Search Results (2,344)

The stability of a circular vortex is studied in the thermal quasi-geostrophic (TQG) model. Several radial distributions of vorticity and buoyancy (temperature) are considered for the mean flow. First, the linear stability of these vortices is addressed. The linear problem is solved exactly for a simple flow, and two stability criteria are then derived for general mean flows. Then, the growth rate and most unstable wavenumbers of normal-mode perturbations are computed numerically for Gaussian and cubic exponential vortices, both for elliptical and higher mode perturbations. In TQG, contrary to usual QG, short waves can be linearly unstable on shallow vorticity profiles. Linearly, both stratification and bottom topography (under specific conditions) have a stabilizing role. In a second step, we use a numerical model of the nonlinear TQG equations. With a Gaussian vortex, we show the growth of small-scale perturbations during the vortex instability, as predicted by the linear analysis. In particular, for an unstable vortex with an elliptical perturbation, the final tripolar vortices can have a turbulent peripheral structure, when the ratio of mean buoyancy to mean velocity is large enough. The frontogenetic tendency indicates how small-scale features detach from the vortex core towards its periphery, and thus feed the turbulent peripheral vorticity. We confirm that stratification and topography have a stabilizing influence as shown by the linear theory. Then, by varying the vortex and perturbation characteristics, we classify the various possible nonlinear regimes. The numerical simulations show that the influence of the growing small-scale perturbations is to weaken the peripheral vortices formed by the instability, and by this, to stabilize the whole vortex. A finite radius of deformation and/or bottom topography also stabilize the vortex as predicted by linear theory. An extension of this work to stratified flows is finally recommended. Full article

Background/Objectives: Endometriosis (EM) is a chronic gynecological condition associated with infertility, oxidative stress, and altered metabolic regulation. Follicular fluid (FF) reflects the microenvironment of the developing oocyte, and changes in its amino acid composition may affect reproductive outcomes. This study aimed to characterize alterations in the amino acid composition of the FF in EM and to identify potential reproductive outcomes. Methods: Targeted metabolomic analysis of 20 amino acids was performed on FF samples from 56 women undergoing in vitro fertilization (17 with endometriosis, 39 controls). Amino acid concentrations were quantified and compared between groups, adjusting for age and body mass index. Pathway, biomarker, and multivariate analyses were conducted to explore metabolic alterations and potential diagnostic markers. Results: Asparagine, histidine, and glycine concentrations were significantly higher in the EM group after adjustment for age and BMI. Pathway analysis indicated perturbations in glycine/serine metabolism, glutathione metabolism, and porphyrin metabolism, consistent with oxidative stress and mitochondrial dysfunction. Multivariate modeling demonstrated partial separation between groups, while biomarker analysis identified asparagine (AUC = 0.76), along with glycine and histidine, as potential discriminators. Additional enrichment of bile acid and methylation-related pathways suggested broader systemic metabolic changes in EM. Conclusions: EM is associated with distinct amino acid alterations in the FF, particularly elevated asparagine, histidine, and glycine, reflecting oxidative and mitochondrial imbalance in the follicular environment. These metabolites emerged as candidate biomarkers requiring validation for EM-related oocyte quality changes and may help individualize in vitro fertilization approaches. Full article

This paper addresses the finite-time stability of a class of fractional Itô–Doob stochastic systems with time delays. Novel stability criteria are established using a combination of Gronwall-type, Hölder’s, and Burkholder–Davis–Gundy (BDG) inequalities, thereby generalizing classical integer-order stability theory to the fractional domain. Furthermore, the analysis uniquely integrates stochastic perturbations and time delays, providing a comprehensive framework for systems exhibiting both memory and randomness. The effectiveness of the proposed approach is demonstrated through a numerical example of a three-dimensional stochastic delayed system with fractional dynamics. Full article

We have theoretically examined the intermolecular interactions in the cationic states of π-stacked dimers of 4nπ antiaromatic molecules. The ground state of face-to-face π-dimer models, consisting of cyclobutadienes (CBDs), was analyzed as a function of the stacking distance (d) for their monocationic and dicationic states using multi-reference second-order perturbation theory. Multi-configurational wavefunction analysis in a diabatic representation was employed to understand the electronic structures of the dimer models in terms of the monomer electron configurations. It is found that the monocationic dimer exhibits a local minimum at about d = 2.4 Å in the ground state, where each monomer is represented by a superposition between neutral triplet and cationic doublet electron configurations. Crossing of the ground and excited states occurs through changing d, which is due to the small energy gap between the highest occupied and lowest unoccupied molecular orbitals of antiaromatic molecules. Full article

Background: Soccer’s varied physical demands require meticulous load monitoring, which is now being advanced by combining GPS for external metrics and NMR-based metabolomics for internal metabolic profiling. This study aimed to investigate how player position influences the metabolomic profile (as a marker of internal load) under known match effort (external load). Methods: This was a longitudinal observational descriptive study involving 12 professional soccer players from the U-20 São Paulo Football Club, enrolled in the 2022 São Paulo State Under-20 Football Championship. Players were monitored across six matches during the season, culminating in a total of 49 individual match observations from those players (4-2-3-1 formation: Central Defenders [CD], n = 9; Full Backs [FB], n = 9; Central Midfielders [CM], n = 14; Wide Midfielders [WM], n = 12; Forwards [F], n = 5). Internal load was assessed via urinary metabolomics, with urine samples collected 24 h post-match. A non-targeted, global metabolomics approach was employed using nuclear magnetic resonance (NMR) spectroscopy. External load was monitored using GPS tracking devices. Multivariate analyses included partial least squares discriminant analysis (PLS-DA), and heat maps. Results: Metabolomic analysis identified 38 metabolites with a Variable Importance in Projection (VIP) score > 1.0, revealing perturbations in carbohydrate metabolism and the tricarboxylic acid (TCA) cycle, amino acid and peptide metabolism, pyrimidine metabolism, and ketone body pathways, and effectively discriminating post-match recovery metabolic profiles. External load metrics varied significantly by player position: CMs covered greater distances below 20 km/h (8702.93 ± 1271.89 m), exhibited higher relative distance (114.29 ± 7.67 m/min), total distance (9193.21 ± 1261.35 m), and player load (945.71 ± 135.82 a.u.); CDs achieved higher peak speeds (31.78 ± 1.20 m/s); and WMs performed greater sprint distances (168.11 ± 91.69 m). Metabolomic profiles indicated that CMs showed stronger associations with markers of muscle damage and inflammation, whereas CDs and WMs were more closely linked to energy metabolism and oxidative stress. Conclusions: These results highlight the importance of a personalized approach to training load monitoring and recovery strategies, considering the distinct physiological and metabolic demands associated with each player position. Full article

Pedestrian crashes remain a leading cause of road traffic fatalities in developing countries (DCs); yet reliable crash data are scarce, constraining the ability to model pedestrian safety risks and evaluate countermeasure effectiveness. This study developed a methodological process for estimating the influence of contextual factors on pedestrian crashes using artificial data. The process integrated literature-derived trend analysis, artificial data generation, external face validity checks, correlation analysis, stepwise negative binomial regression, sensitivity testing, and mapping of results against the International Road Assessment Programme (iRAP) framework. Of the 26 contextual factors considered, 20 were retained in the negative binomial (NB) models, while six were excluded due to weak or inconsistent trend data. Results showed that behavioural and institutional factors, including ad hoc countermeasure implementation, gender composition of pedestrian flows, and vehicle age or technology, exerted stronger influence on crash outcomes than several geometric variables typically emphasised in global models. External validity testing confirmed broad alignment of the artificial dataset with published values, while sensitivity analysis demonstrated the robustness of factor influence values (Fi) across bootstrap resampling and scenario perturbations. The Fi values derived are illustrative rather than decision-ready, reflecting the artificial-data basis of this study. Nonetheless, the findings highlight methodological proof of concept that artificial-data modelling can provide credible and context-sensitive insights in data-scarce environments. Mapping results to the iRAP framework revealed complementarity, with opportunities to extend global models by incorporating behavioural and institutional variables more systematically. The approach provides a replicable pathway for improving pedestrian safety assessment in DCs and informs the development of an enhanced iRAP effectiveness model in subsequent research. Future applications should prioritise empirical calibration with real-world crash datasets and support policymakers in integrating behavioural and institutional factors into countermeasure prioritisation and safety planning. Full article

This work presents a hybrid model that integrates a mechanistic multicomponent transport scheme in hollow-fiber membranes with an Adaptive Neuro-Fuzzy Inference System (ANFIS). The physical model incorporates pressure drops on the feed and permeate sides (Hagen–Poiseuille), non-ideal gas behavior (Peng–Robinson equation of state), and temperature-dependent viscosity; species permeances are treated as constant for model validation. After validation, a post-validation parametric exploration of permeance variability is carried out by perturbing the methane (CH₄) permeance by one decade up and down. From an initial set of 18 variables, 4 key parameters were selected through rigorous statistical analysis (Pearson correlation, variance inflation factor (VIF), and mean absolute error (MAE)); likewise, other physical criteria have been considered: permeance, retentate volume, retentate pressure, and retentate viscosity. Trained with 70% of the simulated data and validated with the remaining 30%, the model achieves a coefficient of determination (R²) close to 0.999 and a root mean square error (RMSE) below 8 × 10⁻⁸ m³/h in predicting the methane volume in the retentate, effectively responding to both steady and dynamic fluctuations. The combination of first-principles modeling and adaptive learning captures both steady-state and dynamic behavior, positioning the approach as a viable tool for real-time analysis and supervisory control in petrochemical membrane operations. Full article

Floods are among the most destructive natural hazards, with damages expected to intensify under climate change and socio-economic pressures. Effective reservoir operation remains a critical yet challenging strategy for mitigating downstream impacts, as operators must navigate nonlinear system dynamics, uncertain inflow forecasts, and trade-offs between competing objectives. This study proposes a novel end-to-end data-driven framework that integrates process-based hydraulic simulations, a Transformer-based surrogate model for flood damage prediction, and reinforcement learning (RL) for reservoir gate operation optimization. The framework is demonstrated using the Coralville Reservoir (Iowa, USA) and two major historical flood events (2008 and 2013). Hydraulic and impact simulations with HEC-RAS and HEC-FIA were used to generate training data, enabling the development of a Transformer model that accurately predicts time-varying flood damages. This surrogate is coupled with a Transformer-enhanced Deep Q-Network (DQN) to derive adaptive gate operation strategies. Results show that the RL-derived optimal policy reduces both peak and time-integrated damages compared to expert and zero-opening benchmarks, while maintaining smooth and feasible operations. Comparative analysis with a genetic algorithm (GA) highlights the robustness of the RL framework, particularly its ability to generalize across uncertain inflows and varying initial storage conditions. Importantly, the adaptive RL policy trained on perturbed synthetic inflows transferred effectively to the hydrologically distinct 2013 event, and fine-tuning achieved near-identical performance to the event-specific optimal policy. These findings highlight the capability of the proposed framework to provide adaptive, transferable, and computationally efficient tools for flood-resilient reservoir operation. Full article

The unprecedented increase in the number of objects orbiting the Earth necessitates a comprehensive characterisation of these objects to improve the effectiveness of Space Surveillance and Tracking (SST) operations. In particular, accurate knowledge of the attitude and physical properties of space objects has become critical for space debris mitigation measures, since these parameters directly influence major perturbation forces like atmospheric drag and solar radiation pressure. Characterising a space object beyond its orbital position improves the accuracy of SST activities such as collision risk assessment, atmospheric re-entry prediction, and the design of Active Debris Removal (ADR) and In-Orbit Servicing (IOS) missions. This study presents a novel approach for the simultaneous estimation of the attitude and optical reflective properties of uncontrolled space objects with known shape using light curves. The proposed method also accounts for atmospheric effects, particularly the Aerosol Optical Depth (AOD), a highly variable parameter that is difficult to determine through on-site measurements. The methodology integrates different estimation, optimisation, and data analysis techniques to achieve an accurate, robust, and computationally efficient solution. The performance of the method is demonstrated through the analysis of a simulated scenario representative of realistic operational conditions. Full article

This manuscript presents a robust formation and collision-free containment control system designed for a quadrotor fleet operating under turbulent wind conditions. Emphasizing collision avoidance, we introduce a two-layer strategy in which a virtual leader defines a trajectory, and leaders and followers maintain their positions while avoiding collisions among them. A graph convention is used to illustrate the roles of leaders and followers, as well as their interactions. Inter-agent collision avoidance is proposed by expanding the desired distance relative to all neighboring agents, thereby guaranteeing the convergence stage. Moreover, the approach employs a class of adaptive sliding mode strategies to ensure finite-time convergence, as well as non-overestimation of the control gain in the presence of uncertainties and perturbations. A stability analysis demonstrates the practical finite-time stability of the system using the Lyapunov methodology. Results from the simulation underscore the effectiveness of our proposal in adhering to the desired time-varying trajectories and ensuring sensor-less inter-agent collision avoidance for the followers, even in the presence of turbulent wind conditions. Full article

To address the issue of reducing noise in images containing mixed noise, a Volterra filtering method based on a Bat algorithm with velocity weight perturbation is proposed to optimize kernel parameters. The structural advantages of the Volterra filter (predictive performance, linear and nonlinear terms) are used to reduce the noise in these images. The dynamic velocity inertia-weight perturbation mechanism is used to improve the Bat algorithm’s optimization ability, while the kernel-parameter optimization and the noise reduction abilities of the Volterra filter are further improved. Theoretical analysis and experimental results show that the high-density mixed noise, comprising Gaussian and salt-and-pepper noise, can be filtered effectively by the proposed algorithm. Compared to traditional image-denoising methods, the proposed method outperforms other algorithms in removing mixed noise from images while preserving edge details. Within a specific noise intensity range, the greater the intensity of mixed noise in the image, the better the noise reduction performance of this filtering method. The method proposed in this paper is less affected by noise intensity. When the number of bats in the population and the number of iterations reach a certain value, the algorithm exhibits good convergence and stability. Full article

The primary objective of this study is to provide a more precise and beneficial mathematical model for assessing corruption dynamics by utilizing non-local derivatives. This research aims to provide solutions that accurately capture the complexities and practical behaviors of corruption. To illustrate how corruption levels within a community change over time, a non-linear deterministic mathematical model has been developed. The authors present a non-integer order model that divides the population into five subgroups: susceptible, exposed, corrupted, recovered, and honest individuals. To study these corruption dynamics, we employ a new method for solving a time-fractional corruption model, which we term the q-homotopy analysis transform approach. This approach produces an effective approximation solution for the investigated equations, and data is shown as 3D plots and graphs, which give a clear physical representation. The stability and existence of the equilibrium points in the considered model are mathematically proven, and we examine the stability of the model and the equilibrium points, clarifying the conditions required for a stable solution. The resulting solutions, given in series form, show rapid convergence and accurately describe the model’s behaviour with minimal error. Furthermore, the solution’s uniqueness and convergence have been demonstrated using fixed-point theory. The proposed technique is better than a numerical approach, as it does not require much computational work, with minimal time consumed, and it removes the requirement for linearization, perturbations, and discretization. In comparison to previous approaches, the proposed technique is a competent tool for examining an analytical outcomes from the projected model, and the methodology used herein for the considered model is proved to be both efficient and reliable, indicating substantial progress in the field. Full article

Offline reinforcement learning suffers from critical vulnerability to data corruption from sensor noise or adversarial attacks. Recent research has achieved a lot by downweighting corrupted samples and fixing the corrupted data, while data corruption induces feature entanglement that undermines policy robustness. Existing methods fail to identify causal features behind performance degradation caused by corruption. To analyze causal relationships in corrupted data, we propose a method, Robust Causal Feature Disentanglement(RCFD). Our method introduces a learnable causal feature disentanglement mechanism specifically designed for reinforcement learning scenarios, integrating the CausalVAE framework to disentangle causal features governing environmental dynamics from corruption-sensitive non-causal features. Theoretically, this disentanglement confers a robustness advantage under data corruption conditions. Concurrently, causality-preserving perturbation training injects Gaussian noise solely into non-causal features to generate counterfactual samples and is enhanced by dual-path feature alignment and contrastive learning for representation invariance. A dynamic graph diagnostic module further employs graph convolutional attention networks to model spatiotemporal relationships and identify corrupted edges through structural consistency analysis, enabling precise data repair. The results exhibit highly robust performance across D4rl benchmarks under diverse data corruption conditions. This confirms that causal feature invariance helps bridge distributional gaps, promoting reliable deployment in complex real-world settings. Full article

Estrogen metabolites (EMs) play a local regulatory role in mammalian ovarian function. Among them, 2-hydroxyestradiol (2-OHE2) exerts dose-dependent effects on reproductive physiology, supporting either normal ovarian processes or contributing to pathological conditions. Specifically, 2-OHE2 modulates ovarian vasculature and progesterone biosynthesis, and at 1–10 nM concentrations, it enhances in vitro developmental competence and blastocyst quality in mouse oocytes. Conversely, doses below 1 nM show no appreciable effects, suggesting the existence of a biological activity threshold. However, the impact of supra-physiological concentrations remains largely unexplored. In this study, we investigated the effects of increasing 2-OHE2 doses (0.05, 0.50, and 5.00 µM) on oocyte meiotic progression and quality. Exposure to 0.50 and 5.00 µM significantly impaired oocyte maturation, while only the highest dose notably reduced the percentage of embryos developing to the blastocyst stage. Morphometric analysis during the GV-to-MII transition revealed altered first polar body morphology, defective asymmetric division, and disruptions in cytoskeletal organization, including enlarged meiotic spindles, increased F-actin cap angles, and aberrant microtubule-organizing centers distribution. These structural alterations were paralleled by distinct changes in cytoplasmic movement velocity patterns observed through time-lapse imaging during meiotic resumption. Together, these findings demonstrate that supra-physiological exposure to 2-OHE2 compromises oocyte maturation and developmental competence by perturbing key cytoskeletal dynamics and cellular architecture necessary for successful meiosis and early embryogenesis. Full article

Plasmonic metasurfaces that convert hydrogen-induced dielectric changes into optical signals hold promise for next-generation hydrogen sensors. Here, we employ simulations and theoretical analysis to systematically assess single-layer, bilayer, and trilayer nanodisk arrays comprising magnesium, palladium, and noble metals. Although monolithic Mg nanodisks show strong optical contrast after hydrogenation, the corresponding surface plasmon resonance disappears completely, preventing quantitative spectral tracking. In contrast, bilayer heterostructures, particularly those combining Mg and Au, achieve a resonance red-shift of Δλ = 62 nm, a narrowed full width at half maximum (FWHM) of 207 nm, and a figure of merit (FoM) of 0.30. Notably, the FoM is boosted by up to 15-fold when tuning both material choice and stacking sequence (from Mg-Ag to Au-Mg), underscoring the critical role of interface engineering. Trilayer “sandwich” architectures further amplify performance, achieving a max 10-fold and 13-fold enhancement in Δλ and FoM, respectively, relative to its bilayer counterpart. Particularly, the trilayer Mg-Au-Mg reaches Δλ = 120 nm and FoM = 0.41, outperforming most previous plasmonic hydrogen sensors. These enhancements arise from maximized electric-field overlap with dynamically changing dielectric regions at noble-metal–hydride interfaces, as confirmed by first-order perturbation theory. These results indicate that multilayer designs combining Mg and noble metals can simultaneously maximize hydrogen-induced spectral shifts and signal quality, providing a practical pathway toward high-performance all-optical hydrogen sensors. Full article