We present the first application of the stochastic advection by Lie transport (SALT) framework to an idealized coupled ocean-atmosphere system. SALT derives stochastic fluid equations from Hamilton's variational principle under a stochastic Lagrangian kinematic assumption, thereby preserving the geometric structure -- Kelvin circulation theorem, Lie-derivative advection operators, and local conservation laws -- of the underlying deterministic equations. The atmospheric component is rendered stochastic while the ocean remains deterministic, following Hasselmann's paradigm of a fast stochastic atmosphere driving a slow climate. The spatial correlation vectors encoding unresolved subgrid transport are estimated from high-resolution simulations via EOF analysis of Lagrangian trajectory differences. A central contribution is the replacement of the standard white-noise temporal model with Ornstein-Uhlenbeck (OU) processes, motivated by strong autocorrelation (decorrelation times of 50-150 time steps) in the dominant EOF modes; the OU process is the unique stationary Gaussian Markov process capable of capturing this temporal memory with a single parameter. Ensemble forecasts exhibit good spread-error agreement over 10-15 time units. Evaluated via the Continuous Ranked Probability Score -- a strictly proper scoring rule measuring discrepancy between the forecast measure and the true conditional distribution -- the stochastic ensemble consistently outperforms a size-matched deterministic ensemble, despite carrying higher RMSE. Well-posedness of the coupled stochastic system is identified as an important open problem.

We investigate the role of artificial intelligence in cybersecurity by evaluating how machine learning techniques can detect malicious network activity and identify potential information leakage in cryptographic implementations. We conduct a series of experiments using the NSL-KDD and CIC-IDS datasets to evaluate intrusion detection performance across controlled and shifted data environments. Our results demonstrate that AI models can achieve near-perfect detection accuracy within stable network environment. However, their performance declines when exposed to fluctuating or previously unseen traffic patterns. We also observed that learned models identify patterns consistent with side-channel leakage, suggesting that AI can assist in uncovering implementation-level vulnerabilities.

Stochastic resetting has attracted significant attention in recent years due to its wide-ranging applications across physics, biology, and search processes. In most existing studies, however, resetting events are governed by an external timer and remain decoupled from the system's intrinsic dynamics. In a recent Letter by Biswas et al, we introduced threshold resetting (TR) as an alternative, event-driven optimization strategy for target search problems. Under TR, the entire process is reset whenever any searcher reaches a prescribed threshold, thereby coupling the resetting mechanism directly to the internal dynamics. In this work, we study TR-enabled search by $N$ non-interacting diffusive searchers in a one-dimensional box $[0,L]$, with the target at the origin and the threshold at $L$. By optimally tuning the scaled threshold distance $u = x_0/L$, the mean first-passage time can be significantly reduced for $N \geq 2$. We identify a critical population size $N_c(u)$ below which TR outperforms reset-free dynamics. Furthermore, for fixed $u$, the mean first-passage time depends non-monotonically on $N$, attaining a minimum at $N_{\mathrm{opt}}(u)$. We also quantify the achievable speed-up and analyze the operational cost of TR, revealing a nontrivial optimization landscape. These findings highlight threshold resetting as an efficient and realistic optimization mechanism for complex stochastic search processes.

We propose IrisFP, a novel adversarial-example-based model fingerprinting framework that enhances both uniqueness and robustness by leveraging multi-boundary characteristics, multi-sample behaviors, and fingerprint discriminative power assessment to generate composite-sample fingerprints. Three key innovations make IrisFP outstanding: 1) It positions fingerprints near the intersection of all decision boundaries - unlike prior methods that target a single boundary - thus increasing the prediction margin without placing fingerprints deep inside target class regions, enhancing both robustness and uniqueness; 2) It constructs composite-sample fingerprints, each comprising multiple samples close to the multi-boundary intersection, to exploit collective behavior patterns and further boost uniqueness; and 3) It assesses the discriminative power of generated fingerprints using statistical separability metrics developed based on two reference model sets, respectively, for pirated and independently-trained models, retains the fingerprints with high discriminative power, and assigns fingerprint-specific thresholds to such retained fingerprints. Extensive experiments show that IrisFP consistently outperforms state-of-the-art methods, achieving reliable ownership verification by enhancing both robustness and uniqueness.

The angle between stellar spin axes and planetary orbits -- stellar obliquity -- probes the dynamics of planetary migration and evolution. The obliquities of giant planets have been extensively studied because they are the most easily measured. Smaller planets, while more difficult to measure, have the advantage of better reflecting the dynamics of planetary systems because they trigger negligible back-reactions onto the host star. This paper introduces a new observational campaign called the Small, Low-mass Oblique Planets Experiment (SLOPE) survey with the Keck Planet Finder (KPF) spectrograph, and presents four new obliquity measurements. The SLOPE survey focuses on planets smaller than Saturn across a variety of system architectures. The sky-projected obliquities of the four planets measured -- TOI-1386b, TOI-480b, TOI-4596b, and TOI-1823b -- are all consistent with spin-orbit alignment. We validate the planetary nature of TOI-4596b with a significant obliquity detection. Including these measurements, we conducted a statistical analysis of the obliquities of sub-Saturn size planets in different planetary system architectures. Compared to other architectures, those in compact multi-planet systems reside in orbits that appear preferentially aligned with the stellar equator with 6 sigma confidence.

We study a simplified model of monoclonal antibodies confined in a patchy random porous medium. Antibodies are represented as Y-shaped particles composed of seven tangential hard spheres with attractive patches on the terminal beads, while the matrix consists of randomly distributed hard-sphere obstacles bearing adhesive sites. The model captures antibody behavior in crowded biological environments with strong short-range antibody-matrix attractions. The theoretical approach combines Wertheim's multidensity thermodynamic perturbation theory, the Flory-Stockmayer theory of polymerization, and scaled particle theory for fluids in porous media. We analyze thermodynamic properties, percolation thresholds, and phase behavior, and compare the selected results with new computer simulations. The interplay between antibody-antibody and antibody-matrix interactions produces a complex phase behavior, including re-entrant phase separation with a closed-loop coexistence region at higher temperatures and conventional liquid-gas separation at lower temperatures.

Perchloric acid (HClO$_4$) is widely used to prepare perchlorate salts with applications in propellants, industry, environmental chemistry, and biology. In this work, we used the intermolecular parameters from the extended Madrid-2019 force field for the perchlorate anion (ClO$_4^-$) and the oxonium cation (H$_3$O$^+$) together with TIP4P/2005 water to model perchloric acid solutions. The force field uses scaled charges of $\pm0.85e$ for monovalent ions and has been widely applied for aqueous ionic systems. We used the model to predict thermodynamic properties [densities and temperatures of maximum in density (TMD)], structural features (ion-water correlations: ion-hydrogen and ion-oxygen), and transport properties (self-diffusion coefficients and viscosity) of perchloric acid solutions at several concentrations. Experimental densities are predicted in excellent agreement up to 10 $m$. We also performed molecular simulations over a wide range of temperatures in order to determine the TMD of perchloric acid at different molalities. Predicted viscosities at 298.15 K and 1 bar are in good agreement with experimental data for concentrations below 4 $m$. Results are discussed in terms of model strengths and limitations.

We consider the SEIRS epidemiology model with such features of the COVID-19 outbreak as: abundance of unidentified infected individuals, limited time of immunity and a possibility of vaccination. The control of the pandemic dynamics is possible by restricting the transmission rate, increasing identification and isolation rate of infected individuals, and via vaccination. For the compartmental version of this model, we found stable disease-free and endemic stationary states. The basic reproductive number is analysed with respect to balancing quarantine and vaccination measures. The positions and heights of the first peak of outbreak are obtained numerically and fitted to simple in usage algebraic forms. Lattice-based realization of this model is studied by means of the asynchronous cellular automaton algorithm. This permitted to study the effect of social distancing by varying the neighbourhood size of the model. The attempt is made to match the quarantine and vaccination effects.

Accurate descriptions of reference systems are a central task in liquid-state theories for the study of more complex systems. Using scaled particle theory (SPT), we derive a fully analytical description of the thermodynamic properties of a hard-sphere (HS) fluid confined in size-polydisperse HS random porous media, extending the existing approaches to higher matrix packing fractions. We calculate chemical potentials for a wide range of porous-matrix parameters, including the matrix packing fraction, degree of polydispersity, and particle-size distributions. Within the proposed framework, our results show excellent agreement with available Monte Carlo simulations and previous integral-equation theories over a broad range of matrix packing fractions, $0.1 \leqslant η_0 \leqslant 0.3$, and degrees of polydispersity.

This review examines multiscale modelling approaches for cellulose nanocrystals (CNCs) and lignocellulosic plant cell walls, with a focus on hemicellulose and lignin interactions in aqueous environments. The three-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) is highlighted as a powerful molecular solvation theory applied in nanochemistry and biomolecular simulations. The method has been successfully employed to investigate hemicellulose hydrogels, the influence of hemicellulose composition on nanoscale forces in primary cell walls, and lignin-lignin and lignin-hemicellulose interactions. Findings indicate that these interactions are predominantly hydrophobic and entropy-driven, arising from water exclusion effects. Insights gained through this modeling framework deepen the understanding of molecular-scale forces in plant cell walls and inform strategies for biomass valorization, including genetic engineering and pretreatment technologies aimed at enhancing cellulose extraction and utilization.

Protein conformational stability and function depend on non-covalent interactions that are strongly influenced by the surrounding environment. To explore protein properties, amino acids are often utilized as model systems. In this study, we determined the densities of seven $α$-amino acids in aqueous solutions between 278.15 K and 308.15 K and calculated the apparent molar volumes. Linear extrapolation yielded standard molar volumes, which were analyzed to characterize amino-acid hydration. The contributions of side chains to the standard molar volume were determined relative to glycine. The standard molar volume increased with temperature, indicating reduced electrostriction of water around the amino acids, consistent with lower hydration numbers at higher temperatures. We employed the Ornstein-Zernike integral equation with hypernetted-chain closure and a coarse-grained Lennard-Jones bead model to calculate pair correlation functions and Kirkwood-Buff integrals, from which standard molar volumes were obtained. The model reproduced the experimental standard molar volumes very well.

We explore the pressure dependence of a set of properties of simple monohydric alcohols, namely of methanol, ethanol and 1-propanol, by using isobaric-isothermal molecular dynamics computer simulations. A recently proposed united atom, non-polarizable force field for each of alcohols [V. García-Melgarejo et al., J. Mol. Liq., 323, 114576 (2021)] is applied. Accuracy of the force field is evaluated by comparing the simulation results and available experimental data from the literature. Specifically, the density of alcohols upon increasing pressure, the isothermal compressibility, the static dielectric constant and self-diffusion coefficient are investigated starting from 1 bar up to 3 kbar. Evolution of the microscopic structure under pressure is discussed in terms of the pair distribution functions and some coordination numbers. Conclusions of the present modelling and necessary developments to consider in future work are commented on.

We study the behavior of aqueous surfactant solutions in the bulk phase and in slit-like pores by molecular dynamics. Adsorption and self-assembly of nonionic surfactants C$_7$E$_3$ that mimic alkyl poly(ethylene oxide) molecules are investigated. We consider pores with the same walls and Janus-like slits. The individual walls are inert, hydrophilic, or hydrophobic. We focus on the morphology of the surfactant solution confined in different slits. The influence of a pore type and its width is discussed. The aggregative adsorption of surfactants was found. Our simulations show that in slits surfactants assemble into structures that do not occur in the bulk phases.

We explore the dependence of a wide set of properties of monohydric alcohols on temperature by using the isobaric-isothermal molecular dynamics computer simulations. Namely, methanol (MeOH), ethanol (EtOH) and 1-propanol (PrOH) alcohols are studied. The recently proposed united atom, non-polarizable force field for each of alcohols [V. García-Melgarejo et al., J. Mol. Liq., 2021, 323, 114576] is applied for this purpose. Accuracy of the force field is discussed comparing predictions from simulations and experimental data for density, dielectric constant, surface tension, and self-diffusion coefficient. Supplementary insights concerning applicability of the model are obtained by exploration of the composition dependence of various properties for MeOH-PrOH mixtures. Peculiarities of mixing of species in this system are elucidated in terms of density, excess mixing volume and excess mixing enthalpy. Static dielectric constant of the mixture and the corresponding excess are obtained. Perspectives of modelling are commented finally.

Symmetric mixtures characterized by high negative geometric and energetic non-additivity do not exhibit phase separation in the bulk. However, the phase separation occurs when such mixtures are confined in slit pores with selective walls. It is demonstrated that the wall selectivity affects the pore filling. When the difference of the interaction energies between the mixture components and pore walls is lower than a certain threshold value, condensation occurs between a dilute phase and the mixed liquid. When this difference exceeds the threshold value, the pore filling may occur in two steps. The first is the condensation of a dilute phase into the demixed liquid, and the second step leads to the formation of the mixed liquid. We have elucidated the changes in the phase behavior caused by non-additivity of symmetric mixtures, and by the difference in the interaction energies of the components with pore walls.

Given any irrational number $α$, we show that for any $0<θ<6/17$, there are infinitely many $y$-smooth (friable) numbers $n$ such that $$\|nα\| < n^{-θ},$$ where $(\log n)^C\leq y\leq n$ for some large constant $C>0$. This improves the previous work of Baker, who obtained the exponent $1/3-2/(3C)+o(1)$ in the case of $y\geq (\log n)^C$, and that of Yau, who obtained the exponent $1/3$ when $y=n^{o(1)}$. Our proof is based on the dispersion method together with arithmetic inputs coming from the average bounds for Kloosterman sums over smooth numbers.

The location of electrons governs phenomena ranging from chemical bonding and electric polarization to the topological classification of band insulators and the emergence of correlated states in quantum matter. While a prescription exists for finding local state representations of electrons in one-dimensional insulators, no comparably general theory exists in higher dimensions. Here, we introduce a general framework for finding the location of electrons in insulators in two and three dimensions based on the spectral properties of quantum-mechanical operators that we term Spatial Localizers. This framework naturally extends the notion of Wannier centers to insulators with boundaries, defects, and disorder, which we use to establish a position-space formulation of the bulk-defect correspondence for electronic charge. This framework also yields maximally localized electronic states. As two representative examples, we show that these states reduce to maximally localized Wannier functions in atomic insulators, whereas in Chern insulators they form coherent states that mirror the coherent-state structure of Landau levels in the quantum Hall effect.

To better understand the potential habitability of planets orbiting brown dwarfs, this work presents a new set of equilibrium temperature evolution tracks. Unlike most previous work that relied on analytic scaling relationships for brown dwarf luminosity evolution, we use the outputs of modern brown dwarf evolution models that account for the effects of deuterium burning, cloud formation and dissipation, and the most recent atmospheric opacities. While clouds are present, brown dwarfs cool more slowly than if they did not have clouds, allowing orbiting planets to remain in the habitable zone (HZ) for millions of years longer than previously estimated. Similarly, we find that during the deuterium-burning phase of brown dwarfs, which also slows the evolution, planets at the same orbital radius but orbiting brown dwarfs of different masses can remain in the HZ for the same duration, creating deuterium ``sweet spots'' for habitability around brown dwarfs near the deuterium-burning limit. For example, at 0.01 au a planet orbiting both a 0.012 and a 0.020 solar mass brown dwarf stays in the HZ for ~170 - 180 Myr because deuterium-burning more strongly affects the cooling of lower-mass brown dwarfs. The size of the effect decreases with decreasing orbital radius, with larger orbital radii having a more pronounced deuterium burning influence. These effects are absent from the analytic cooling approximations used in prior studies of substellar HZs and are revealed by our application of modern substellar evolution models.

Self-ordering kiosks (SOKs) are widely deployed in fast food restaurants, transforming food ordering into digitally mediated, self-navigated interactions. While these systems enhance efficiency and average order value, they also create opportunities for manipulative interface design practices known as dark patterns. This paper presents a structured audit of the McDonald's self-ordering kiosk in Germany using the Temporal Analysis of Dark Patterns (TADP) framework. Through a scenario-based walkthrough simulating a time-pressured user, we reconstructed and analyzed 12 interface steps across intra-page, inter-page, and system levels. We identify recurring high-level strategies implemented through meso-level patterns such as adding steps, false hierarchy, bad defaults, hiding information, and pressured selling, and low-level patterns including visual prominence, confirmshaming, scarcity framing, feedforward ambiguity, emotional sensory manipulation, and partitioned pricing. Our findings demonstrate how these patterns accumulate across the interaction flow and may be amplified by the kiosk's linear task structure and physical context. These findings suggest that hybrid physical--digital consumer interfaces warrant closer scrutiny within emerging regulatory discussions on dark patterns.

An exponent of distribution 1/16 is established for square-free palindromes. The main input is an upper bound for the number of palindromes, in arithmetic progressions to large moduli, divisible by large squares. Our argument combines a simplifying reformulation with exponential-sum estimates, recent work on 6-almost-prime palindromes, and the large sieve with square moduli of Baier-Zhao.

Doubly Special Relativity (DSR) deforms special-relativistic kinematics by introducing an invariant Planck energy scale $E_{\mathrm{Pl}}$ alongside the speed of light, while preserving the relativity principle. A key issue in curved spacetimes, particularly black-hole thermodynamics, is the operational meaning of the ``energy'' in modified dispersion relations (MDRs). We compare two common implementations in a controlled static black-hole spacetime: (i) MDRs in local orthonormal frames on a fixed background geometry, and (ii) the rainbow-metric approach with an energy-dependent family of effective metrics. For static, spherically symmetric horizons and using a consistent finite operational energy scale $E_\star$ for emitted quanta, both yield the same near-horizon temperature rescaling \[ T(E_\star)=T_0\,\frac{g(E_\star/E_{\mathrm{Pl}})}{f(E_\star/E_{\mathrm{Pl}})}, \quad T_0=κ_0/(2π), \] where $f$ and $g$ are the standard rainbow/MDR functions. This establishes a universality of the tunneling/surface-gravity temperature, with deformation entering solely via the ratio $g/f$. We illustrate for Amelino-Camelia MDR and Magueijo-Smolin DSR (where $f=g$, implying $T(E_\star)=T_0$). Extending to a two-parameter generalized DSR (G-DSR) with leading parameters $(α_2, Δα)$, we obtain \[ T_{\mathrm{GDRS}}(E_\star) = T_0 \sqrt{\frac{1-2Δα\,(E_\star/E_{\mathrm{Pl}})}{1-2α_2\,(E_\star/E_{\mathrm{Pl}})}} \simeq T_0 [1 - (Δα- α_2) E_\star/E_{\mathrm{Pl}}]. \] We discuss the role of $Δα- α_2$ (vanishing correction for the symmetric $Δα=α_2$ subfamily) and note that further model dependence arises from phase-space measures, greybody factors, and non-linear composition laws. Corrections are strongly suppressed for macroscopic black holes and become relevant only near the Planck regime.

As artificial intelligence (AI) becomes increasingly integrated into daily life, higher education must move beyond code-centric instruction to foster holistic AI literacy. We present a novel pedagogical approach that integrates embodied, unplugged activities into a university-level Introduction to AI course. Inspired by the effectiveness of CS Unplugged in K-12 education, our physical, collaborative activities gave students a first-person perspective on AI decision-making. Through interactive games modeling Search Algorithms, Markov Decision Processes, Q-learning, and Hidden Markov Models, students built an intuition for complex AI concepts and more easily transitioned to mathematical formalizations and code implementations. We present four unplugged AI activities, describe how to bridge from unplugged activities to plugged coding tasks, reflect on implementation challenges, and propose refinements. We suggest that unplugged activities can effectively bridge conceptual reasoning and technical skill-building in university-level AI education.

Effect of internal chirality on collective motion of a large number of active objects is studied by simulations of appropriately modified Vicsek model. We add a fixed angle to the noise and consider small ratios, $p$, between this angle and the maximal deviation from the average local direction of motion. When the above ratio is $p=1/120$, the traveling bands observed with the symmetrical noise are destroyed, and small bands moving in different directions appear. Circular rotating flocks of objects with the same orientation are formed for $p=1/7.5$. Stable vortexes in the stationary state were found from $p=1/60$ to $p=1/20$. Velocity autocorrelation function shows equilibrium between the inflow and the outflow to and from the vortex. Long-time evolution is considerably influenced by a temporary trapping of the objects in the vortex. The ballistic behavior for the symmetrical noise changes to the diffusive behavior for the chirality leading to the onset of vortexes.

An important distinction in our understanding of capacities of classical versus quantum channels is marked by the following question: is there an algorithm which can compute (or even efficiently compute) the capacity? While there is overwhelming evidence suggesting that quantum channel capacities may be uncomputable, a formal proof of any such statement is elusive. We initiate the study of the hardness of computing quantum channel capacities. We show that, for a general quantum channel, it is QMA-hard to compute its quantum capacity, and that the entanglement-assisted zero-error capacity under some restrictions is uncomputable; indicative of the fact that quantum channel capacities may generally be undecidable.

Solid-state electrolytes (SSEs) are attractive for next-generation lithium-ion batteries due to improved safety and stability but their low room-temperature ionic conductivity hinders practical application. Experimental synthesis and testing of new SSEs remain time-consuming and resource intensive. Machine learning (ML) offers an accelerated route for SSE discovery; however, composition-only models neglect structural factors important for ion transport while graph neural networks (GNNs) are challenged by the scarcity of structure-labeled conductivity data and the prevalence of crystallographic disorder in CIFs. Here, we train two complementary predictors on the same room-temperature, structure-labeled dataset (n = 499). A gradient-boosted tree regressor (GBR) combining stoichiometric and geometric descriptors achieves best performance (MAE = 0.543 in log(S cm-1)), and Shapley Additive exPlanations (SHAP) identifies probe-occupiable volume (POAV) and lattice parameters as key correlations for conductivity. In parallel, we fine-tune large language models (LLMs) using compact text prompts derived from CIF metadata (formula with optional symmetry and disorder tags), avoiding direct use of raw atomic coordinates. Notably, Llama-3.1-8B-Instruct achieves high accuracy (MAE = 0.657 in log(S cm-1)) using formula and symmetry information, eliminating the need for numerical feature extraction from CIF files. Together, these results show that global geometric descriptors improve tree-based predictions and enable interpretable structure-property analysis, while LLMs provide a competitive low-preprocessing alternative for rapid SSE screening.

In today's complex industrial environments, operators must often navigate through extensive technical manuals to identify troubleshooting procedures that may help react to some observed failure symptoms. These manuals, written in natural language, describe many steps in detail. Unfortunately, the number, magnitude, and articulation of these descriptions can significantly slow down and complicate the retrieval of the correct procedure during critical incidents. Interestingly, Retrieval Augmented Generation (RAG) enables the development of tools based on conversational interfaces that can assist operators in their retrieval tasks, improving their capability to respond to incidents. This paper presents the results of a set of experiments that derive from the analysis of the troubleshooting procedures available in Fincantieri, a large international company developing complex naval cyber-physical systems. Results show that RAG can assist operators in reacting promptly to failure symptoms, although specific measures have to be taken into consideration to cross-validate recommendations before actuating them.

In this paper, we introduce the SPINNs (stochastic physics-informed neural networks) in a systematic manner. This provides a mathematical framework for approximating the solution of stochastic differential equations (SDEs) driven by Levy noise using artificial neural networks.

A striking prediction of QCD on the properties of the novel Transverse Momentum Dependent (TMD) distribution functions is that the time-reversal odd Sivers and Boer-Mulders functions extracted from semi-inclusive deep-inelastic scattering (SIDIS) will undergo a sign reversal in the Drell-Yan (DY) process. This prediction has been tested by experiments that have focused on the Sivers functions so far. We examine the current status on the theoretical prediction and experimental extraction of the signs of the Boer-Mulders functions from SIDIS and DY. We show that the existing SIDIS and DY data are consistent with the predicted sign reversal of the Boer-Mulders functions for proton's valence quark distribution. Prospects for future experiments at EIC capable of testing the sign reversal of the pion Boer-Mulders functions are also discussed.

We propose a next-generation precision measurement of the muon anomalous magnetic moment (muon $g-2$), at the High Intensity Heavy-Ion Accelerator Facility (HIAF) in China. The project, named CANTON-$μ$ (Coherent Anomalous magNetic momenT ObservatioN with muon), represents the first muon $g-2$ experiment aimed at surpassing Fermilab precision. It introduces novel approaches based on HIAF's intense pulsed GeV-scale muon beams, particularly for negative-muon polarity. This work studies the expected muon beam intensity at HIAF, establishing the statistical reach and level of systematic control required to achieve a precision of 0.13 ppm in Phase 1, matching the current Fermilab precision, and 0.05 ppm in Phase 2 with the HIAF upgrade. This precision enables stringent tests of the Standard Model with sensitivity to new physics beyond current collider scales, and offers a uniquely sensitive test of CPT symmetry within the Standard-Model Extension at the $10^{-24}$ GeV level, improving existing limits by more than an order of magnitude.

Community currencies (CCs) have been adopting innovative systems to overcome implementational hurdles from issuing paper currencies. Using a qualitative approach, this paper examined this digital transition of Sarafu Network in Kenya and its predecessor CCs as a case study. From the original vouchers launched in 2010, the foundation Grassroots Economics introduced a digital interface in 2016 that operates on a feature phone, and then integrated blockchain technology starting in 2018, undergoing several migrations before becoming settling on its current iteration called Community Asset Vouchers on the Celo blockchain since 2023. Using affordances from human-computer interaction, the research shows that digitalization and blockchain improved the facilitation of economic activities of the local communities, both their typical market transactions as well as traditional reciprocal labor exchanges, by offering more functionalities compared to the analog version of Sarafu. The unique contributions of blockchain include enabling automation of holding tax calculations and linking the vouchers to the mainstream monetary system via stablecoins facilitated by a series of smart contracts also known as the liquidity pool. The study also finds that there is an inherent trade-off between blockchain benefits and user interface complexity. Hence, balancing innovation and community needs remains a challenge.