The European Physical Journal Plus最新文献

Predator movement determined by prey density is known as prey-taxis, which plays a crucial role in ecosystem dynamics. This study considers the effect of prey-taxis in a predator–prey system with hunting cooperation in predators. Linear stability analysis is used to establish the condition for diffusive instability. Analytical investigation was carried out in the temporal system to investigate the existence of biologically possible equilibrium points and their stability, positivity, and boundedness of solutions and the presence of transcritical and Hopf bifurcations. The transcritical and Hopf bifurcations observed in the predator–prey system for predator death rate and predator hunting cooperation, respectively. As the level of hunting cooperation rises, the system becomes unstable. The stability switches between predator-free and interior equilibrium as predator death rates increase. In the spatially extended system, prey-taxis plays a crucial role in Turing pattern formation.

This paper presents an analytical technique and NLSE-NLSE transformations to discover new spatial soliton solutions, Akhmediev breather (AB) solutions, and first-order rogue waves in photorefractive media backgrounds. The study includes non-centrosymmetric and centrosymmetric photorefractive media, as well as media exhibiting both linear and quadratic electro-optic effects. Using the proposed technique, we discovered exact spatial soliton solutions that can be steered in all these media. Exact AB solutions for all three media types were derived using NLSE-NLSE transformations. We found that AB solutions exhibit a plane-wave profile when photorefractive nonlinearity is high and transform into Peregrine solitons (first-order rogue waves) when nonlinearity is low. By taking the limit of the electro-optic parameter to zero from the AB solutions, we derived analytical solutions for first-order rogue waves across all the media. The characteristics of spatial solitons, AB, and first-order rogue waves, as examined in each media background, show significant differences according to the intrinsic nature of each medium. The NLSE-NLSE transformation has proven to be a powerful and invertible tool, and, in addition to its application in the AB case, can also be widely used to recover spatial soliton and first-order rogue wave solutions found in this study.

In this work, we extend the Kugo–Ojima–Nakanishi covariant operator formalism to quantize two higher derivative systems, considering their extended phase space structures, more specifically, the one describing spin-0 particles by a vector field and the generalized electrodynamics. We investigate the commutator structure of these theories and present the definition of their physical Hilbert subspaces. The first model presents a reducible gauge symmetry, implying the necessity of two sets of auxiliary fields. The massless limit is also carefully analyzed. After this prelude, the generalized QED(_4) can be investigated with such machinery. Regarding the interacting regime, the positive norm subspace is no longer time invariant, since the interaction can create negative norm states from an initially ghost-free one. Then, we furnish an alternative description of the situation by analyzing a set of spectral representations highlighting the lack of positivity associated with the well-known ultraviolet improvement. Finally, based on these efforts and also on recent discussions about Lee–Wick-like models, we prove that it is possible to establish a specific higher derivative interacting model compatible with establishing a time-invariant positive norm subspace.

Cholera is mainly spread by the ingestion of contaminated food or water, especially in areas where poor sanitation is prevalent. The bacteria responsible for cholera, Vibrio cholerae, are observed to multiply in environments lacking proper water treatment and sewage management systems. A novel exploration of machine learning solutions is presented in this paper, with supervised neural structures being applied to a nonlinear stochastic cholera epidemic (SCE) model that incorporates quarantined impact and Brownian motion uncertainty. Artificial neural networks optimized by the Levenberg–Marquardt algorithm (ANNs-LMA) are utilized to predict the dynamics of the SCE model. The probabilistic dynamics of the representative nonlinear SCE model are described in terms of susceptible, infected, quarantined, and recovered individuals, along with the bacterial population represented by the concentration of cholera bacteria in water and food sources. Synthetic data for the execution of ANNs-LMA are generated using the Euler–Maruyama numerical method, with variations in key parameters, including the migration rate into the susceptible group, the transmission rate of cholera through contaminated food and water, the rate at which immunity is lost, natural death rates, the disease progression, and mortality rates among infected individuals, and the recovery or severe disease progression rates among quarantined individuals. The effectiveness of the proposed ANNs-LMA approach is demonstrated by its close alignment with the reference numerical results of the SCE model, as indicated by an error value approaching zero, and is further validated through various assessment metrics, including mean square error-based convergence, adaptive governing parameters, error histograms, and autocorrelation analyses.

This research conducts a computational analysis of a stochastic scabies model using the Legendre spectral collocation technique (LSCM). By including stochasticity into the model via the suggested stochastic differential equations, we are confiscating the random fluctuations required for disease growth and spread. The stability, convergence, and accurate characteristics of the LSCM are meticulously examined, showcasing its efficacy in addressing complicated epidemiological problems. Furthermore, this mathematical model is used to explain the transmission dynamics of scabies infection in the population with standard incident rate. The dynamics of scabies are illustrated schematically, and then an ordinary differential equation (ODE) is derived using the law of mass action. Positiveness, boundedness, and equilibrium points have been analyzed. Next-generation techniques are used to determine the reproduction number. Sensitivity analysis is also accomplished to investigate the impact of various parameters of reproduction number. Disease-free equilibrium exists asymptotically in local whenever (R_{0} < 1). The accuracy and effectiveness of the constructed stochastic computing using neural networks are shown by a comparison of the results derived from the dataset utilizing the spectral collocation approach. Our research demonstrates that mitigating the severe impacts of scabies requires prompt detection and timely intervention. Additionally, our mathematical model serves as a valuable tool for refining disease management strategies. This study enhances our understanding of scabies dynamics, offering actionable insights into public health planning and epidemic control.

We carry out a detailed study of medium modifications on transverse momentum spectra and angular correlations between a large transverse momentum hadron and a (Z/gamma) trigger in relativistic heavy-ion collisions within a perturbative QCD parton model improved by the Sudakov resummation technique. The total energy loss of a hard parton propagating inside the medium is employed to modify the fragmentation function, while the medium-induced transverse momentum broadening is included in the resummation approach, and both of them are related to the jet transport parameter and obtained by the higher-twist formalism. We obtain good agreements with the existing data on transverse momentum and azimuthal angular correlations for the (Z/gamma)-hadron pairs in pp and AA collisions and predict the correlations for the (gamma)-hadron in central PbPb collisions at 5.02 TeV. The numerical analyses for the (Z/gamma)-hadron in central PbPb collisions show that the normalized angular distribution is decorrelated due to the medium-induced transverse momentum broadening; however, the angular correlation is enhanced due to the parton energy loss, namely anti-broadening. The observed modification of the angular correlation is a result of the competition between the broadening and the anti-broadening. This work provides a reliable theoretical tool for a comprehensive and precise study of jet quenching in relativistic heavy-ion collisions.

A new observable, (S_{T}), is introduced in terms of the sum of the transverse momentum of charged particles ((sum _{i} {p_{{T}_{i}}}) ) produced in proton proton (p−p) collisions at LHC energies to probe the underlying events (UE). The UE are defined as those aspects of p−p collisions that are not attributed to the primary hard scattering process, but rather to the accompanying interactions of the rest of the proton. The conventional approach of studying underlying events is usually carried out by defining topological regions with respect to the leading particle in an event. The transverse region is generally sensitive to UE and various classifiers have been used to discriminate the extent of UE activity regions. The production of identified particles like (pi ^{pm }), (K^{pm }), p, (K_{S}^{0}), and (Lambda ^{0}) are studied in different ranges of transverse activity classifier in p−p collisions at (sqrt{s} = 13 ) TeV using pQCD inspired PYTHIA 8 event generator. A comparative analysis of the identified particle spectra, mean multiplicity and mean transverse momentum has been carried out with respect to (S_{T}) and the performance of this new observable is gauged by comparing the results with previously defined (R_{T}) observable.

This work presents an upgrade to the methodology adopted to investigate the provenance of the raw lapis lazuli material used in antiquity for carving precious artefacts. Samples from archaeological excavation contexts frequently display superficial degradation processes affecting the crystals of the mineral phases useful for provenance attribution (especially pyrite). To address this issue, an innovative workflow has been developed, centred on the application of X-ray micro-computed tomography (μ-CT) and micro-ablation treatments with a pulsed laser source prior to investigation with ion beam analysis (IBA). High-resolution μ-CT is employed to evaluate the alteration state of pyrite crystals within the entire volume of the lapis lazuli rock, and, if required, to identify the most suitable crystals on the surface for subsequent laser treatment. The micro-ablation procedure aims to create a small breach in the superficial altered layer (the irradiated areas are approximately 65 × 65 μm²), thereby exposing the preserved crystal beneath and allowing for the analysis of its trace element contents with IBA. The methodology of the workflow is presented, together with its first application to archaeological lapis lazuli material: three precious beads from the ancient Royal Cemetery of Ur (Mesopotamia, 3rd millennium BCE). The results are complemented by the application of a provenance protocol already validated that proved, for the first time using a micro-invasive analytical approach, a match between the Afghan quarry district and the raw material used to carve these beads.

The discovery of novel superconducting materials is a long-standing challenge in materials science, with a wealth of potential for applications in energy, transportation and computing. Recent advances in artificial intelligence (AI) have enabled expediting the search for new materials by efficiently utilizing vast materials databases. In this study, we developed an approach based on deep learning (DL) to predict new superconducting materials. We have synthesized a compound derived from our DL network and confirmed its superconducting properties in agreement with our prediction. Our approach is also compared to previous work based on random forests (RFs). In particular, RFs require knowledge of the chemical properties of the compound, while our neural net inputs depend solely on the chemical composition. With the help of hints from our network, we discover a new ternary compound Mo₂₀Re₆Si₄, which becomes superconducting below 5.4 K. We further discuss the existing limitations and challenges associated with using AI to predict and, along with potential future research directions.