The European Physical Journal B最新文献

We present a novel method for analyzing the structural organization of protein families by integrating random matrix theory (RMT) and network theory with the physiochemical properties of amino acids and multiple sequence alignment. RMT distinguishes significant interactions between amino acids from background noise, pinpointing coevolving positions likely crucial for protein structure and function. This property-based approach captures both short and long-range correlations, unlike previous methods that treat amino acids as mere characters. The eigenvector components of eigenvalues outside the RMT bound deviate from typical RMT observations, offering critical system information. We quantify the information content of each eigenvector using an entropic estimate, showing that the smallest eigenvectors are highly localized and informative. These eigenvectors form clusters of biologically and structurally significant positions, validated by experiments. By creating networks of amino acid interactions for each property, we uncover key motifs and interactions. This method enhances our understanding of protein evolution, interactions, and potential targets to modulate enzymatic actions. We study two protein families Cadherin-4 and Betalactamase families which display two extreme characteristics one nearly random and the other very structured or organised.

The square-lattice (J_1)-(J_2) XXZ model with the ring-exchange interaction K was investigated numerically. As for the hard-core-boson model with the nearest-neighbor hopping (J_1/2), namely, the (J_1)-K XY model, it has been reported that the ring exchange leads to a variety of exotic phases such as the valence-bond-solid (VBS) phase. In this paper, we extend the parameter space to investigate the phase boundary between the XY (superfluid) and VBS phases. A notable feature is that the phase boundary terminates at the fully frustrated point, (J_2/J_1 rightarrow 0.5^-). As a scaling parameter for the multi-criticality, the distance from the multi-critical point (delta (ge 0)) is introduced. To detect the phase transition, we employed the high-order fidelity susceptibility (chi ^{(3)}_F), which is readily evaluated via the exact-diagonalization scheme. As a demonstration, for a fixed value of (delta ), the XY-VBS criticality was analyzed by the probe (chi ^{(3)}_F). Thereby, with properly scaling (delta ), the (chi ^{(3)}_F) data were cast into the crossover-scaling formula to determine the multi-criticality.

We propose a method that can generate customer trajectories and purchasing behaviors in retail stores simultaneously using Transformer-based deep learning structure. Utilizing customer trajectory data, layout diagrams, and retail scanner data obtained from a retail store, we trained a GPT-2 architecture from scratch to generate indoor trajectories and purchase actions. Additionally, we explored the effectiveness of fine-tuning the pre-trained model with data from another store. Results demonstrate that our method reproduces in-store trajectories and purchase behaviors more accurately than LSTM and SVM models, with fine-tuning significantly reducing the required training data.

With the popularity of social media, the speed and scale of information dissemination has increased exponentially, among which rumors have become a prominent issue that arouses public concern. Due to the lack of comprehensive consideration of population flow, propagation mechanisms, and effective rumor refutation strategies, as well as related dynamical analysis in traditional models, we propose an extended ISR rumor spreading model based on the traditional SIR (Susceptible, Infected, Recovered) model. The average field equation is established by introducing the flow of population, trust and suspicion mechanism and two rumor-refuting strategies, and the basic reproduction number of homogeneous and heterogeneous networks and the stability of the rumor-free equilibrium point are analyzed. Finally, our experimental simulation shows that the trust mechanism increases the speed and scale of the spread of rumors in the network, while the suspicion mechanism inhibits the spread of rumors and gradually reduces the spread scale. In addition, the dual rumor refutation strategy has a good inhibition effect on the spread of rumors, especially the direct rumor refutation by the disseminator, and has been verified in different models. These findings enhance our understanding of rumor dynamics and suggest strategies for mitigating misinformation in social networks.

Here, we show that the ferro(para)magnetic and non-collinear antiferromagnetic interfaces contribute to oscillating signals observed in the magnetoresistance. We associate the effect with the fact that the spins on the surface of IrMn(_3) produce instability in the surface magnetoresistance of the material, which is sensitive to the magnetic field. We carried out experiments using bilayers of IrMn(_3) under permalloy (Py) and IrMn(_3) under platinum (Pt). The oscillations were intensely evident at the Py/IrMn(_3) interface and less explicit at the Pt/IrMn(_3) interface. We carried out the experiments using pulsed current, with a square pulse width of 1 (mu )s and amplitudes of (I_{AC}) = 20 (mu )A to 20 mA. The oscillating signals are proportional to the crystallographic direction of the material, the ferromagnetism of the material adjacent to IrMn(_3), and sensitive to the amplitude of the pulsed current. We believe that observation is a way of transmitting encoded information through magnetoresistance.

We analyze charge and field equilibrium distributions in an insulating medium with ions confined by two flat adsorbing–desorbing electrodes without an external electric field. The equilibrium charge density profile of the sample is determined by considering the accumulation of mobile charges at the surface, which generates a surface electric potential. Charging dynamics are explored using a kinetic balance equation, resulting in a time-dependent net charge surface and an inhomogeneous Volterra integral equation of the second kind, and determining the surface electric potential in the half-space approximation. The results show a non-monotonic time dependence for the surface electric potential due to adsorption–desorption processes. The analysis is extended to a finite sample and reveals the presence of a maximum in the time behavior of difference of potential when only one type of ion is selectively adsorbed. The trend is instead monotonic when one electrode only adsorbs positive, and the other one adsorbs only negative ions. This approach may be used as a convenient theoretical tool for further investigation of charge accumulation on the surfaces of electrolytic cells.

We consider a spinless t-(t') ionic Hubbard chain at 1/2 filling and large hopping ratio (t'/t). In this limit, the model adequately maps onto a weakly coupled triangular ladder with a potential interchain bias. The low-energy properties of the system are formed due to the interplay of geometrical frustration, correlations and charge imbalance. We derive the effective field-theoretical model to study universal properties of the model in the scaling limit. We show that at full dynamical frustration, the ground state of the ladder represents a repulsive version of the Luther–Emery liquid with dominant orbital antiferromagnetic correlations exhibiting the slowest power law decay in the ground state. Pairing correlations also display algebraic order but are subdominant. At an incomplete dynamical frustration, a finite commensurability gap is dynamically generated, and the fluctuating OAF transforms to a long-range ordered state with a spontaneously broken time-reversal symmetry. The mass gap in the spectrum of relative density fluctuations gets suppressed upon increasing the potential bias.