The European Physical Journal Plus最新文献

With the extensive application of images in scenarios such as communication, surveillance, and intelligent recognition, the problem of their secure transmission in complex network environments is rendered increasingly prominent. Existing chaotic image encryption methods are primarily dependent on pixel-level perturbation, and the lack of adaptive processing capabilities for target semantic regions results in deficiencies in both overall security and the protection of key regions. A semantic-aware encryption scheme integrating YOLOv13 with chaotic systems is proposed in this paper. The bounding boxes output by YOLOv13 are combined with chaotic systems, and the rapid object detection capability of YOLOv13 is leveraged to generate plaintext-dependent chaotic sequences. Furthermore, parameter-dynamic Arnold scrambling is adopted to effectively rearrange image pixels spatially. Finally, a dual-diffusion approach combining modular arithmetic and cyclic shifting is employed. Through cipher feedback and bit-level shifting, the global diffusion of minute pixel variations is realized by this scheme. Simulation results demonstrate that the proposed scheme is capable of effectively performing image encryption and decryption. These results indicate that the proposed scheme achieves superior security performance, providing a useful reference for integrating deep learning with chaotic systems in image encryption.

This work investigates quantum teleportation of arbitrary unknown low-dimensional multi-particle cat-like states using high-dimensional entangled states as quantum channels. We first establish a protocol for teleporting f-dimensional unknown two-particle cat-like states through d-dimensional maximally entangled three-qudit channels ((d>f)). The sender performs sequential measurements: a single-particle measurement in f-dimensional Hilbert space followed by a non-symmetric basis measurement in ((dtimes f))-dimensional Hilbert space. Based on these outcomes, the receiver applies corresponding unitary operations to reconstruct the original state. Subsequently, we extend this to non-maximally entangled three-qudit channels, enabling probabilistic state reconstruction via auxiliary qubits and targeted operations. Calculated success probabilities demonstrate that the non-maximally entangled approach generalizes the maximally entangled case. Finally, both protocols are generalized for f-dimensional k-particle cat-like state teleportation via d-dimensional ((k+1))-qudit entangled channels.

We present quantum heat machines using a diatomic molecule modeled by a q-deformed Morse potential as a working medium. We analyze the effect of the deformation parameter and other potential parameters on the work output and efficiency of the quantum Otto and quantum Carnot heat cycles. Furthermore, we derive the analytical expressions of work and efficiency as a function of these parameters. Interestingly, our system operates as a quantum heat engine across the range of parameters considered. In addition, the efficiency of the quantum Otto heat engine is seen to be tunable by the deformation parameter. Our findings provide useful insight for understanding the impact of anharmonicity on the design of quantum thermal machines.

Traditional sticky rice-lime mortar, a remarkable ancient building material, derived its effectiveness from the synergistic interaction between lime and sticky rice pulp. While the role of starch has been studied, the specific activation mechanism and microstructural templating effect of its protein components (constituting about 10% of rice) remained unclear. This study systematically investigated the use of alkali-treated sticky rice pulp, a process inspired by traditional slaking practices and modern industrial starch modification, to activate these proteins. The results showed that alkaline pretreatment enhanced protein dissolution, induced a conformational change, and increased surface hydrophobicity. Critically, these activated proteins acted as organic templates, directing the formation of uniform CaCO₃ spheres in solution and promoting a reinforced, interlocking microstructure in the hardened mortar. Macroscopically, the mortar with alkali-treated sticky rice pulp demonstrated superior workability, optimal dimensional stability (lowest drying shrinkage of 2.3% and no cracking), and improved durability, although its long-term strength development was moderated by reduced carbonation. This work revealed the alkali-activation mechanism of proteins in sticky rice pulp and their consequent templating role, providing a scientific basis for optimizing traditional materials for cultural heritage conservation.

To investigate the regulatory mechanisms of additives on the physicochemical and mechanical properties of aluminum/epoxy (Al/Ep)-based reactive materials, KMnO₄/TiH₂ modified Al/Ep materials were prepared via vacuum curing process. The physicochemical parameters (decomposition temperature, specific heat capacity, density, sound velocity, heat of reaction) and quasi-static/dynamic mechanical behaviors were characterized using TG-DSC, densimeter, ultrasonic material characterization (UMC), calorimeter, universal testing machine, and split Hopkinson pressure bar (SHPB). Meanwhile, a Zhu–Wang–Tang (ZWT) constitutive model coupled with damage was established and verified by writing a VUMAT subroutine in Fortran. The results show that KMnO₄ reduced the material’s decomposition temperature and increased the specific heat capacity but decreased the reaction heat, while TiH₂ maintained high decomposition temperature, improved the density, and enhanced the energy storage capacity. Under quasi-static compression, KMnO₄ and TiH₂ increased the yield strength by 21.3% and 26.4%, respectively. Under dynamic compression, KMnO₄ reduced the peak stress due to pore formation and endothermic effects caused by oxidative decomposition, whereas TiH₂ improved the peak stress through particle strengthening, and both materials exhibited strain rate sensitivity. The simulation results of the established ZWT model show high consistency with the experimental data. In summary, KMnO₄ and TiH₂ realize controllable regulation of Al/Ep material properties through chemical–physical synergy.

In this study, the radioactivity concentration of naturally occurring radionuclides found in soil samples collected from different suburban areas of Mekelle City was determined using HPGe detector. The measured concentrations of the radioactivity of ²²⁶Ra, ²³²Th, and ⁴⁰K in the soil samples varied from 11.05 ± 0.86 to 53.05 ± 4.02 Bq kg⁻¹, 12.50 ± 1.31 to 171.60 ± 17.50 Bq kg⁻¹, and 166.30 ± 7.30 to 613.7 ± 28.1 Bq kg⁻¹, respectively. The mean activity concentration of ²³²Th slightly exceed the standard safe value of 45 Bq kg⁻¹, but ²²⁶Ra and ⁴⁰K are below the safe values of 32 and 420 Bq kg⁻¹, respectively. The absorbed dose rates for all samples are less than the recommended safe values of 59 nGyh⁻¹. The associated annual indoor and outdoor effective dose equivalent obtained ranged from 0.103 ± 0.005 to 0.74 ± 0.05 mSv y⁻¹ and 0.026 ± 0.001 to 0.18 ± 0.01 mSv y⁻¹, respectively. The values for the radiation hazard indexes are below one. Beside the high concentration of ²³²Th, the results indicate that, Mekelle soil has no significant health effects.

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The COVID-19 pandemic has posed significant challenges to healthcare systems worldwide, particularly for populations living with chronic infectious diseases such as HIV. In this study, a deterministic compartmental model is formulated to investigate the transmission dynamics of HIV–COVID-19 co-infection. A key feature of the model is the inclusion of an exposed HIV compartment, representing recently exposed but non-infectious individuals, thereby allowing the incorporation of preventive interventions such as post-exposure prophylaxis. The model is shown to be mathematically well posed, with solutions that remain positive and bounded within an invariant region. Threshold dynamics are characterized through the derivation of basic reproduction numbers, and the local and global stability properties of the disease-free and endemic equilibria are analyzed. Model parameters are estimated using reported epidemiological data from India via nonlinear least-squares fitting. Numerical simulations support the theoretical results and demonstrate that HIV-induced immunosuppression substantially increases COVID-19 transmission and disease burden, leading to higher hospitalization levels among co-infected individuals. An optimal control framework is further developed to evaluate time-dependent intervention strategies for HIV and COVID-19. The results indicate that combined control measures are more effective than single-disease interventions in reducing overall infection levels. These findings highlight the importance of integrated public health strategies for managing co-circulating infectious diseases.

Ground-level ozone is a major air pollutant whose concentrations are influenced by meteorological conditions and other air pollutants. Understanding the causal relationships among ozone, meteorological variables, and other air pollutants is important for two reasons: (1) accurate prediction and forecasting and (2) effective air quality management. This study employs a combination of Granger causality testing, cross-correlation analysis, multiple linear regression, deep NARMAX modelling, and structural equation modelling to investigate the causal and time-lagged effects of meteorological and anthropogenic factors on ozone formation and depletion. A five-year dataset of hourly measurements of ozone, other air pollutants, and meteorological parameters is analysed for Craiova, Romania, to identify the dominant drivers of ozone variability. The results provide a basis for developing improved predictive models and offer insights into the delayed effects of air pollutants and weather conditions on urban ground-level ozone concentrations, supporting informed strategies for air quality management.

Microfluidics technology, as an emerging method for cell/microparticle manipulation, offers advantages such as low cost, simplicity, and high efficiency. In previous research, we developed multi-channel swirl microfluidic chips that enabled various manipulation functions, including particle capture, transfer, assembly, sorting, and enrichment. To achieve more precise control of microparticle, this paper investigates the relationship between microparticle’s physical properties and manipulation performance based on a two-microchannel swirl microfluidic chip. First, we conduct physical mechanisms analysis to reveal the force applied to microparticle in a swirl. We analyze the correlation between microparticle parameters and manipulation performance, specifically the capture time and post-capture angular velocity. Additionally, we establish a predictive model between these variables. The results show that the model can achieve bidirectional predictions between microparticle property and manipulation performance, with a mean squared error of less than 0.01. This study introduces data-driven to enrich the traditional approaches of microfluidic chips for particle manipulation and analysis. The results provide guidance for advanced microfluidic chips.

Graphical Abstract

This paper investigates the relationship between microparticle’s physical properties and manipulation performance based on a two-microchannel swirl microfluidic chip. We analyzed the correlation between particle parameters and their manipulation performance, specifically the capture time and post-capture rotational angular velocity. This study introduces data-driven and machine learning, enriching the traditional approaches of microfluidic chips for particle manipulation and analysis. The results provide guidance for manipulating particles in microfluidic chips and offer valuable insights for future applications in biosensors, biomedicine, and chemistry research.

Owing to their unique nonlinear dynamical characteristics, discrete memristive chaotic systems have received extensive attention in recent years. In this study, a two-dimensional discrete memristive chaotic system with non-equidistant initial value offset boosting characteristics (2D-NDMS) is constructed, and its dynamical distribution and spectral entropy complexity are systematically analyzed. The study finds that in the process of increasing the offset of initial values, the system exhibits alternating offsets between two non-equidistant regions, accompanied by local abnormal offset behavior during region switching. Further analysis of the system's evolution from transient chaos to steady state reveals the existence of dual-channel transient chaotic paths, which eventually converge to the same non-connected stable attractor. In terms of engineering applications, this study implements the chaotic attractor of the system on the STM32 hardware platform and applies it to the design of a pseudorandom number generator, with the generated sequences passing the NIST tests. Furthermore, the system is applied to image encryption, and the security of the encryption system is evaluated from multiple perspectives.