Author: cxdig

Honeybees adapt to a range of comb cell sizes by merging, tilting, and layering their construction

Honeybees are renowned for their skills in building intricate and adaptive combs that display notable variation in cell size. However, the extent of their adaptability in constructing honeycombs with varied cell sizes has not been thoroughly investigated. We use 3D-printing and X-ray microscopy to quantify honeybees’ capacity in adjusting the comb to different initial conditions. Our findings suggest three distinct comb construction modes in response to foundations with varying sizes of 3D-printed cells. For smaller foundations, bees occasionally merge adjacent cells to compensate for the reduced space. However, for larger cell sizes, the hive uses adaptive strategies such as tilting for foundations with cells up to twice the reference size and layering for cells that are three times larger than the reference cell. Our findings shed light on honeybees adaptive comb construction abilities, significant for the biology of self-organized collective behavior, as well as for bio-inspired engineered systems.

Gharooni-Fard G, Kavaraganahalli Prasanna C, Peleg O, López Jiménez F (2025) Honeybees adapt to a range of comb cell sizes by merging, tilting, and layering their construction. PLoS Biol 23(8): e3003253.

Read the full article at: journals.plos.org

Integrated information and predictive processing theories of consciousness: An adversarial collaborative review

Andrew W. Corcoran, Andrew M. Haun, Reinder Dorman, Giulio Tononi, Karl J. Friston, Cyriel M. A. Pennartz, TWCF: INTREPID Consortium

As neuroscientific theories of consciousness continue to proliferate, the need to assess their similarities and differences — as well as their predictive and explanatory power — becomes ever more pressing. Recently, a number of structured adversarial collaborations have been devised to test the competing predictions of several candidate theories of consciousness. In this review, we compare and contrast three theories being investigated in one such adversarial collaboration: Integrated Information Theory, Neurorepresentationalism, and Active Inference. We begin by presenting the core claims of each theory, before comparing them in terms of (1) the phenomena they seek to explain, (2) the sorts of explanations they avail, and (3) the methodological strategies they endorse. We then consider some of the inherent challenges of theory testing, and how adversarial collaboration addresses some of these difficulties. More specifically, we outline the key hypotheses that will be tested in this adversarial collaboration, and exemplify how contrasting empirical predictions may pertain to core and auxiliary components of each theory. Finally, we discuss how the data harvested across disparate experiments (and their replicates) may be formally integrated to provide a quantitative measure of the evidential support accrued under each theory. We suggest this approach to theory comparison may afford a useful metric for tracking the amount of scientific progress being made in consciousness research.

Read the full article at: arxiv.org

4th Meeting of the Spanish Society of Complex Systems January 21-23, 2026, Doñana Biological Station and University of Seville.

The Spanish Society of Complex Systems (CS³ Spain) was founded in 2022, during the International Conference on Complex Systems held in Palma de Mallorca, with the aim of strengthening the community of complex systems researchers in our country.

Since then, the Spanish Chapter has held three meetings: in Santander (2023), in Barcelona (2024) and in Madrid (2025), consolidating itself as a reference space for the exchange of ideas and interdisciplinary collaboration.

The 4th Meeting of the Spanish Society of Complex Systems will take place in Seville, from January 21 to 23, 2026, at the emblematic Uruguay Pavilion, headquarters of the University of Seville.

More at: cs3.es

Engineering Swarms of Cyber-Physical Systems By Melanie Schranz, Wilfried Elmenreich, Farshad Arvin

Engineering Swarms for Cyber-Physical Systems covers the whole design cycle for applying swarm intelligence in Cyber-Physical Systems (CPS) and guides readers through modeling, design, simulation, and final deployment of swarm systems. The book provides a one-stop-shop covering all relevant aspects for engineering swarm systems. Following a concise introduction part on swarm intelligence and the potential of swarm systems, the book explains modeling methods for swarm systems embodied in the interplay of physical swarm agents. Examples from several domains including robotics, manufacturing, and search and rescue applications are given. In addition, swarm robotics is further covered by an analysis of available platforms, computation models and applications. It also treats design methods for cyber-physical swarm applications including swarm modeling approaches for CPSs and classical implementations of behaviors as well as approaches based on machine-learning. A chapter on simulation covers simulation requirements and addresses the dichotomy between abstract and detailed physical simulation models. A special feature of the chapters is the hands-on character by providing programming examples with the different engineering aspects whenever possible, thus allowing for fast translation of concepts to actual implementation. Overall, the book is meant to give a creative researcher or engineer the inspiration, theoretical background, and practical knowledge to build swarm systems of CPSs. It also serves as a text for students in science and engineering.

Read the full article at: www.routledge.com