The European Physical Journal Plus最新文献

The combined effect of gravity modulation and temperature modulation in Rayleigh–Bénard convection with stress-free boundaries in the presence of a magnetic field in the vertical direction is investigated numerically. Gravity modulation is incorporated by placing a horizontal layer of fluid on a sinusoidally vibrating plate, while temperature modulation is added by assuming that the temperature gradient has a periodic component along with the steady part. The Floquet method is adopted to analyse the problem. When the temperature of the fluid layer surpasses a critical value, oscillatory convection starts. The excited wave at the onset of convection may oscillate synchronously or with half of the forcing frequency. The value of Rayleigh number ((textrm{Ra}_o)) on the threshold of convection is investigated varying various modulation parameters and fluid parameters. The (textrm{Ra}_o) increases with the increase in strength of magnetic field, whereas this value decreases with an increase in any one or both the modulation amplitudes. The union of two modulations leads to two types of bi-critical points, viz. SH and SS. The SH bi-critical point corresponds to the coexistence of a harmonic wave and a subharmonic wave at convection onset, while SS refers to the coexistence of two subharmonic waves with distinct wavenumbers. The second type of bi-critical point is new in a modulated system. Finally, the results of temperature modulation and gravity modulation are discussed separately as a particular case of the original system.

The proliferation of digital imagery across diverse sectors necessitates robust security measures to protect sensitive image data during transmission and storage. This study presents an advanced method for image encryption employing a fusion of chaotic mapping, specifically integrating a novel hybrid chaotic map derived from the logistic map and cosine map. The motivation behind this approach lies in harnessing the inherent complexities of chaotic systems to fortify image encryption against potential threats. The encryption process begins with the scrambling of the image using this newly introduced chaotic map, followed by encryption through a simplified encryption algorithm. Notably, the encryption algorithm is further fortified through the incorporation of particle swarm optimization (PSO). The synergistic utilization of chaotic mapping and PSO enhances the security and robustness of the encryption process, ensuring heightened resistance against potential attacks. This innovative approach demonstrates promising outcomes in safeguarding image data in digital communication systems against unauthorized access and security breaches.

In this study, we explore a modified Hastings–Powell food chain model that incorporates the Allee effect in prey growth and hunting cooperation among middle predators. We analyze the system’s equilibria along with stability, revealing that species persistence and extinction depend critically on the parameters. The continuous system exhibits rich dynamical behaviors, including Hopf as well as saddle-node bifurcations, chaotic dynamics, and bistability. Our findings indicate that lower prey growth rates or Allee thresholds lead to population extinction, while their moderate values yield initially stable coexistence followed by chaotic fluctuations. At higher values, populations ultimately collapse. Similarly, increased hunting cooperation destabilizes the system, causing transitions from stable coexistence to irregular oscillations and extinction. We further examine the discrete-time version of the model, uncovering additional complex dynamics not observed in the continuous system. Our analysis reveals a period-doubling route to chaos, highlighting the system’s sensitivity to parameter variations and initial conditions. To manage these chaotic dynamics, we employ hybrid control methods that effectively suppress bifurcations and stabilize the system under specific conditions. Moreover, we investigate the impacts of environmental noise, demonstrating their critical role in species persistence and extinction. We observe that lower noise intensities permit stable coexistence, while slight increments destabilize the system, leading to species extinction. These results emphasize the fragility of ecological systems under stochastic fluctuations. Overall, our finding underscores the interplay of deterministic and stochastic factors in shaping ecological dynamics and provides valuable strategies for managing complex ecosystems.

This study estimates the mean absorbed dose to the embryo of a pregnant caregiver assisting a child during an ¹⁸F-FDG PET torso examination. Using the FLUKA Monte Carlo (MC) simulations, various clinical scenarios were modeled taking into account different ages of pediatric patients, caregiver’s positions and exposure scenarios. The accuracy and reliability of the simulation framework were validated by comparing its results with ICRP (Ann ICRP 38:7–96, 2008) and AAPM (Madsen et al. in Med Phys 33:4–15. 2006. https://doi.org/10.1118/1.2135911) data. Embryo dose estimates obtained using the FLUKA Monte Carlo code were also compared with those derived from ICRP Publication 116 (Ann ICRP 40(2–5):1–257, 2010). The embryo dose values obtained through the Monte Carlo simulations were consistently lower than the ICRP 116 values. Furthermore, these simulated doses were below the 1 mSv limit for the embryo/fetus over the entire pregnancy period (European Union in Off J Eur Union L 13:1–73, 2014) and the 3 mSv dose constraint for caregivers under 60 years old, as stipulated by Italian law (ITALIA in Gazz Uff Repub Ital Ser Gen, n. 201, 10 Aug. 2020, Suppl. ord. n. 29).

Age structure of trees or plants is a crucial phenomenon because in a forest stand, the capture and storage of carbon dioxide (mathrm {(CO_{2})}) depend on their age and number. This study presents a nonlinear mathematical model to explore the consequences of the capacity of plants/trees to absorb atmospheric (mathrm {CO_{2}}). For the formulation of model, we divide the whole plant population into four groups, namely: small plants (like, herbs and shrubs), early age tree (sapling), middle age tree (mature) and old age tree. To analyze the qualitative behavior of model, we focused on to investigate the region of attraction and permanence of solutions, and carried out the conditions to ensure the existence of all equilibria and their stabilities. Also, it is noticed that for a sudden depletion rate exceeding a critical threshold of small plants may trigger a transcritical bifurcation resulting the disappearance of small plants. To corroborate the analytical results of our mathematical model, numerical simulations are conducted. Effects of crucial parameters are compared for each category of plants/trees to absorb (mathrm {CO_{2}}) which depict the importance of categorization.

Identifying influential nodes in weighted complex networks is essential for understanding information diffusion, control, and robustness, particularly in security-sensitive and dynamic environments. Traditional centrality measures often overlook edge weights, neighborhood diversity, shortest path multiplicity, and structural risks, limiting their ability to capture both local and extended influence. In this paper, we propose Weighted Entropy and Number of Shortest Paths (WEINSP), a security-aware and scalable semi-local centrality measure that integrates weighted local subgraph information, shortest path dynamics, and neighborhood entropy to provide a comprehensive assessment of node influence. WEINSP assigns edge weights based on degree, neighborhood similarity, and normalized connection strength, while evaluating influence through a combination of the weighted number of shortest paths and entropy-based neighbor contributions. An entropy-driven weighting scheme balances the relative importance of each component, ensuring that nodes with higher discriminative power receive greater emphasis. Experiments on real-world networks demonstrate that WEINSP effectively captures both structural and relational significance, provides computationally efficient influence estimation, and enhances the detection of critical nodes in security-sensitive scenarios.

Anchorage control is a key aspect of orthodontic therapy, whose potential impact on treatment precision and patient safety makes it particularly critical during oral radiotherapy. While miniscrews are commonly fabricated from stainless steel, titanium, or titanium alloys, alternative materials such as zirconium-based bulk metallic glasses and polyetheretherketone have recently emerged due to their favorable biomechanical and biocompatible properties. The presence of these devices in the oral cavity can perturb photon-based radiotherapy by altering local dose distributions through enhanced photon interactions. In this study, Monte Carlo N-Particle (MCNP) simulations were employed to quantitatively evaluate photon–matter interactions under two scenarios: an ideal infinite medium across a broad energy range of 0.015–15 MeV and a tissue-equivalent model under Cobalt-60 exposure. The simulations revealed that 316L stainless steel produces the highest photon attenuation and secondary dose buildup (~ 975% increase in secondary photon contribution), followed by Zr₇₀Ni₁₆Cu₆Al₈ BMG and Ti-based materials. In contrast, PEEK exhibited the greatest photon transmission and lowest dose perturbation (~ 8% secondary contribution), demonstrating excellent radiotherapeutic compatibility. The relative radiophysical suitability of the investigated miniscrews under Cobalt-60 irradiation was ranked as follows: PEEK > Ti-6Al-4V ≈ CP Ti > Zr BMG > 316L SS. These findings, together with mechanical and biological considerations, may provide essential insights for optimizing material selection and enhancing treatment accuracy in head and neck radiotherapy involving intraoral implants.

In the phase recognition and calibration of helical antenna elements, the accuracy of phase recognition based on machine vision (MV) is often difficult to ensure due to various factors such as highly reflective surfaces. This paper proposes an improved method for the rapid and precise recognition of phase in mechanical dual-helical phased array antenna (DHPAA) with highly reflective surfaces using brightness-contrast enhancement (BCE). Based on image grayscale value characteristics, BCE enhances edge details via local gamma correction and eliminates most reflection noise by stretching and thresholding grayscale values. After detection by the YOLOv8 object detection algorithm, the ends and centers of the antenna elements are processed with BCE. Then, the Canny operator and the Hough circle detection algorithm are utilized to accomplish edge detection and center point localization of antenna elements. By establishing a rectangular coordinate system to calculate the phase angle of each antenna element, the phase difference of DHPAA can be determined, and thus the specific pointing angle of DHPAA can be obtained. Experiments are conducted on a (12 times 12) antenna array, and systematic errors are corrected by reconstructing images of individual antenna elements into a complete antenna array image. After repeated experiments, the phase recognition results demonstrate stability. Compared with previous work, the recognition errors are predominantly within the range of ((-2^{circ }), (2^{circ })), and the average absolute error (AAE) of phase recognition results is (0.80^{circ }), validating the accuracy of the algorithm. The entire phase recognition algorithm is highly efficient for antenna elements’ images, with frames per second (FPS) being 8.849.

The distinction between weak and strong lensing is somewhat arbitrary, and both regimes are manifestations of the same physical phenomenon: gravity bending the path of light. Nevertheless, these two regimes have to a large extent been treated separately, since they require different approaches. This review traces the development of methods combining weak-lensing and strong-lensing data for joint lens-mass reconstruction, with a particular emphasis on cluster lenses, where both effects occur. We conclude that so-called inverse methods have been successful in merging the two regimes insofar data analysis is concerned. However, a number of improvements seem to be in place. First, not many studies include weak-lensing data beyond shear. In light of the unprecedented quality of the data of JWST and future surveys, this is a clear point of improvement. Especially so since flexion terms have proven useful in determining substructures. Second, considering the amount of data available, and the complexity of nonparametric lenses, automating the processes of lens-mass reconstruction would be beneficial. Towards this end, invoking machine learning seems like a promising way forward.

This study investigates the use of the Monte Carlo method (MCM) for uncertainty evaluation in the realization of reference pressure using a hydraulic piston balance. A Python-based simulation framework was developed, with AI-assisted support provided by ChatGPT to structure and implement the codes. Monte Carlo simulations were conducted at nominal pressure points of 50 bar, 500 bar, 1000 bar, and 1600 bar, yielding detailed distributions of the reference pressure, along with corresponding mean values and standard uncertainties. The simulated results exhibited Gaussian-like behaviour, and the uncertainty values obtained showed close agreement with those calculated using the traditional GUM approach. This confirms that the GUM framework remains valid for this application, while also demonstrating that Monte Carlo analysis provides a more general and powerful tool for propagating uncertainties. The findings highlight both the robustness of piston balances for reference pressure realization and the potential of AI-assisted tools in supporting advanced metrological uncertainty evaluations.