Early warning signals for loss of control in complex systems

Jasper J van Beers, Marten Scheffer, Prashant Solanki, Ingrid A van de Leemput, Egbert H van Nes, Coen C de Visser

PNAS 123 (27) e2608847123

From aircraft to power grids, controlled systems form a crucial part of human societies. Nonetheless, catastrophic failures happen. Many of those arise from the accumulation of incremental problems, such as natural wear and tear, that can go unnoticed until it is too late. We demonstrate that generic indicators of resilience can detect growing instabilities in damaged drones. The generic nature of our approach makes it compatible across diverse controlled systems. This not only allows for on-the-fly warning of instability but also facilitates anomaly detection during manufacturing and promotes proactive maintenance. A complementary application is to use our indicators for exploratory design, allowing one to “tinker” with systems through small adjustments and sensing quickly whether those worsen or improve system resilience.

Read the full article at: www.pnas.org

Strongly Clustered Random Graphs via Triadic Closure: Degree Correlations and Clustering Spectrum

Lorenzo Cirigliano, Gareth J. Baxter and Gábor Timár
Complexities 2026, 2(2), 13;

Real-world networks often exhibit strong transitivity with nontrivial local clustering spectra and degree correlations. Such features are not easily modeled in tractable network models, creating an obstacle to the theoretical understanding of such complex network structures. Here, we address this problem using a model for strongly clustered random graphs in which each triad of a random network backbone is closed with a certain probability. Despite the intricate loopy local structure of the graphs obtained, we provide exact expressions for the local clustering spectrum and the degree correlations, filling the gap in the theoretical description of this model for random graphs. In particular, we find positive degree assortativity accompanying high transitivity, and nontrivial structure in the clustering spectrum. Exact asymptotic analytical results, obtained for uncorrelated locally tree-like backbones, are complemented with extensive numerical characterization of finite-size effects.

Read the full article at: www.mdpi.com

Sketch of a novel approach to a neural model

Gabriele Scheler

There is room on the inside. We present an account of neuroplasticity with respect to cell-internal processing pathways and their relation to membrane and synaptic plasticity. We think traditional synapse-centric, weight-based models of memorization are not sufficient or adequate to capture the complexity of neuroplasticity. In standard accounts, we model a network of neurons connected by adaptive transmission links. The adaptation of these transmission links is overly simplified using short-term and long-term potentiation/depression, assuming weight changes according to use of the transmission link. In contrast, we propose a paradigm switch from a synapse-centric model (each synapse learns independently, based on its history of use) to a neuron-centric model (each neuron uses signal selection for intracellular pathways to express plasticity at the membrane). Each neuron has a ‘vertical’ dimension where internal parameters steer the external membrane- and synapse-expressed parameters. A neural model consists of (a) expression of parameters at the membrane, in particular dendritic synapses or spines, and axonal boutons (b) internal parameters in the sub-membrane zone and the cytoplasm with its protein signaling network and (c) core parameters in the nucleus for genetic and epigenetic information. In a neuron-centric model, each node (=neuron) in the horizontal network has its own internal memory. Neural transmission and information storage are separated, not automatically combined by coupling strength. There is filtering and selection of signals for storage. Not every transmission event leaves a trace. This represents an important conceptual advance over synaptic weight models. We present the neuron as a self-programming device, rather than as passively determined by ongoing input. We believe a new approach to neural modeling is necessary, because the experimental evidence is not well captured by traditional synapse-centric models. Ultimately, we are interested in the possibilities of a flexible memory system that processes external signals according to its inherent structure.

Read the full article at: f1000research.com

Identifying Energy Communities of Practice on Twitter: A Multiplex Network Analysis Using Graph Traversal Techniques

Vincenzo De Leo, Michelangelo Puliga, Martina Erba, Cesare Scalia, Andrea Filetti and Alessandro Chessa
Complexities 2026, 2(2), 15

In this work, we inspected the friendship network on Twitter (recently rebranded as X), concentrating on individuals and organizations intertwined with the energy field. We particularly focus on seasoned professionals, corporate entities, and domain specialists, all connected through ‘following’ relationships. By meticulously examining these ties, we uncover several distinct groupings within the network, each defined by the unique roles its members occupy. Our analysis demonstrates that the natural emergence of such clusters on social platforms exerts a profound influence on public discourse regarding energy and other critical matters, including climate change. Furthermore, we observe that the resulting communities exhibit distinct structural properties and communication patterns, with some clusters showing lower internal engagement, which may be indicative of fragmentation dynamics in online conversations. These emergent clusters, characterized by their shared communication styles, form relatively compact communities where the exchange of information is infrequent compared to larger networks and is usually confined to accounts created for specific commercial objectives. We emphasize that our analysis focuses on a structurally coherent connected component emerging from a curated set of energy-related seed accounts, rather than attempting to reconstruct the entirety of the energy discourse on Twitter. Consequently, peripheral or weakly connected communities may be underrepresented. Additionally, by combining machine-learning-based node classification with graph-based centrality measures, we are able to characterize the roles of structurally central actors within these niche segments and analyze the connectivity patterns that define their positions. This method provides novel insights into how corporate communication unfolds on social media, offering a refreshed perspective on professional networking. Ultimately, our findings highlight the ways in which companies within the energy sector take advantage of Twitter to coordinate their initiatives, with key institutions serving as central nodes in maintaining the organization of these networks.

Read the full article at: www.mdpi.com

The Brain We Still Don’t Understand | Gabriele Scheler

On this episode of BeyondPhrenology, I speak with Dr. Gabriele Scheler (Carl Correns Foundation for Mathematical Biology) about the state of AI and neuroscience—past the hype, and closer to their limits.

We begin by revisiting what AI once meant: symbolic systems, logic, early neural networks, and the long-standing divide between learning and reasoning. Against that backdrop, we examine the current moment, where large language models dominate the conversation but remain, in many ways, underwhelming relative to the broader ambitions of artificial intelligence.

The discussion then turns to neuroscience, where despite decades of experimental progress, a central problem remains unresolved: the absence of integrated, functional models of cognition. We explore the consequences of a synapse-centric view, the limits of current theoretical approaches, and why accumulating more data—without perspective—fails to move the field forward. Along the way, we touch on issues that rarely make it into official narratives: the role of funding structures, the drift toward mediocrity, and the persistence of poorly framed questions.

From there, we consider an alternative direction. Dr. Scheler outlines a neuron-centric, function-driven approach to modeling the brain—one that emphasizes modularity, one-shot learning, internal inference, and decision-making as a unifying principle across cognition and emotion. Framed through evolution, this perspective highlights how biological systems bridge scales of structure and function in ways current models largely fail to capture.

The episode closes by reflecting on what this means for the future: not just for AI, but for science itself—how research cultures shift, why fascination alone is not enough, and what it would take to build models that are not just complex, but actually explanatory.
Watch at: www.youtube.com