The Ruđer Bošković Institute (RBI; Croatian: Institut Ruđer Bošković, pronounced [instǐtuːt rûd͡ʑɛr bɔ̂ʃkɔʋit͡ɕ], IRB) is a research institute located in the Šalata neighborhood of Zagreb, Croatia, founded in 1950, which studies the sciences.
Ecological models routinely rely on broad empirical regularities to constrain theory and validate predictions, yet these regularities are typically applied implicitly under alternative labels such as "patterns", "laws", or "benchmarks." Such implicit applications obscure a model's empirical commitments, hide unverified assumptions, mask scale dependencies, and hinder transfer across systems. Explicitly naming these regularities as "stylized facts" — a formalization with roots in economics and a robust application in ecology through Dynamic Energy Budget (DEB) theory — changes their epistemic status: foundational constraints become searchable, comparable across studies, and directly auditable against empirical support. This review evaluates the epistemological role and methodological utility of explicit stylized facts across ecological modeling. We first survey their widespread but implicit use across subfields. We then use DEB as a focal example to examine how explicit stylized facts drive theoretical development through three modes — expansion, adjustment, and structural change — and how contradictory evidence, rather than invalidating a theory, defines its precise domain of applicability and motivates accountable theoretical pluralism. Auditing empirical support at the scale of the published literature is beyond what any author team can do by hand; what we report instead is a structured authors' confidence assessment covering both DEB's core stylized facts and a set of additional statements formulated by the author panel. Recognizing the bias inherent in any small panel and the sheer volume of the modern literature, we call for an AI-assisted, community-curated system for cataloguing and auditing stylized facts. Several long-standing assumptions already command markedly less confidence than their foundational status implies — and making that visible, and open to revision, is what turns a set of modeling conventions into an auditable empirical foundation.
Up to date, the development of highly efficient, visible light-active catalysts remains a formidable challenge due to the enhanced rising of atmospheric CO2 concentration. This study discusses a class of ceria-based high-entropy oxides designed to optimize charge carrier dynamics, surface reactivity, and CO2 activation efficiency. Due to the advantages of high configurational entropy and multi-element synergy, these materials achieved improved photocatalytic performance, surpassing conventional ceria-based systems. Structural and spectroscopic analyses reveal that Pr3+/Pr4+ redox pairs and abundant oxygen vacancies create an electronically disordered yet thermodynamically stable environment, which enhances charge separation and suppresses electron-hole recombination. Photocatalytic experiments demonstrated that Ce0.2Zr0.2La0.2Pr0.2Sm0.2O2-delta (CZLPS) achieves the highest CO2 conversion rate, reaching a conversion of 20.3% under visible light irradiation, significantly surpassing pure ceria (1.4%), with a calculated space-time yield (STY) of 10.15 mol(CO)kg(-1)h(-1) under the same conditions. First-principles density functional theory (DFT) simulations were employed to investigate the CO2 reduction mechanism on CZLPS catalysts. The study elucidates the Gibbs free energy changes (Delta G) for each step of the reaction pathways leading to CO and HCOOH formation, highlighting the Zr site of CZLPS as the most active for the CO2RR, which is responsible for the outstanding catalytic activity
Generative models for crystalline materials often rely on equivariant graph neural networks, which capture geometric structure well but are costly to train and slow to sample. We present Crystalite, a lightweight diffusion Transformer for crystal modeling built around two simple inductive biases. The first is Subatomic Tokenization, a compact chemically structured atom representation that replaces high-dimensional one-hot encodings and is better suited to continuous diffusion. The second is the Geometry Enhancement Module (GEM), which injects periodic minimum-image pair geometry directly into attention through additive geometric biases. Together, these components preserve the simplicity and efficiency of a standard Transformer while making it better matched to the structure of crystalline materials. Crystalite achieves state-of-the-art results on crystal structure prediction benchmarks, and de novo generation performance, attaining the best S.U.N. discovery score among the evaluated baselines while sampling substantially faster than geometry-heavy alternatives.
Detecting parity violation on cosmological scales would provide a striking clue to new physics. Large-scale structure offers the raw statistical power many three-dimensional modes to make such tests. However, for scalar observables, like galaxy clustering, the leading parity-sensitive observable is the trispectrum, whose high dimensionality makes the measurement and noise estimation challenging. We present two late-time parity-odd kurto spectra that compress the parity-odd scalar trispectrum into one-dimensional, power-spectrum-like observables. They are built by correlating (i) two appropriately weighted quadratic composite fields, or (ii) a linear and cubic composite field, constructed from dark matter (DM) or galaxy overdensity fields. We develop an FFTLog pipeline for efficient theoretical predictions of the two observables. We then validate the estimators for a specific parity-odd primordial template on perturbative DM field, and on DM and halo fields in full N-body Quijote simulations, with and without parity-odd initial conditions, in real and redshift space. For DM, the variance is dominated by the parity-even contribution i.e., the gravitationally induced parity-even trispectrum and is efficiently suppressed by phase-matched fiducial subtraction. For halos, discreteness-driven stochasticity dominates and is not appreciably reduced by subtraction; however, optimal weighting and halo-matter cross kurto spectra considerably mitigate this noise and enhance the signal. Using controlled down-sampling of the matter field, we empirically calibrate how the parity-even variance scales with number density and volume, and provide an illustrative forecast for the detectability of parity-odd kurto spectra in a Euclid-like spectroscopic galaxy survey.
We present the GRASPion, a compact, open-source bristlebot designed for the controlled study of active matter systems. Built around an Arduino-compatible board and modular 3D-printed components, the GRASPion combines ease of use, programmability, and mechanical versatility. It features dual vibrating motors for self-propulsion, integrated sensors for local interaction, and customizable firmware enabling various motion modes, from ballistic to diffusive regimes. The robot is equipped with onboard IR communication, color and proximity sensors, and a magnetometer, allowing for real-time interaction and complex collective behaviors. With a runtime exceeding 90 min and reproducible fabrication, the GRASPion provides a robust and scalable platform for both educational and research applications in out-of-equilibrium physics. This article details the mechanical and electronic design and software architecture of the GRASPion and illustrates its capabilities through prototypical experiments relevant to active matter, such as diffusive motion, synchronization, and phototaxis.
