The European Physical Journal Plus最新文献

This study presents a comprehensive first-principles investigation of the structural, electronic, optical, mechanical, phonon, and thermodynamic properties of novel L₃FO (L = K, Rb, Cs) anti-perovskites using density functional theory (DFT) within the GGA-PBE framework, supported by meta-GGA (RSCAN) and hybrid (HSE06) functionals. All compounds crystallize in a stable cubic (Pm-3 m) structure with negative formation energies and tolerance factors between 0.72–0.80, confirming thermodynamic and geometric stability. The calculated band gaps decrease systematically from K₃FO (1.94 eV) to Cs₃FO (1.42 eV) using HSE06, indicating indirect semiconducting behavior suitable for optoelectronic applications. Mulliken and Hirshfeld analyses reveal predominantly ionic bonding with oxygen-centered charge localization. Mechanical analyses demonstrate that Rb₃FO possesses the highest stiffness and isotropy, while K₃FO and Cs₃FO exhibit moderate and soft elastic responses, respectively. Phonon spectra confirm the dynamic stability of K₃FO and Rb₃FO, whereas Cs₃FO shows minor imaginary modes, suggesting marginal instability. Optical analyses reveal strong absorption in the ultraviolet region, with refractive indices and dielectric functions indicating enhanced polarizability for heavier cations. Thermodynamic and molecular dynamics results validate the structural robustness of L₃FO compounds at elevated temperatures. These insights establish L₃FO anti-perovskites as environmentally benign, stable, and tunable materials with promising potential for next-generation optoelectronic, thermoelectric, and renewable energy applications.

Nuclei segmentation and cell classification in histopathologic images (HI) are considered a vital and significant task for cancer analysis and prediction. However, it is a demanding task owing to the nuclei clustering, as well as dimensions with overlapping borders. In this research, the Taylor elk herd optimization with recurrent residual U-Net is developed for nuclei segmentation, and the neuron attention stage-by-stage LeNet forward fractional network (NASLe FF-Net) is developed for cell classification in pan-cancer histology images. At first, the collected HIs are pre-preprocessed with the adaptive Gaussian filtering, and then nuclei segmentation is done through the recurrent residual U-Net with the developed Taylor elk herd optimization, which is the combination of both Taylor series and elk herd optimizer. Subsequently, data augmentation and feature extraction are performed. Furthermore, the extracted features are classified based on the NASLe FF-Net, which combined the neuron attention stage-by-stage net as well as LeNet with fractional calculus. Thus, the NASLe FF-Net achieved an accuracy of 0.944, a true positive rate of 0.968, and a true negative rate of 0.928 with an image size of 40.

We study a cosmological model with a decaying vacuum energy density evolving as (varLambda propto a^{-alpha }), coupled to dark matter via gravitationally induced particle creation. The interaction emerges naturally from the decay process, helping alleviate the cosmic coincidence problem. Assuming a creation rate (varGamma propto H), we derive analytical expressions for key cosmological parameters and constrain the model using Bayesian MCMC analysis. Observational datasets include CMB distance priors, BAO, Pantheon(+) SNe Ia, cosmic chronometers, and structure growth data. The best-fit values of the Hubble constant are (H_0 = 69.85), 69.79, and 68.95 km/s/Mpc for the BASE, +SGR, and +CMB DP datasets, respectively. The model parameters (alpha) and (beta) are found to be of order (10^{-2}) and (lessapprox 10^{-4}), indicating slow vacuum decay and subtle matter creation which becomes negligible at late-times. A smooth transition from decelerated to accelerated expansion is observed through the deceleration parameter and transition redshift. The model respects the Generalized Second Law of thermodynamics and performs comparably or better than (varLambda)CDM under model selection criteria. It offers a minimal and promising extension to standard cosmology, with potential to address key late-time evolution.

A micropolar fluid flow in a striated annular passage is studied—the striations are characterized by shallow circumferential protrusions. The annular passage is bounded by smooth rotating cylinder and a stationary striated shaft. The mean stresses and torque are analytically determined as functions of the parameters specifying the effects of microstructure and geometry. The mean shear difference between the non-zero components of the asymmetric stress tensor is determined to examine the variation due to the microstructure. Furthermore, the analysis is extended to elucidate the behavior of the mean couple stress under the interactive effects of the flow parameters. The mean torque acting on the cylinder is evaluated, and the results indicate that the torque is significantly higher for a micropolar fluid compared to its Newtonian fluid counterpart, considered as a limiting case. The torque increases appreciably with the reduction in the gap width of the annular passage, primarily due to the reinforced influence of the bounding surfaces. This boundary effect is further amplified as the wavenumber of the striations increases, owing to the enhanced flow resistance introduced by the surface topography of the shaft. The percentage variation in the torque reveals that the flow of a micropolar fluid requires less torque when the inner shaft is smooth than when it is striated.

Despite its therapeutic potential, curcumin suffers from poor solubility and low bioavailability. This study overcomes these limitations by developing a multifunctional nanocomposite of nanocurcumin, mesoporous silica, and piezoelectric PVDF nanofibers. By leveraging the electromechanical response of PVDF, mechanical strain generates weak electric impulses that synergistically enhance antibacterial activity. Results demonstrate an exceptional 4.2-log reduction (99.99% kill) of Pseudomonas aeruginosa, significantly outperforming individual components. This synergy is driven by piezoelectric pulses acting as a critical external cofactor, evidenced by an extended bacterial lag phase, minimized stress efficiency index, and a 16-fold increase in phospholipase leakage. These findings confirm that electrostatically induced permeability changes compromise cellular integrity, successfully augmenting curcumin’s antimicrobial efficacy for targeted wound healing.

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To investigate the two-phase flow patterns in rough fractures under the coupled effects of in situ confining pressure and shear displacement, this study selected shale samples from Sichuan for seepage experiments. The findings reveal that for both mated and non-mated fractures, increased confining pressure leads to fracture closure, significantly reducing the equivalent hydraulic aperture. However, under identical confining pressure, the equivalent hydraulic aperture of non-mated fractures remains significantly higher than that of mated fractures. As confining pressure increases and fractures close, the local flow paths of the two-phase fluids change, interfacial resistance increases, and the relative permeability of the water phase shows a marked downward trend. Shear displacement effectively expands flow channels within fractures. When shear displacement reaches 0.6 mm, the relative permeability of the water phase increases by 80%, while that of the gas phase increases by 40%. Based on the analysis of relative permeability evolution under the combined effects of fracture roughness, confining pressure, and shear displacement, we established a relative permeability prediction model accounting for these factors. This model accurately describes the flow behavior of two-phase fluids in rough rock fractures under varying confining pressures and shear displacements.

The accurate capacity and fast life cycle predictions are important concerns for the safe and reliable operation of power sources. Recently, machine learning (ML) and deep learning algorithms have been employed to predict the remaining useful life (RUL) of batteries. This research work proposes a fusion-based hybrid model, which comprises a gated recurrent unit (GRU) with a convolutional neural network (CNN), i.e., GRU-CNN, to enhance the accuracy of RUL prediction of Li-ion batteries. The key feature of a Li-ion battery, i.e., capacity, is identified using linear, ridge, lasso, gradient boosting, and random forest regression ML models. Therefore, the capacity fading feature is used to predict the remaining life of the Li-ion battery. The moving average, exponential moving average (EMA), and Savitzky–Golay (Savgol) methods are applied for smoothing the data using a sliding window of ten data points. The proposed algorithm is trained on 80% of the entire dataset and validated on the remaining 20%. The effectiveness of the proposed hybrid algorithm is validated using two datasets: NASA and CALCE. The proposed model is compared with other existing algorithms, and statistical errors such as MSE, RMSE, and R² are analyzed. It ensures the least prediction error, i.e., the highest accuracy of the proposed model among the existing algorithms for RUL prediction.

Archaeological textiles are often highly fragmented, posing significant challenges for reconstruction and analysis. Human experts traditionally rely on technical analyses, such as thread counting, to match fragments and determine their origins. However, this process is labour-intensive and time-consuming, especially for degraded artefacts such as the Oseberg tapestry from the Viking Age. Despite the potential for automation, existing computational methods have proven insufficiently robust, lacking the nuanced approach that human expertise offers. We propose a framework for automating thread counting in textile images using a feature extractor to capture thread counts in vertical and horizontal directions. Our method models thread counting as a pulse counting problem, inspired by how humans intuitively count threads. It involves analysing traces of pixel values from binary and gradient images to estimate thread density. Our method was compared to measurements by human experts, and while it tended to overestimate thread counts, it showed promising results in clustering fragments. Specifically, 14 out of 27 fragments were grouped in alignment with archaeologists’ hypotheses regarding their origins. Our method offers a valuable tool for digitally reconstructing archaeological textiles, improving efficiency in analysing and matching fragmented artefacts. Future work will focus on refining our approach with advanced thresholding techniques, modelling complex weaving techniques and utilizing hyperspectral images to improve accuracy in identifying complex weaving structures and mixed thread materials.

Associative memory plays a crucial role in cognitive abilities, learning efficiency, and daily life. This paper proposes a new general-purpose memristor model and couples it with a Hopfield neural network (HNN) to construct a HNNM associative memory model. The stability of the HNNM system is analyzed. During the analysis of the parameters of the HNNM system, it is found that the system has rich dynamic characteristics, such as global hyperchaos and coexistence of attractors. By incorporating a multi-level step function into the system equations of HNNM, multi-cavity control of attractors is achieved. The sequence has a high degree of randomness, making it applicable to secure communications and industrial fields. Finally, the physical feasibility of the HNNM system is verified through the DSP platform. The HNNM model proposed in this paper provides a reference for research on associative memory in the field of brain-inspired research.

The rapid pace of industrialization and urbanization has significantly intensified the presence of harmful air pollutants, particularly particulate matter, posing serious risks to public health. Accurate and timely prediction of these pollutants is critical for developing effective mitigation and control strategies. In this study, a two-stage framework is introduced for particulate matter prediction that integrates the strengths of the random forest regressor for spatial components and a deep temporal model of the convolutional gated recurrent unit. Random forest generates initial predictions that capture intricate, nonlinear patterns in the input features, thereby enriching the input representation for downstream learning, wherein the convolutional layer extracts localized spatial features and the gated recurrent unit layers for long-term temporal dependencies. To further refine the learned representations and emphasize the most critical temporal and spatial features, a multi-head attention layer is applied after the convolutional gated recurrent unit outputs. Additionally, regularization techniques are employed within the proposed model component to enhance generalization and mitigate overfitting. The triangular approach explores the interactions among pollutant concentrations, meteorological variables, and station locations, providing a comprehensive spatiotemporal representation of pollutant dynamics. Extensive experiments are carried out on real-world Beijing hourly datasets, using both single and multi-step forecasting. A case study involving the benchmark Indian dataset and statistical significance at a 95% confidence level validates the model’s effectiveness. The results consistently demonstrate the superiority of the proposed framework over existing methods.