The nonequilibrium structural and dynamical properties of semiflexible active polar polymers subject to linear flow are studied by numerical simulations. Filaments are confined in two dimensions and immersed in a fluid described by the Brownian Multiparticle Collision Dynamics approach. The applied shear flow causes conformational changes of a polymer, aligns it along the flow direction, and induces a tumbling motion at large flow rates. In an intermediate, activity-dependent shear-rate regime, a characteristic scaling exponent for the mean-square end-to-end distance along the gradient direction is observed. This exponent appears to be determined by the semiflexibility of the polymer. The tumbling dynamics exhibits a characteristic time, with a stronger dependence on the Weissenberg number than that of flexible active or passive polymers. Activity strongly impacts the rheological properties of the semiflexible polymers, and even implies a negative viscosity for weak flows. At very large values of the shear rate, shear dominates over activity and passive-polymer behavior is assumed.

AI coding agents are increasingly integrated into real-world software development workflows, yet their robustness under diverse and adversarial scenarios remains poorly understood. We present ABTest, a behavior-driven fuzzing framework that systematically tests coding agents by turning real-world failure reports into repository-grounded behavioral tests. ABTest (1) mines user-reported anomalies to derive reusable workflow patterns (Interaction Patterns) and behaviors (Action types); (2) composes them into stepwise fuzzing templates; (3) instantiates executable test cases in real repositories; (4) executes them with coding agents while recording traces and artifacts; and (5) detects and validates anomalous behaviors. We apply ABTest to three widely used coding agents: Claude Code, OpenAI Codex CLI, and Gemini CLI. From 400 user-reported developer-confirmed agent failures, we extract 47 Interaction Patterns and 128 Action types, generating 647 repository-grounded fuzzing cases. Executing the 647-case bundle once per evaluated configuration, ABTest flags 1,573 behavioral anomalies across the three coding agent families, of which 642 are manually confirmed as new true anomalies, achieving a detection precision of 40.8%. Our results demonstrate that ABTest effectively uncovers real-world failures, exposes robustness differences across models, and reveals previously unreported failure modes.

Understanding how corporate control concentrates in modern ownership systems is crucial in an economy increasingly shaped by cross-border mergers and acquisitions. Rather than expanding productive capacity, these operations reorganize ownership and control over existing firms through complex transnational structures involving financial intermediaries, holding companies, and investment vehicles. As a result, corporate control may become highly concentrated even when formal ownership appears fragmented. This paper examines how foreign direct investments-related capital centralization reshapes firm-level governance by tracing how control converges on individual companies through multi-layered ownership networks. Focusing on two strategically relevant Italian firms, we show that control is rarely exercised solely by ultimate owners, but instead arises from the interaction of a small set of financially interconnected intermediaries operating along transnational ownership chains. The results show how small equity stakes translate into substantial governance power, highlighting the role of financial intermediation and raising implications for strategic autonomy and economic sovereignty in key sectors.

As quantum computing matures toward the realization of Cryptographically Relevant Quantum Computers (CRQC), global cryptographic infrastructure faces an existential threat. This paper introduces a two-dimensional coordinate system to map the co-evolution of cryptographic resilience (x-axis) and computational capability (y-axis). By analyzing the four resulting quadrants, we categorize the transition from legacy classical systems to quantum-resilient architectures. We argue that the "Quantum Gap" - the delta between CRQC arrival and quantum-safe adoption represents the highest systemic risk, necessitating an immediate transition to crypto-agile frameworks.

In particle physics, as in many areas of science, parameter inference relies on simulations to bridge the gap between theory and experiment. Recent developments in simulation-based inference have boosted the sensitivity of analyses; however, biases induced by simulation-data mismodeling can be difficult to control within standard inference pipelines. In this work, we propose a Template-Adapted Mixture Model to confront this problem in the context of signal fraction estimation: inferring the population proportion of signal in a mixed sample of signal and background, both of which follow arbitrarily complex distributions. We harness many biased simulations to perform data-driven estimates of each process distribution in the signal region, substantially reducing the bias on the signal fraction due to the domain shift between simulation and reality. We explore different methodological choices, including model selection, feature representation, and statistical method, and apply them to a Gaussian toy example and to a semi-realistic di-Higgs measurement. We find that the presented methods successfully leverage the biased simulations to provide estimates with well-calibrated uncertainties.

A constellation is a subset of $[n]=\{1,2, \ldots, n\}$ formed by scaling and translating a rational pattern $Q=\left[0, q_1, \ldots, q_{k-1}, 1\right]$, with key examples including arithmetic progressions. In 2010, Butler, Costello, and Graham proposed a conjecture, that is, for any constellation pattern $Q$ there is a coloring pattern of $[n]$ that has $γn^2+o\left(n^2\right)$ monochromatic constellations, where $γ$ is smaller than the coefficient for a random coloring. In this paper, we confirm this conjecture.

The Tolman-Oppenheimer-Volkoff (TOV) equations for a massless fluid take the form of a pair of coupled autonomous first order differential equations, which can be employed in a model for a ``dynamical gravastar'' black hole mimicker. The mimicker has no true horizon, but rather a ``simulated horizon'', outside which the geometry resembles a Schwarzschild black hole, but inside which the $g_{00}$ component of the metric is always positive and becomes exponentially small. Collins has reinterpreted the relevant TOV equations in terms of a two-dimensional flow with a spiral form, and Zöllner and Kämpfer have mapped the simulated horizon to a specific segment of the Collins spiral. We give here results of an asymptotic analysis, relating initial values at the small radius end of this spiral segment to the black hole mimicker mass and other parameters that emerge at the large radius kink in the TOV solution, which corresponds to the simulated horizon. A curious feature of this asymptotic mapping, given in Sec. IIB, is the appearance of power law behaviors with exponents of $1/5$ and $1/10$.

We study two $(1+1)$-dimensional systems, denoted $(R0)$ and $(Z0)$, that are rigorously derived from the three-dimensional axisymmetric Euler equations in a signed polar formulation on the meridian plane. The main point of view in this revision is that these $(1+1)$D systems are not ad hoc model equations and not merely ``symmetry-axis reductions.'' Rather, they arise as exact symmetry-axis/apex restrictions of the full $(1+2)$D system~$(E2)$ obtained from 3D axisymmetric Euler, and they already contain the core finite-time singularity mechanism of the full problem. The rev3 geometry is based on the symmetry axes \[ θ=0,\qquad θ=\pm \fracπ{2}, \] for which ridge flatness is preserved automatically by the evenness in $(r,z)$. Along these axes, and in particular at the apex $x^2=r^2+z^2=0$, the reduced dynamics closes exactly. This yields two rigorously derived $(1+1)$D systems: the horizontal-axis system $(R0)$ and the vertical-axis system $(Z0)$. The apex trace of these systems reduces further to a closed ODE of Constantin--Lax--Majda type, from which we obtain finite-time blow-up at the coordinate origin. The paper has three main outputs. First, it derives the signed-polar $(1+2)$D subsystem~$(E2)$ from the 3D axisymmetric Euler equations and identifies the exact $(1+1)$D systems $(R0)$ and $(Z0)$ carried by the symmetry axes. Second, it proves finite-time blow-up for the resulting apex dynamics and analyzes the associated convective axis reduction. Third, it derives the exact background--remainder equations and formulates a conditional nonlinear stability mechanism: if a compatible full background exists on $[0,T)$ with the coefficient bounds required by the weighted energy method, then the full solution inherits the same finite-time apex blow-up.

We prove a compactness result for gradient flow lines in a general set-up which comprises both the situation of Morse gradient flow lines as well as Floer cylinders converging to a critical submanifold respectively. For the compactness result we have to impose two conditions. Both are readily verified in the Morse case but establishing the second condition in the Floer case poses a technical challenge and relies on an exponential decay estimate for Floer cylinders, with coefficient function continuously depending on the initial loop. This is a result of independent interest.

TESS (Transiting Exoplanet Survey Satellite) has produced long-term photometry for millions of stars across the sky. In this work, we present an asteroseismic catalogue of 19,151 red giants in the TESS Continuous Viewing Zones using sectors 1--87 (Years 1--7). We visually assessed the power spectra for oscillations, and then applied the computationally efficient nuSYD method to confirm reliability. We identified an increase of 80% in the number of previously known oscillating red giants at a TESS magnitude $>$ 8. We determined the frequency of maximum power ($\rm ν_{max}$) and the large frequency separation ($\rm Δν$) using the pySYD pipeline, achieving typical precisions of 1.5% and 1%, respectively. We classified the stars into Red Giant Branch (RGB) and Core Helium Burning (CHeB) classes using a Convolutional Neural Network. Using spectroscopic data for 10,298 stars with reliable asteroseismic measurements, we have been able to measure stellar mass and radii with precisions of 7.5% and 2.8%, which is comparable to that from 4-yr $Kepler$ data. A comparison of the seismic radii with Gaia radii shows excellent agreement. With three years of TESS data, the asteroseismic parameters are precise enough to identify the RGB bump and delineate the Zero Age Helium Burning edge. Combined with astrometric data, these parameters reveal established trends across the Galactic plane, providing a valuable set of uniformly determined asteroseismic parameters for Galactic Archaeology.

This paper investigates the valuation of variable annuity contracts with an early surrender option under non-Markovian models. Moreover, policyholders are provided with guaranteed minimum maturity and death benefits to protect against the downside risk. Unlike the existing literature, our variable annuity account value is linked to two non-Markovian processes: an equity index modeled by a rough Heston model and a force of mortality following a Volterra-type stochastic model. In this case, the early surrender feature introduces an optimal stopping problem where continuation values depend on the entire path history, rendering traditional numerical methods infeasible. We develop a deep signature Least Squares Monte Carlo approach to learn optimal surrender strategies on a discretized time grid. To mitigate the curse of dimensionality arising from the path-dependent model, we use truncated rough-path signatures to encode the historical paths and approximate the continuation values using a neural network. Numerically, we find that the fair fee increases with the Hurst parameters of both the stock volatility and the force of mortality. Finally, a convergence proof is provided to further support the stability of our method.

Clustering mixed-type data remains a major challenge in biomedical research to uncover clinically meaningful subgroups within heterogeneous patient populations. Most existing clustering methods impose restrictive assumptions like local independence, fail to accommodate censored biomarkers, or unable to quantify variable importance. We propose a Bayesian finite mixture model (BFMM) clustering framework that addresses these limitations. BFMM flexibly models both continuous and categorical variables, incorporates three covariance structures to capture cluster-specific dependencies among continuous features, and handles censored observations through likelihood-based imputation. To facilitate feature prioritization, BFMM uses spike-and-slab priors to estimate variable importance on a continuous 0-1 scale. Simulation studies demonstrate that BFMM outperforms existing methods in clustering accuracy, particularly given strong within-cluster correlation or censored variables, and reliably distinguishes informative features from noise under varying conditions. We applied BFMM to two real-world datasets: (1) the SENECA cohort integrating electronic health records from patients with Sepsis; and (2) the EDEN randomized trial of patients with acute lung injury. In both settings, BFMM identified clinically interpretable phenotypes and revealed variable-specific contributions to subgroup differentiation. In the EDEN trial, it also uncovered evidence of treatment heterogeneity. These findings validate BFMM as an effective, interpretable, and practically useful clustering tool for complex biomedical datasets.

We investigate the discovery prospects of a singly produced vector-like bottom quark in the Type-II Two-Higgs-Doublet Model extended by an $SU(2)_L$ vector-like $(T,B)$ doublet. We focus on the non-standard decay chain $B \to ϕb$, followed by $ϕ\to t\bar{t}$, where $ϕ= H$ or $A$, leading to a final state with one charged lepton, missing transverse energy, and multiple $b$-jets. We perform a full simulation of both signal and Standard Model backgrounds at $\sqrt{s}=14$ TeV. We show that the exotic channels $B \to ϕb$ can dominate over the conventional decay modes, reaching branching ratios of order $50\%$ for both neutral scalars in the alignment limit. A conventional cut-based analysis provides a $5σ$ discovery significance only at sufficiently high integrated luminosity. By contrast, an XGBoost-based multivariate analysis substantially improves the signal-background discrimination and extends the discovery reach up to $m_B \simeq 1.3$ TeV with $600~\mathrm{fb}^{-1}$ and up to $m_B \simeq 1.6$ TeV with $3~\mathrm{ab}^{-1}$, even in the presence of systematic uncertainties as large as $15\%$.

Call an interval $\{N+1,\dots,N+H\}$ of consecutive natural numbers \emph{bad} if the product $(N+1) \dots (N+H)$ is divisible by the square of its largest prime factor; \emph{very bad} if this product is powerful, and \emph{type $F_3$} if it has the same squarefree component as a factorial. Such concepts arose in the analysis of the factorial equation $a_1! a_2! a_3! = m^2$ with $a_1<a_2<a_3$. Answering several questions of Erdős and Graham, we obtain asymptotics for the number of integers contained in bad or very bad intervals, and to get near-asymptotics for the number of right endpoints of a type $F_3$ interval, or on the number of solutions to $a_1! a_2! a_3! = m^2$.

Flexible surfaces can modulate fluid forces through deformation, enabling passive adaptation to flow conditions. Here we show that kirigami sheets, planar surfaces patterned with arrays of parallel slits, provide a simple route to tunable aerodynamics by transforming into three-dimensional porous meso-architectures that can be reversibly reconfigured in flow. When exposed to crossflow, parallel-cut kirigami buckle out of plane to form a lattice of inclined plate-like elements. Experiments reveal that this architecture generates not only drag but also a substantial transverse lift force, even when the sheet is held perpendicular to the incoming flow. Because the mesostructure can switch between distinct states, a single sheet produces large and selective variations in drag and lift under identical flow conditions, in some cases partially decoupling these forces. The evolving mesostructure also alters the scaling of forces with flow speed, influencing both instantaneous loads and their velocity dependence. Force measurements collapse when expressed in terms of the Cauchy number, identifying stiffness, set by the cutting pattern, as the dominant control parameter, a relationship captured by a continuum elastic model. These results show how kirigami architectures encode aerodynamic functionality and behavior directly through their structure, providing a scalable platform for surfaces with reprogrammable fluid forces.

Schrodinger path to the quantum mechanical wave equation was heuristic and guided more by physical intuition than formal deduction. Here we derive the Schrodinger equation for the particle wave function, assuming that it has a meaning of the probability amplitude to find the particle at time t at point r and the relations E=hw, p=hk expressing particle energy and momentum in terms of the frequency and wave vector of the associated probability wave.

Digital reticular chemistry relies on accurate crystal structures to power computational screening, data-driven discovery, and structure-property analysis, yet recent studies reveal that more than half of the top-performing candidates in major computational screening campaigns are chemically invalid. In experimental MOF databases, structural errors arise when disordered or incomplete structural models are incorrectly converted into fully specified simulation inputs. In hypothetical MOF database, structures are complete by construction but may encode chemically implausible oxidation states, coordination environments, or charge distributions. We term these erroneous structural models "structural demons." This mini-review asks three questions: where these errors enter, how we find them, and how we prevent them. On the prevention side, the key steps are keeping diffraction data and synthesis details together from the start, using consistent curation when structures enter a database, and filtering topology choices before structure generation. Connecting these steps can keep many bad structures out of downstream databases and reduce the need to fix them later.

Accurate identification of EGFR and KRAS mutations is essential for precision therapy in non-small cell lung cancer (NSCLC), but tissue genotyping is invasive and may not capture tumor heterogeneity. CT-based radiogenomics offers a noninvasive alternative, although generalization across centers remains challenging. We benchmarked handcrafted radiomics features (HRF), deep feature representations (DFR), and their fusion for three-class mutation prediction (wild-type, KRAS-mutant, and EGFR-mutant) with external testing. We curated 1,023 thoracic CT scans from 12 public datasets across more than 20 centers, including 136 patients with EGFR/KRAS labels. IBSI-compliant HRFs were extracted with standardized preprocessing, and DFRs were derived using PySERA. HRF-only, DFR-only, and fused HRF+DFR pipelines were evaluated using five-fold cross-validation and external testing. A semi-supervised pseudo-labeling strategy leveraged unlabeled CT scans, and SHAP supported interpretability. In external testing, HRF-based models generalized best, achieving AUC 0.77 +/- 0.07 and accuracy 0.77 +/- 0.00. DFR-based models showed a larger drop from cross-validation to external testing, with best external AUC around 0.57 +/- 0.05. Fusion improved robustness over DFR-only models but did not consistently outperform HRFs. SHAP identified morphology- and heterogeneity-related radiomic phenotypes as key predictors. Standardized handcrafted radiomics within a multicenter semi-supervised framework may provide a generalizable and interpretable approach for CT-based EGFR/KRAS stratification.

We resolve a puzzle associated with the spherically-symmetric sector of the AdS$_4$ Einstein-Maxwell partition function with inverse temperature $β$. Since charge is quantized, the semiclassical limit of the partition function is expected to be given by a sum over complex black hole solutions obtained by shifting the associated chemical potential $μ$ by $\frac{2πi n}{e β}$ in terms of the relevant charge quantum $e$. However, the sum over all such saddles turns out to diverge at any finite value of $β$. We therefore consider a definition of this partition function as an integral over a space of metrics that are real and of Lorentz-signature up to the presence of certain conical singularities. A Picard-Lefshetz analysis shows that only a finite subset of the above saddles contribute to our integral at finite $β$, and thus that the sum over such saddles converges. The low temperature limit is nonetheless associated with a convergent sum over all saddles that (as $β\rightarrow \infty$) approach the usual large real Euclidean black holes. We also analyze the analogous partition function for the (uncharged) BTZ black hole in the ensemble defined by fixing an angular velocity $Ω$ up to shifts by $\frac{2πi m}{s β}$, where $s=\frac{1}{2}$ or $s=1$ depending on the presence of absence of fermionic states. In this case, at all $β$ we find that all saddles contribute and that the sum over saddles converges. We also comment briefly on the apparent lack of utility of the so-called KSW condition in our context.

Non-Hermitian evolution is mathematically invertible, yet finite dynamic range imposes a sharp operational limit on reversibility. We identify Precision-Induced Irreversibility (PIR): amplification, mode mixing (as warranted by non-normality), and a finite resolution floor -- whether set by numerical precision, detector noise, or environmental fluctuations -- conspire to produce a quantitative predictability horizon $T_{\mathrm{of}}$, beyond which distinct states collapse onto identical representations. Within the effective non-Hermitian description, the mechanism requires neither environmental decoherence nor nonlinear dynamics; remove any ingredient and reversibility can be restored. Echo-fidelity tests confirm this transition across arbitrary-precision arithmetic and hardware, revealing where formal invertibility and physical reversibility diverge.

Classical (or ``global'') Bernstein theory establishes sharp control on entire functions of exponential type that are bounded and real-valued on the real axis. We localize some of this theory to rectangular regions $\{ x+iy: x \in I, 0 \leq y \leq y_0 \}$, showing that Bernstein-type bounds with acceptable errors can continue to hold for functions holomorphic in such rectangles, bounded and real-valued on the lower edge of the rectangle, at most exponentially large on the upper edge, and at most double exponentially large on the vertical sides. As a consequence of these bounds, we are able to localize the Erdős lower bound $\sup_{x \in [-1,1]} λ(x) \geq \frac{2}π \log n - O(1)$ on the Lebesgue constant of interpolation on $C([-1,1])$ to shorter intervals $I$ than $[-1,1]$, answering a question of Erdős and Turán. By using suitably weighted versions of the residue theorem, we also obtain the asymptotically sharp lower bound $\int_I λ(x)\ dx \geq \frac{4|I|}{π^2} \log n - o(\log n)$ for integral variants of such constants, answering a further question of Erdős.

The migration to post-quantum cryptography is urgent for Internet of Things devices with 10--20 year lifespans, yet no systematic benchmarks exist for the finalised NIST standards on the most constrained 32-bit processor class. This paper presents the first isolated algorithm-level benchmarks of ML-KEM (FIPS 203) and ML-DSA (FIPS 204) on ARM Cortex-M0+, measured on the RP2040 (Raspberry Pi Pico) at 133 MHz with 264 KB SRAM. Using PQClean reference C implementations, we measure all three security levels of ML-KEM (512/768/1024) and ML-DSA (44/65/87) across key generation, encapsulation/signing, and decapsulation/verification. ML-KEM-512 completes a full key exchange in 35.7 ms with an estimated energy cost of 2.83 mJ (datasheet power model)--17x faster than a complete ECDH P-256 key agreement on the same hardware. ML-DSA signing exhibits high latency variance due to rejection sampling (coefficient of variation 66--73%, 99th-percentile up to 1,125 ms for ML-DSA-87). The M0+ incurs only a 1.8--1.9x slowdown relative to published Cortex-M4 reference C results (compiled with -O3 versus our -Os), despite lacking 64-bit multiply, DSP, and SIMD instructions--making this a conservative upper bound on the microarchitectural penalty. All code, data, and scripts are released as an open-source benchmark suite for reproducibility.

Discoveries of ultra-massive magnetic white dwarfs (WDs) and peculiar pulsars have been proposed to originate in double white dwarf (DWD) mergers. There are three possible post-merger central remnants of non-explosive mergers: 1) a stable sub-Chandrasekhar WD; 2) a rapidly rotating super-Chandrasekhar WD; 3) a neutron star (NS). In this work, we explore the thermal transient arising from non-explosive DWD mergers that leave an NS remnant from the prompt collapse of the merged core. The transient is powered by the cooling of the expanding dynamical ejecta, with energy injection from magnetic dipole radiation, which depends on the dipole factor $D = B_d^2/P_0^4$, with $B_d$ and $P_0$ being the surface magnetic field strength and initial rotation period of the newborn NS. We simulate lightcurves in the Legacy Survey of Space and Time (LSST) bands and estimate the horizon and detection rates for these transients across a range of model parameters. We find LSST detection horizons upper limits ranging $30$--$820$ Mpc and corresponding detection rates $10^2$--$10^6$ yr$^{-1}$ for $\log D = 24$--$40$. Accounting for the survey cadence, we find that only configurations with $\log D = 36$--$40$ are detectable within $240$--$760$ Mpc, with detection rates $10^4$--$10^5$ yr$^{-1}$. Combined searches across surveys can compensate for the low cadence and improve the detection rates of fast and less energetic sources. Multi-wavelength campaigns can aid in detecting the spindown radiation at higher energies observable after the optical transient. Observations of these transients will provide direct evidence of the non-explosive DWD mergers, characterise the remnants and progenitor parameters, and the fraction of explosive and non-explosive mergers.

The interaction between conduction electrons and localized magnetic moments profoundly influences the electrical and magnetic properties of materials, giving rise to a variety of fascinating physical phenomena and quantum effects. Here, we discover a giant anomalous Hall effect (AHE) in a frustrated Eu-based magnet, exhibiting a giant anomalous Hall conductivity (AHC) of 31000 Ω-1cm-1 and a remarkable anomalous Hall angle (AHA, tanθH) of 12 %--surpassing conventional mechanisms (either intrinsic or extrinsic) by two orders of magnitude. Combining magnetotransport, quantum oscillations, neutron diffraction and ab initio calculations, we establish that the giant AHC originates from fluctuating spin chirality skew scattering, generated by indirect Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions of Eu-4f moments. Simultaneously, Hund's coupling of itinerant electrons and localized Eu-4f spins triggers giant exchange splitting, evidenced by temperature-dependent Fermi surface reconstruction. This work establishes a frustrated magnetic platform for engineering the AHE and elucidates the governing role of exchange interactions and spin textures in quantum transport, while also providing a framework for designing unconventional spintronic systems that harness emergent spin-texture dynamics.

Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. This paper develops an integrative framework for analysing agentic finance: financial market environments in which autonomous or semi-autonomous AI systems participate in information processing, decision support, monitoring, and execution workflows. The analysis proceeds in three steps. First, the paper proposes a four-layer architecture of financial AI agents covering data perception, reasoning engines, strategy generation, and execution with control. Second, it introduces the Agentic Financial Market Model (AFMM), a stylised agent-based representation linking agent design parameters such as autonomy depth, heterogeneity, execution coupling, infrastructure concentration, and supervisory observability to market-level outcomes including efficiency, liquidity resilience, volatility, and systemic risk. Third, it presents an illustrative empirical application based on event studies of AI-agent capability disclosures and heterogeneous market repricing. The paper argues that the systemic implications of AI in finance depend less on model intelligence alone than on how agent architectures are distributed, coupled, and governed across institutions. The empirical application is intentionally exploratory: it does not validate the full AFMM, but shows how one observable expectations channel can be studied using public data. In the near term, the most plausible equilibrium is bounded autonomy, in which AI agents operate as supervised co-pilots, monitoring systems, and constrained execution modules embedded within human decision processes.

Blockchain technology enforces the security, robustness, and traceability of operations of Process-Aware Information Systems (PAISs). In particular, transparency ensures that all data is publicly available, fostering trust among participants in the system. Although this is a crucial property to enable notarization and auditing, it hinders the adoption of blockchain in scenarios where confidentiality is required, as sensitive data is handled. Current solutions rely on cryptographic techniques or consortium blockchains, hindering the enforcement capabilities of smart contracts and the public verifiability of transactions. This work presents the CONFETTY open-source web application, a platform for public-blockchain based process execution that preserves data confidentiality and operational transparency. We use smart contracts to enact, enforce, and store public interactions, while we adopt attribute-based encryption techniques for fine-grained access to confidential information. This approach effectively balances the transparency inherent in public blockchains with the enforcement of the business logic.

We demonstrate noninvasive measurement of gas temperature based on the optical gas imaging. Gas flows containing carbon dioxide (CO2) appear as either bright or dark images, depending on the relative temperatures of the background and the gas, when using a narrowband mid-infrared camera tuned to the CO2 absorption wavelength at 4.3 micrometers. When the background temperature is varied continuously, the gas image vanishes transiently and then the contrast reverses. The specific background temperature at the point when the gas image disappears provides the gas temperature. This technique is an evolved implementation of the classical line reversal method, made possible by advanced infrared devices. We also apply this technique to two-dimensional temperature mapping and to dynamic emissions from engine exhaust and human breathing.

Extended radio sources present unique challenges for automated detection and classification in wide-field radio surveys. With current surveys such as the Evolutionary Map of the Universe (EMU), robust and scalable methods are essential to identify and catalogue these complex sources. We apply three automatic approaches to detect complex radio emission in EMU observations of the Galaxy And Mass Assembly (GAMA) 09 field (EMU-G09) in order to evaluate their relative strengths and limitations in preparation for large-scale application across future EMU data releases. These include DRAGNHunter, designed to detect likely DRAGNs (Double Radio sources associated with Active Galactic Nuclei) from a component catalogue; coarse-grained complexity, a metric designed to highlight regions of complex emission; and RG-CAT, a machine learning pipeline trained on radio sources identified in the EMU pilot survey. We find that together, the three methods recover nearly all extended sources in EMU-G09 but identify largely distinct, partially-overlapping subsets, with only 375 sources identified by all finders. This demonstrates that a combination of complementary techniques will be required to achieve a complete census of extended radio sources in future large-scale surveys.

Incorporating oxygen into metallic atomic-scale junctions modifies the interatomic bonding and may even promote the formation of monoatomic chains. In the specific case of copper oxide, first-principles studies have predicted the emergence of ferromagnetic ground states, attributing certain atomic configurations with spin filtering capabilities. By means of low-temperature transport measurements, we provide a series of experimental evidence indicating the presence of local magnetism in air oxidized mechanically controllable copper break junctions. Our findings include hysteretic magnetoresistance, zero-bias anomalies attributed to the Kondo effect and anomalous shot noise behavior. We provide an extension of the Landauer description of shot noise for quantum coherent transport to include energy-dependent transmission functions. The analysis of the anomalous shot noise in the presence of strong zero-bias anomalies arising from the Kondo effect allows to determine the spin polarization of the current which may reach even full polarization, confirming the spin-filtering capability of copper oxide atomic contacts.

Collective modes in Fermi liquids are usually regarded as dissipation channels that relax electronic excitations through Landau damping. Whether such modes can instead mediate the formation of correlated electronic states under non-equilibrium conditions remains an open question. Here we show that, under optical photo-doping, a bulk plasmon can drive correlated inter-band transfer within a transient electronic continuum. Using time- and angle-resolved photoemission spectroscopy (Tr-ARPES) on EuCd$_2$As$_2$ supported by electronic structure calculations, we observe that at high excitation density, plasmons transfer energy from a weakly dispersing bulk band into unoccupied surface states. This bulk-to-surface redistribution stabilizes a long-lived, energy-localized spectral feature consistent with a Mahan exciton. Our results uncover a non-equilibrium regime of Fermi-liquid physics in which collective modes do not merely dissipate energy, but also stabilize correlated bound states.