–12-month developmental window as critical for biomarker detection and the onset of behavioral symptoms. Multimodal approaches that integrate various imaging techniques have substantially enhanced predictive capabilities, while longitudinal analyses have shown potential for tracking developmental trajectories and treatment responses. Conclusions: AI-driven neuroimaging biomarkers represent a promising frontier in ASD research, potentially enabling the detection of symptoms before they manifest behaviorally and providing objective measures of intervention efficacy. While technical and methodological challenges remain, advancements in standardization, diverse sampling, and clinical validation could facilitate the translation of findings into practice, ultimately supporting earlier intervention during critical developmental periods and improving outcomes for individuals with ASD. Future research should prioritize large-scale validation studies and standardized protocols to realize the full potential of precision medicine in ASD. Full article