Amid escalating global food security challenges, global food trade has become increasingly pivotal in securing national food supplies and stabilizing nutritional availability. However, the identification of food export drivers remains hindered by fragmented indicator systems and the limitations of linear causality methods in capturing complex, nonlinear dynamics. In this study, we construct a comprehensive, multidimensional indicator framework encompassing economic, agricultural, and environmental factors, and apply the nonlinear Convergent Cross Mapping (CCM) method to identify robust causal relationships between these variables and global food exports. The results show that economic factors—particularly GDP, GDP per capita, and the Consumer Price Index—are the most influential drivers of food exports, while renewable water resources show similarly strong effects and agricultural indicators such as arable land and food production exert moderate effects; in contrast, precipitation shows a weak causal signal. Furthermore, developed countries tend to rely more on economic efficiency and technological advancement, whereas developing countries are more dependent on agricultural production capacity and labor inputs, reflecting significant heterogeneity in export drivers across development levels. This research expands the methodological toolkit for international trade analysis, offers new insights into the causal mechanisms underlying food exports, and provides empirical guidance for tailoring food trade and agricultural policies to development contexts.

The emergence of excessive social media use has raised significant concerns in the twenty-first century, necessitating urgent attention to mitigate potential adverse consequences. To address this challenge, a range of preventive strategies, such as advertising campaigns and awareness programs, are being utilized to highlight the negative impacts of digital technologies. Employing advanced mathematical methods and terminology can significantly contribute to encouraging healthier lifestyles and preventing related health problems. Consequently, this article investigates the fractional-order mathematical modeling to comprehend social media addiction across various user classes, such as non-users, exposed individuals, social media users, professionals, and addicted users. In the realm of qualitative analysis, the study establishes the existence, uniqueness, non-negativity, and boundedness of solutions of the model. Within the framework of the fractional-order model, essential elements are identified, including the equilibrium points and the fundamental reproduction number. To evaluate the stability of the social media addiction model across these user categories, the study employs the fractional Routh- Hurwitz criterion. Furthermore, the research proves the global asymptotic stability of all equilibria through the development of inventive Lyapunov functions. Additionally, the numerical simulation of the fractional-order model provides a comprehensive understanding of the dynamics of social media addiction within the distinct user classes.

This paper presents a comprehensive review of the Social Laser Theory (SLT) as a natural extension of the broader framework of Quantum-Like Modeling (QLM). While QLMapplies the mathematical formalism of quantum theory—such as Hilbert space representations, interference, and non-commutative observables—to model context-dependent and non-classical phenomena in cognition, decision-making, and social behavior, SLT advances this approach by integrating concepts from quantum field theory. The theory conceptualizes social systems as ensembles of “social atoms” capable of absorbing and emitting quantized units of social energy. Under conditions analogous to population inversion in physical lasers, external informational stimuli (e.g., media signals or mobilizing rhetoric) can trigger coherence across the population, resulting in large-scale, synchronized collective actions such as protests or ideological shifts. SLT thus provides a formal framework for understanding the amplification and coherence of social energy leading to abrupt phase-like transitions in collective behavior. Beyond its metaphorical appeal, the theory proposes measurable quantities and predictive parameters that may support empirical diagnostics of sociopolitical dynamics. By bridging micro-level psychological processes with macro-level sociological phenomena, SLT extends QLM into the domain of complex social systems, offering a mathematically grounded paradigm for interpreting rapid transformations in contemporary societies.

This paper introduces G-Impact, an agent-based model that combines modeling of household consumption and belief diffusion. Household decisions integrate personal impacts (quality, cost), perceived consequences (climate change, human responsibility), and social norms. The evaluation of these different criteria relies on household beliefs, which can be exchanged during social interactions. These beliefs can be used to explain household decisions on a macro and micro scale, and thus to target information or incentive policies. The model is applied to dietary choice in France, among the omnivorous (INCA3), the flexitarian and the vegetarian diets. The costs and greenhouse gases emissions of the different diets are initialized from real data, initial beliefs of households are derived from opinion surveys. We observe after 5 years of simulation a significant increase in the proportion of flexitarians, and a slight increase in the proportion of vegetarians. We use the model to design an information campaign in the aim of making INCA3 dieters switch to the flexitarian diet, and analyze its impact.

In this paper, we use an entropic formalism to compare the temporal strike patterns of two important unions in Córdoba city, Argentina. Despite being uncorrelated, the combined impact of the protests is not simply additive. This is reflected in the form of emerging nonlinear effects. The formalism allows for a study of system’s controllability aspects described in terms of entropic regimes. The analysis enables the characterization of correlations between overlapping periods of protest activity. It makes possible to quantitatively point out how in principle low-intensity seasons of independent strikes can often form a joint pattern of protests with higher social conflict.

Soft set theory offers a systematic method for addressing imprecision and uncertainty by classifying set elements based on specific parameters. In the realm of semigraph theory, soft semigraphs employ this method, providing a parametrized perspective that has greatly enhanced the field through efficient parameter management. Building on this foundation, disemigraphs extend semigraphs by incorporating directional relationships among vertices, making them ideal for modeling situations where the sequence and direction of connections are essential. However, existing models such as fuzzy graphs or soft graphs often fall short in capturing both the directionality and parameter-based uncertainty simultaneously, especially in networks involving multiple interaction types and layered relational structures. This paper introduces the concept of soft disemigraphs, a novel framework that integrates the flexibility of soft set theory with the directional rigor of disemigraphs. This integration enables a more nuanced, structured, and context-aware representation of complex systems, particularly where directionality, hierarchy, and imprecision coexist. We explore various operations on soft disemigraphs, including extended union, restricted union, extended intersection, and restricted intersection, and analyse their structural properties. These operations provide deeper insights into the dynamics of parametrized, directed networks and offer a clear advantage in modeling real-world systems. Furthermore, we demonstrate the practical applicability of this framework through ecological network modeling, particularly in the representation of food webs, where species interactions are inherently directional, hierarchical, and context-sensitive.

The phenomenon of gentrification of an urban area is characterized by the displacement of lower-income residents due to rising living costs and an influx of wealthier individuals. This study presents an agent-based model that simulates urban gentrification through the relocation of three income groups — low, middle, and high — driven by living costs. The model incorporates economic and sociological theories to generate realistic neighborhood transition patterns. We introduce a temporal network-based measure to track the outflow of low-income residents and the inflow of middle- and high-income residents over time. Our experiments reveal that high-income residents trigger gentrification and that our network-based measure consistently detects gentrification patterns earlier than traditional count-based methods, potentially serving as an early detection tool in real-world scenarios. Moreover, the analysis highlights how city density promotes gentrification. This framework offers valuable insights for understanding gentrification dynamics and informing urban planning and policy decisions.

When considering a specific event, news that accurately reflects the ground truth is deemed as real information, while news that deviates from the ground truth is classified as false information. False information often spreads fast due to its novel and attention-grabbing content, threatening our society. By extending the Susceptible-Infected (SI) model, our research offers analytical decision boundaries that enable effective interventions to get desirable results, even when intermediate functions cannot be analytically solved. When assessing intervention costs using the model, the results indicate that the sooner we intervene, the lower the overall intervention cost tends to be.

Financial stock returns correlations have been studied in the prism of random matrix theory to distinguish the signal from the “noise”. Eigenvalues of the matrix that are above the rescaled Marchenko–Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis, we use complex network analysis to simulate the “noise” and the “market” component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated “market” random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to [Formula: see text] on short-time scale.