Latest Articles
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Section: Archaeology ; Topics: Archaeology, Anthropology, Computer sciences ; Conference: CAA2025
Experimental interpretation of ancient games through playtesting
10.24072/pcjournal.738 - Peer Community Journal, Volume 6 (2026), article no. e56
Get full text PDFReconstructing the rules of ancient games remains one of archaeology’s most intricate interpretive challenges. While surviving boards and pieces provide tangible evidence, the logic of play, rules, roles, and goals have largely vanished. Traditional reconstructions rely on text and comparisons to better known games, yet these methods seldom capture the experiential and social dimensions of ancient gameplay. This paper presents an experimental framework designed to explore how such systems might emerge through play itself. Using Ludus Latrunculorum (“The Game of Little Soldiers”) as a case study, participants engaged in iterative, co-creative sessions where they invented, refined, and transmitted rules across successive lineages of play. The resulting games revealed recurring mechanics that closely parallel known reconstructions of Ludus Latrunculorum. While the outcomes do not claim historical accuracy, they demonstrate how collective experimentation can illuminate the cognitive and social processes underlying the development of structured games in antiquity. The study thus proposes experimental play as a complementary archaeological tool for examining how rulesets evolve, stabilize, and reflect the societies that produce them.
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Section: Mathematical & Computational Biology ; Topics: Biophysics and computational biology, Computer sciences
Accelerating k-mer-based sequence filtering
10.24072/pcjournal.735 - Peer Community Journal, Volume 6 (2026), article no. e55
Get full text PDFMotivation. The exponential growth of global sequencing data repositories presents both analytical challenges and opportunities. While k-mer-based indexing has improved scalability over traditional alignment for identifying relevant documents, pinpointing the exact sequences matching numerous queries remains a hurdle. In particular, searching for numerous k-mers with a single large query or multiple distinct queries strains existing exact matching tools, whose performance scales poorly with an increasing number of patterns. At the same time, indexing entire vast datasets for infrequent or ad-hoc searches is often resource-prohibitive. Designing fast methods for matching a large number of k-mers without exhaustive pre-indexing is therefore critical. Contributions. We propose an efficient solution to the problem of k-mer-based sequence filtering: given a set of k-mers of interests and a threshold, quickly evaluate whether an arbitrary sequence has a number of k-mer matches above or below the threshold. Our approach demonstrates how minimizer-based based sketching, alongside SIMD acceleration, can enhance the performance of streaming searches, and is implemented as a Rust tool named K2Rmini. On a consumer laptop, K2Rmini is able to filter long reads at 2 Gbp/s. Availability. https://github.com/Malfoy/K2Rmini.
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Section: Ecology ; Topics: Ecology, Statistics
Uncovering the relative movements of ecological trajectories
10.24072/pcjournal.736 - Peer Community Journal, Volume 6 (2026), article no. e54
Get full text PDFMovement analogies are often employed by ecologists to describe how ecological dynamics relate to one another. For instance, two communities whose similarity increases in time may be said to converge. Here we argue that the movement analogies used by ecologists to compare ecological dynamics could be enriched with other notions such as “pursuit” or “parallel” movements, if accompanied by appropriate statistical testing. By building on the framework of Ecological Trajectory Analysis, we present here Relative Trajectory Movement Assessment (RTMA), a framework to detect and qualify relative movements in ecological dynamics defined as trajectories in multivariate space. Using synthetic trajectory data, we illustrate how RTMA can reveal a diverse range of relative movements beyond the convergence and divergence patterns already recognized in ecology. We exemplify the use of RTMA on real ecological datasets describing 1) old field successional dynamics in eastern North America and 2) temporal patterns in tree size structure of a New Zealand forest. RTMA provides ecologists with a new way of describing and comparing ecological dynamics that could be widely applied, from plot-scale dynamics to the effects of global change.
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Section: Ecology ; Topics: Ecology, Population biology, Statistics
Disentangling different sources of variation in functional responses: between-individual variability, measurement error and inherent stochasticity of the prey-predator interaction process
10.24072/pcjournal.729 - Peer Community Journal, Volume 6 (2026), article no. e53
Get full text PDFThe consumption rate of prey by predators, or functional responses, are known to be highly variable even within a single population. Identifying and estimating the different sources of variation of functional responses is a long-standing challenge. We develop here a statistical framework derived from a mechanistic stochastic process model that explicitly accounts for different sources of variation. We apply it to disentangle and estimate in particular 1) residual variance due to measurement errors and model misspecification, 2) between-predator variability, and 3) the interaction stochasticity, i.e. the intrinsic and mechanistic variability due to interactions processes between prey and predators. We show that it is possible to estimate these sources of variation under realistic experimental conditions. Our results also show that model fitting can compensate by overestimating residual source of variation, leading to biased parameter estimates when interaction stochasticity is misspecified. Applied to empirical data, the model reveals that standard assumptions, such as prey renewal and lack of spatial structure, fail to capture observed variability. We also show how experimental design affects parameter identifiability, highlighting the trade-off between the number of individuals and repeated observations.
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The network image was drawn by Martin Grandjean: A force-based network visualization CC BY-SA