Growing interest to describe the electrical behavior of glial cells, mainly astrocytes, in intact brain tissue poses more and more challenges to commonly accepted belief they only respond in a linear manner in uptake of the excess of extracellular potassium and maintenance of their network equipotentiality. Their highly conductive mutual interconnections via gap junction (GJ) connections introduce yet another class of nonlinear elements. As more studies report nonlinearities in membrane voltage V m dependence of both, the membrane and junctional conductances, the need to formulate minimal dynamical models of their transient behavior is getting more acute. Since ODE models of coupled cells, even in simplest 1-d arrays, require simplified descriptions and small set of parameters, rare quantitative studies on glia makes the task even more difficult. This study attempts to qualify a self-coupled cell, or a glial cell coupled to fixed voltage as useful system for detecting the nature of instabilities and transitions coming from coupling. In a novel biophysical model of coupled astrocyte, we introduce nonlinear kinetics of deactivation for large junctional voltages for the first time. We found that N-shaped nonlinearities and corresponding fold structure in the vector field of isolated cell serves as a baseline on top of which coupling nonlinearities enrich the bifurcation picture. Numerical simulations of 1-d array of coupled astrocytes show that coupling increases the propensity of astrocytic V m to bistability and front propagation. We believe that presented illustrations of possible effects of coupling nonlinearities will motivate neurobiologists to further explore their impact in disease.

Multi-view graph clustering (MVGC) has made great progress in analyzing the interaction patterns of complex networks. Existing methods leverage different graph filters to obtain high- and low-pass signals and implement multi-view fusion. However, these filter-based methods face a scalability issue, which results in insufficient representation discrimination. Besides, they lack view-specific semantics in multi-view fusion, leading to poor information fidelity. To address these limitations, we propose a graph clustering framework with scalable graph filters and view-specific semantic fusion (SGSF-GC). SGSF-GC designs a Beta-based scalable graph filter and cohesion-based fusion mechanism to capture and integrate high- and low-frequency signals. Then SGSF-GC employs class activation mapping to capture semantics of view-specific representations for multi-view fusion. Finally, SGSF-GC conducts KL-based graph clustering. Extensive experiments on five public datasets with eleven baselines verify the utility and superiority of SGSF-GC.

Following significant advances in microscopic and macroscopic single-particle tracking and supercomputing, the theoretical investigation of fluctuations and anomalous dynamics in complex systems is currently of high interest. Stochastic processes and their generalizations represent an important tool for the statistical description of such systems. Modeling random walks and stochastic processes in complex systems, including complex networks and graphs, requires an interdisciplinary approach due to the different applications in various fields, such as physics, biology, chemistry, engineering, computer science, and economy. Various studies of active and passive tracer diffusion, for instance, in biological cells and in heterogeneous and porous media showed that the underlying structure of the environment has a strong effect on the particle movement, leading to anomalous dynamics due to the constrained particle motion or the variation of the local diffusion coefficient and the potential energy function. Moreover, determining optimal search strategies is central in diverse fields, from physics to computer science, from biology to robotics. In particular, random search strategies have been widely observed for animal foraging, in reaction pathways in DNA-binding proteins, in intracellular transport, etc. Furthermore, it has been shown that the resetting of the searcher to its initial position can improve the search strategy by appropriate optimal resetting rate, which results in minimizing the mean first-passage time. This Editorial is meant to serve as an Introduction to this Focus Issue in the form of a mini-review of the field.

The rapid expansion of social media has intensified the spread of rumors, increasing the need for effective rumor detection. Previous research employing Graph Neural Networks (GNNs) fails to resist noise from the intricate information sources and model uncertainty caused by the lack of distribution characteristics, even tually leading to deficiencies in the robustness of models. To address these challenges, this paper proposes a novel robust rumor detection framework (RRD-N). In this framework, we apply data augmentation strategies to generate diverse graph views and leverage contrastive learning pretraining to learn the representative charac teristics of rumors. In particular, we adopt a node sampler and training loss based on Information Bottleneck (IB) theory to improve resistance to noise. Furthermore, a fine-tuning task utilizing Bayesian networks based on variational inference is implemented to obtain the latent distribution, tackling the issue of prediction uncer tainty. We conduct extensive experiments on two public datasets, Twitter15 and Twitter16. The results show performance improvements of at least 1.72% and 1.52% in accuracy compared with twelve state-of-the-art baselines, demonstrating the effectiveness and superiority of the proposed RRD-N. Our code is available at https://github.com/shaieesss/RRD-N.

We consider shear-driven finite-velocity diffusion, both normal and anomalous. In the macroscopic description, this leads to a telegrapher's or Cattaneo-like equation. We analyze the probability density function, and the corresponding moments are obtained analytically. We show that the system exhibits a characteristic crossover of the anomalous dynamics. We also explore corresponding processes under stochastic resetting and find that the systems reach non-equilibrium stationary states in the long time limit that also results in saturation of the evolution of the corresponding mean squared displacement, variance, skewness, and kurtosis.

Here, we investigate whether stochastic resetting, a technique that periodically reverts training to beneficial checkpoints, recently explored in the context of deep learning with noisy labels, can improve the decoding of anomalous diffusion trajectories, a task made challenging by noise and limited trajectory lengths, which often render subtle differences between diffusion processes indistinguishable. First, we find that incorporating stochastic resets of neural network parameters improves the validation loss across different hyperparameters and noise levels, confirming its applicability in trajectory decoding tasks. Second, we find that the relative benefit of resetting increases with trajectory length, and we offer a mechanistic explanation supported by minibatch-gradient ensemble diagnostics that links this observation to the underlying optimization dynamics. Furthermore, we observe that there exists an optimal resetting probability that yields the best performance, highlighting the importance of tuning this hyperparameter. Building on this insight, we introduce time-varying resetting mechanisms that dynamically adjust the resetting probability during training. Our results show that these mechanisms often match or surpass the performance of the best fixed-resetting probabilities and offer a solid basis for designing effective dynamic resetting strategies for regularization.

The rapid evolution of Artificial Intelligence (AI) has catalyzed a transition from reactive, task-specific agents toward Agentic AI – autonomous systems capable of independent reasoning, goal-setting, and recursive planning. Despite extensive research and application, existing literature remains fragmented, often conflating individual autonomy with collective agency. This paper provides a comprehensive systematic review that delineates the transition from individual artificial agency (I-AA) to social artificial agency (S-AA). We introduce a unified architectural framework based on a continuous six-component cognitive loop: perception, reasoning, planning, memory, action, and learning. Our taxonomy categorizes agents into four foundational paradigms: traditional, large language model (LLM)-based, embodied, and human digital twin (HDT)-based, while further defining three advanced Agentic AI configurations: agentic LLM-based, agentic LLM+embodied, and agentic LLM+HDT systems. By distinguishing between localized agency in I-AA and emergent agency in S-AA, we explore the complexities of a macro-scale society of heterogeneous agentic ecosystems.

High-frequency trading (HFT) demands adaptive strategies to navigate volatile markets. Current cutting-edge discrete sub-agent frameworks struggle with rigid market condition allocations, limiting adaptability. We propose a hierarchical framework with an attention-based meta-agent for dynamic sub-agent coordination. By leveraging market embeddings and reinforcement learning, the meta-agent optimally adjusts responsibility weights, enabling adaptive action aggregation across market regimes. Experiments on historical second-level HFT data show that the proposed framework outperforms state-of-the-art baselines, achieving a 42.15% total return and a 4.19 Sharpe ratio. Ablation studies validate the contributions of the dynamic sub-agent assign mechanism and multi-head attention mechanism, highlighting the framework's ability to adapt to market transitions and deliver superior performance.

A space fractional diffusion-like equation is introduced, which embodies the nonlocality in time, represented by the memory kernel and the non-locality in space. A specific example of the nonlocal term is considered in combination with three different forms of the memory kernel. To analyse the probability density function, we utilize the subordination approach. Subsequently, the corresponding continuous time random walk model is presented. Furthermore, we investigate the effects of the stochastic resetting on the dynamics of the process and we showed that in the long time limit the system approaches a nonequilibrium stationary state.

Telegraphers' equation perturbed by a uniformly moving external harmonic impact is investigated to uncover information useful for distinguishing properties of the time evolution patterns that describe either memoryless or memory-dependent modeling of transport phenomena. Memory effects are incorporated into telegraphers' equation by smearing the first- and second-order time derivatives so that the memory kernel smearing the second-order time derivative acts as the smeared derivative of the smeared first-order time derivative. Such a generalized telegraphers' equation (abbreviated as GTE) is solved under initial conditions that specify the values of the solutions and their time derivatives taken at the initial time and boundary conditions that require the sought solutions to vanish either at the x space infinity or the (+l)/(-l) boundaries of a compact domain. The question is which solutions would be classified as traveling or standing waves. To answer this, we consider the Doppler effect and investigate how the frequency and velocity of external sources influence the obtained solutions. Using the short-time Fourier transform allows us to advance the problem and shows that infinite domain solutions to the GTEs, provided by a model example involving the Caputo fractional derivatives (c) D-t(2 alpha) and (c) D-t(alpha) with 0

The rapid expansion of renewable energy demands strategic planning to ensure environmentally and socially responsible vision that reduces conflicts and facilitates the deployment of low carbon energy. Here we advance a methodology for energy planning through the spatial prioritization of barren lands and brownfields for photovoltaic and wind power development, demonstrated via a case study in Macedonia. The study incorporates environmental constraints (e.g., slope, protected areas, biodiversity), technical factors (e.g., solar irradiation, wind speed, proximity to grid and roads), and socio-economic indicators (e.g., available workforce, settlement proximity), utilizing a multi-criteria decision analysis framework integrated with the Analytic Hierarchy Process (AHP). GIS mapping tools were used to evaluate multiple scenarios over 450,000 hectares of land, generating high-resolution suitability maps. A questionnaire was administered to 93 stakeholders from public institutions, the private sector, and academia to determine AHP weighting. The integration of national cadaster data with ecosystem classifications further validated the spatial accuracy and credibility of the analysis. The results reveal substantial land areas suitable for photovoltaic (up to 50 GW) and wind (up to 457 MW) installations, even under strict environmental constraints. Sensitivity analyses underscore the spatial and technical trade-offs when additional exclusions such as Important Bird Areas and Important Plant Areas are considered. This framework offers a replicable and transparent approach for governments seeking to balance energy security, land use efficiency, and ecological preservation. The case study illustrates how data-driven, participatory planning tools can guide equitable and sustainable renewable energy expansion aligned with national energy and climate goals.

We study a generalized Langevin equation framework that incorporates stochastic resetting of a truncation power-law memory kernel. The inclusion of stochastic resetting enables the emergence of resonance phenomena even in parameter regimes where conventional settings (without resetting) do not exhibit such behavior. Specifically, we explore the response of the system to an external field under three scenarios: (i) a free particle, (ii) a particle in a harmonic potential, and (iii) the effect of truncation in the memory kernel. In each case, the primary focus is on understanding how the resetting mechanism interacts with standard parameters to induce stochastic resonance. In addition, we explore the effect of resetting on the dielectric loss.

We study the effects of stochastic resetting on the Reallocating geometric Brownian motion (RGBM), an established model for resource redistribution relevant to systems such as population dynamics, evolutionary processes, economic activity, and even cosmology. The RGBM model is inherently non-stationary and non-ergodic, leading to complex resource redistribution dynamics. By introducing stochastic resetting, which periodically returns the system to a predetermined state, we examine how this mechanism modifies RGBM behavior. Our analysis uncovers distinct long-term regimes determined by the interplay between the resetting rate, the strength of resource redistribution, and standard geometric Brownian motion parameters: the drift and the noise amplitude. Notably, we identify a critical resetting rate beyond which the self-averaging time becomes effectively infinite. In this regime, the first two moments are stationary, indicating a stabilized distribution of an initially unstable, mean-repulsive process. We demonstrate that optimal resetting can effectively balance growth and redistribution, reducing inequality in the resource distribution. These findings help us understand better the management of resource dynamics in uncertain environments.

Understanding production interdependencies is essential for economic modeling, yet existing approaches to constructing large-scale input-output networks are resource-intensive and demand specialized expertise. This study introduces an AI agent-based framework that leverages Large Language Models (LLMs) in conjunction with the Harmonized System (HS) classification of goods to infer and validate production linkages. The method automates the identification of input-output relationships at both the two-digit (HS2) and four-digit (HS4) levels, reducing reliance on manual mapping. The resulting networks are assessed through structural comparison with the World Input-Output Database (WIOD) and statistical analysis of international trade data. Structural validation demonstrates high recall and strong temporal stability, while statistical evaluation confirms that the majority of inferred input-output pairs align with observed trade flows and exhibit positive import-export correlations. These findings indicate that LLMs can effectively reason about and model production processes, providing a scalable and systematic alternative to conventional methods. Overall, this work highlights the potential of LLM-driven approaches to advance the analysis of production structures and offers practical implications for applications in trade analysis, economic modeling, and industrial policy.

We study the first-passage time of the heterogeneous telegrapher's process, which is a stochastic process with a multiplicative dichotomic noise and a position-dependent velocity. As special cases we recover results for heterogeneous diffusion in the Stratonovich interpretation, as well as the standard telegrapher's process with a constant velocity. In the framework of the renewal equation approach, we study the survival probability and the first-passage time density. An exact result for the mean first-passage time in the presence of Poissonian stochastic resetting of the particle to the initial position is obtained as well. An optimal resetting rate is obtained. In this case, the mean first-passage time becomes minimal for every power-law exponent of the power-law position-dependent velocity. We have also observed that the optimal resetting rate increases when the power-law exponent of the velocity decreases. Copyright (c) 2025 EPLA All rights, including for text and data mining, AI training, and similar technologies, are reserved.

In this work we consider a generalised Ornstein-Uhlenbeck (O-U) process for a stochastically driven particle in an harmonic potential which is governed by a Fokker-Planck equation in the presence of a memory kernel. We analyse the probability density function, the mean and the mean squared displacement (MSD) by employing the subordination approach connecting the operational time of the process with the (generalised) laboratory time. We provide analytical results for the mean and the MSD in case of a power-law memory kernel which corresponds to the fractional O-U process. The generalised O-U process in the presence of Poissonian resetting is also investigated by using the renewal equation approach, and the nonequilibrium stationary state approached in the long time limit is obtained. The analytical results are confirmed by numerical simulations based on the coupled Langevin equations.

We consider a shear-driven anomalous diffusion by introducing a memory kernel in the Fokker–Planck equation, which results from the long-tailed waiting time of the particle. We analyze the probability density function and the corresponding moments in the framework of the subordination approach. The moments, obtained analytically, show that the system exhibits characteristic crossover anomalous dynamics. We also explore corresponding process under stochastic resetting, and we find that the system reaches a non-equilibrium stationary state in the long time limit that also results in saturation of the evolution of corresponding mean squared displacement, variance, skewness, and kurtosis.

The increased migration of the last decades has contributed to the generation of diversified societies, raising the need to understand perceptions about cultural diversity and educate on intercultural communication, intending to shrink discrimination. In order to be able to impact and instruct on intercultural competence, professors must possess preparation and development related to interaction in culturally diversified settings. With the aim to recognize the necessity for improving the interaction between professors and students in multicultural environments, this research explores the role of professors’ identity as an antecedent of intercultural competence, further designating their job engagement. The results of the survey with 322 university professors reveal the central role of professors’ commitment to social justice, as the link between their dedication to students’ needs and their own keenness towards continued involvement in working tasks. The implications emphasize the professors’ predisposition to building a productive and harmonious learning environment as a trigger for their enthusiasm, efficacy and commitment while working in culturally diverse environments. Intercultural competence presents the individual commitment to act with respect and avoid issues that could jeopardize communication in a culturally diverse environment, pointing out its importance for personal and professional relationships. In this way, this study observes cultural diversity from the lens of the impact on professors’ engagement, as the critical tool of diversity-competent, aware and sensitive communities in the long run.