Proponents of software verification suggest that code simplicity is linked to the effort to verify code, hypothesizing that formal verifiers produce fewer false positive warnings and require less manual intervention when analyzing simpler code. A recent meta-analysis study found empirical support for this hypothesis: a small correlation between the sum of verifier warnings and human-derived code comprehensibility metrics. Based on this finding, we conjectured that using the sum of verifier tool (verifier) warnings to represent program semantic information as an input feature to machine learning (ML) models for code comprehensibility prediction can enhance their performance, when combined with traditional syntactic and developer features. To test this conjecture, we performed a control-treatment experiment incorporating the verifier warning sum feature into machine learning models from the literature, and conducted a comparative analysis of their performance against models trained only on syntactic and developer features. We found no significant difference in the prediction performance of models with and without the warnings feature. Our findings suggest that while a correlation exists, the verifier warning sum offers limited discriminative power: combining syntactic and developer features is just as effective for predicting human-judged code comprehensibility.

This study presents a structured dataset of blockchain-registered artificial intelligence agents under the ERC-8004 standard on Ethereum. The dataset integrates on-chain identity records, minting transactions, transfer events, reputation summaries, and individual feedback records, together with resolved off-chain metadata where available. Data were collected from Ethereum mainnet using Web3 RPC queries and processed into tabular form to enable reproducible analysis. The dataset covers 10,000 agents within a defined block range and includes both event-level records and aggregated summaries. It enables empirical research on agent identity formation, reputation systems, service exposure, and early-stage decentralized AI ecosystems. This resource supports studies in blockchain analytics, decentralized trust infrastructure, and the emerging agentic economy.

We present a new algebraic characterisation of Eve-positionality for $ω$-regular languages. It involves only a limited number of elementary local properties to be checked. An $ω$-regular language is Eve-positional if, in all games with this language as objective, the existential player (Eve) can play optimally without keeping any information concerning the history of the moves seen so far. This notion plays a crucial role in verification, automata theory and synthesis. Our proof heavily relies on a recent result of Casares and Ohlmann which states several characterisations of Eve-positionality for $ω$-regular languages. More precisely, it relies on their a 1-to-2 player lift result: for an $ω$-regular language, being Eve-positionally over all finite Eve-only arenas suffices for being Eve-positional over all two-player arenas.

Phase sensitive detection in spectral domain optical coherence tomography (SD-OCT) is a powerful method for functional imaging of biological events with high spatiotemporal resolution. The depth-dependent signal-to-noise ratio (SNR) is a limiting factor on the minimum detectable phase changes of phase in shot noise-limited SD-OCT systems. The SNR over a depth is constrained by the terminal optics, usually using a focusing lens to project light into the tissue and collect the backscattered light. In situ ultrasonically sculpted optical waveguides have been used to improve SNR roll-off over depth compared to conventional SD-OCT systems. In this paper, we extend this feature to demonstrate phase sensitive detection at depth using ultrasonically enhanced OCT (ue-OCT). Our experimental results show that ultrasonically sculpted optical waveguides are phase stable and follow near shot-noise limited behavior. We measured milk flow velocity changes to demonstrate a phase sensitivity of 5.25 mrad at 10 dB SNR and dynamic range of 0.8 mm/s to 14.7 cm/s using ue-OCT. Our results show flow detection with ue-OCT at extended depths (i.e., 3.5 mm) otherwise not possible with conventional SD-OCT systems with matched focal lengths. The results in this paper show the potential of ue-OCT for phase-sensitive flow measurement from the depth of tissue for a gamut of applications such as cerebral blood flow imaging as a proxy to neural activity mapping.

We study a mathematical model of in situ leaching of rare metals, in which the joint filtration of two liquids is governed by the microscopic model $\mathbb{A}^{\varepsilon}$. A key difficulty is the unknown (free) boundary $Γ(r)$ between solid and liquid components, determined by an additional condition on $Γ(r)$; no standard methods exist for this nonlinear problem. To resolve it, we apply the fixed point theorem. For a given function $r(\boldsymbol{x},t)$ from a set $\mathfrak{M}_{(0,T)}$ of sufficiently smooth functions describing the skeleton structure, we consider the auxiliary problem $\mathbb{B}^{\varepsilon}(r)$: an elliptic system for displacements of the liquid and solid components coupled with parabolic equations for the acid concentration. Selecting the weak solution of minimal smoothness, we apply the homogenization method to pass from the microscopic to the macroscopic description. The resulting macroscopic model $\mathbb{H}(r)$ contains a homogenized boundary condition that expresses the normal boundary velocity $V_{N}=\partial r/\partial t$ as a linear function of the acid concentration $c$. Since $c$ depends on $r$ via an operator $\mathbb{F}\colon\mathfrak{M}_{(0,T)}\to\mathfrak{M}_{(0,T)}$, we prove that $\mathbb{F}$ is Lipschitz continuous and, by Banach's theorem, possesses a unique fixed point $r^{*}$, which yields the unique solution $\mathbb{H}=\mathbb{H}(r^{*})$.

In this paper, we propose and analyze a novel one-dimensional inhomogeneous random walk model that combines spatial decay of transition probabilities with a temporal renewal structure for each excursion. In this model, the probability of moving to the right from each state creats a spatial inhomogeneity that causes a stronger pull-back toward the origin as the process moves farther away. Furthermore, it features a random environment aspect where the parameter of each transition probability is independently resampled from a uniform distribution at the beginning of each excursion. We rigorously derive the hitting probability to an upper boundary using a scale function. Furthermore, by solving linear difference equations, we provide the probability generating function of the first hitting time, the expected occupation time for each state during an excursion (discrete Green's function), and the distribution and expectation of the maximum penetration depth.

Black-hole X-ray binaries (BHXRBs) in the hard and hard-intermediate spectral states commonly exhibit prominent type-C quasi-periodic oscillations (QPOs) in their X-ray power spectra. Despite extensive observational and theoretical efforts, the physical mechanism responsible for these oscillations has not yet been firmly established. The disk-corona system in BHXRBs is radiatively coupled, as hard X-ray emission from the corona can be reprocessed by the accretion disk and re-emitted as soft photons that contribute to cooling the coronal electrons. Aim of the present study is to examine whether this feedback can give rise to limit cycles having the spectro-temporal properties of QPOs. We model the coronal emission using a one-zone radiation framework and solve the time-dependent kinetic equations for electrons and photons. Electrons are energized by some unspecified process and cool via inverse Compton scattering of soft photons originating from (i) the accretion disk and (ii) disk reprocessing of the hard radiation produced in the corona. When electron cooling is dominated by soft photons reprocessed in the accretion disk, the disk-corona system undergoes limit-cycle oscillations. For a subset of the model parameters, these oscillations reproduce key properties of type-C QPOs observed in BHXRBs. The oscillation frequency depends on the coronal radius and on the energization timescale, while the resulting X-ray spectra are well described by power laws extending up to energies of ~ 10-100 keV. These calculations confirm and extend earlier semi-analytical results obtained with simplified treatments. Owing to the scale-invariant nature of the model, the results can be readily extrapolated to other accreting systems, such as Active Galactic Nuclei.

We generalize complex manifolds to manifolds with corners $X$, and to manifolds with generalized corners (g-corners) in the sense of the second author arXiv:1501.00401, using complex structures on the b-tangent bundle (log tangent bundle) ${}^bTX$. We prove a formal Newlander-Nirenberg type theorem showing that along each corner stratum of $X$, the b-complex structure agrees with a standard model to infinite order. In the sequel we show that if $S$ is a log smooth log $\mathbb C$-scheme, or log smooth log complex analytic space, then the Kato-Nakayama space $S^{\rm KN}$ has the structure of a b-complex manifold with g-corners. Using our Newlander-Nirenberg theorem we give necessary and sufficient conditions for a b-complex manifold with g-corners to be a Kato-Nakayama space.

We propose parameter-robust preconditioners for the statically condensed linear system arising from a hybridizable discontinuous Galerkin discretization of the coupled Stokes--Darcy system. The design strategy relies on first applying the operator-preconditioning framework [Numer. Linear Algebra Appl., 18(1):1--40, 2011] to construct a preconditioner for the non-condensed discretization. This is done by proving uniform well-posedness of the scheme. Next, we prove robustness of the resulting condensed preconditioner applied to the reduced linear system using the framework we proposed in [SIAM J. Sci. Comput., 47(6):A3212--A3238, 2025]. Numerical examples demonstrate robustness of the proposed preconditioners.

In this paper, we propose a novel stochastic process that serves as a natural discrete-time counterpart to the continuous-time model known as the ``Poisson hyperbolic staircase'' proposed by Levikson et al. (1999), and clarify its analytical properties. The proposed model is a Markov chain on the state space $(0,1]$. Its transition rule states that at each time step, it jumps downwards to a value less than or equal to the current state according to a continuous uniform distribution with a probability proportional to the current state, and otherwise remains in the same state. In the analysis of the continuous-time model, the scaling property based on the continuity of time and space serves as a powerful tool. However, for this discrete-time process, an essential analytical difficulty arises because this scaling property is inapplicable. To overcome this difficulty, we adopt an approach that directly evaluates recurrence relations and integral equations. First, starting from the conditional transition of this process, we derive closed-form expressions for the marginal distribution and the joint survival function. Next, focusing on the counting process representing the number of jump occurrences and the sum of the state variables, we provide exact closed-form expressions for the probability generating function and the Laplace transform. Furthermore, we clarify the necessary and sufficient conditions that a sequence of functions must satisfy to construct a martingale associated with this process, and present a concrete sequence of martingales.

An action of a group $G$ on a set $X$ is called ``decent'' if every subgroup of $G$ with a finite orbit in $X$ fixes a point in $X$ and every finitely generated subgroup of $G$ such that every element of the subgroup fixes a point of $X$ must itself have a global fixed point. In this article, we study conditions on when actions of groups on restricted products are ``decent''. We prove that the action of the automorphism group of a restricted product with base space the projective plane $\mathbb{P}^2(k)$ over a field $k$ is decent, generalizing a result of Lonjou--Przytycki--Urech.

For the family of Lozi maps $L_{a,b}$, we consider parameter pairs for which the f\mbox{}ixed point $X$ has no homoclinic points and the period-two orbit $\{P,P'\}$ is attracting. For such parameters, let $\ell$ be the set of accumulation points of the unstable manifold $W_X^u$ that do not lie on $W_X^u$. We construct a polygon $\mathcal{D}$ whose forward images under $L_{a,b}$ form nested sequences of sets that eventually become trapping. We show that this geometric construction gives a characterization of $\ell$ as the intersection of these iterates. Using this structure, we prove that the non-wandering set for $L_{a,b}^2$ is contained in the union of $\ell$ and the set of f\mbox{}ixed points of $L_{a,b}$. As a consequence, the Lozi map, restricted to the complement of $\ell$ in the plane, has zero topological entropy. This result extends a recent one of Misiurewicz and Štimac to a broader set of parameters.

For $k$-graphs $F$ and $H_0$ the $F$-bootstrap percolation process (or $F$-process) starting with $H_0$ is a sequence $(H_i)_{i\geq0}$ of $k$-graphs such that $H_{i+1}$ is obtained from $H_i$ by adding all those $e\in V(H_0)^{(k)}\setminus E(H_i)$ as edges that complete a new copy of $F$. The running time of this $F$-process, denoted by $M_F(H_0)$, is the smallest $i$ with $H_i=H_{i+1}$. Bollobás proposed the problem of determining the maximum running time for $n\in\mathbb{N}$, i.e., $M_F(n)=\max_{\vert V(H_0)\vert=n}M_F(H_0)$. Although this problem has received a lot of attention recently, until now the best known upper bound for $M_{K_t}(n)$, with $t\geq5$, was the trivial bound $\binom{n}{2}$. Here we provide the first non-trivial upper bound for this problem by showing that $$M_{K_t}(n)\leq\Big(\frac{t-3}{t-2}+o(1)\Big)\binom{n}{2}$$ holds for every integer $t\geq 3$. In fact, we prove the following more general result. For every $k\geq2$, every $k$-graph $F$, and every $e\in E(F)$ we have $M_F(n)\leq\big(π(F-e)+o(1)\big)\binom{n}{k}$, where $π$ is the Turán density.

We present FlashFolio, a GPU-accelerated solver for single-period and multi-period portfolio optimization with factor-based risk modeling, bid-offer spread costs, and nonlinear market impact. These models are widely used in portfolio construction and optimal execution, but become computationally challenging at large scale, especially in the multi-period setting. We benchmark FlashFolio against MOSEK on instances constructed from realistic market inputs. FlashFolio delivers consistent runtime improvements, achieving speedups of up to 12.9x in the single-period setting and 48x in the multi-period setting, while also exhibiting stronger robustness on challenging multi-period instances. Our results show that GPU-based optimization can help improve the practicality of large-scale portfolio optimization.

Many engineered systems must balance competing objectives, such as performance and safety, cost and reliability, or efficiency and sustainability, and are naturally modeled as compositions of interacting subsystems. We study online multi-objective decision-making in monotone co-design, where functionalities and resources are partially ordered, and the goal is to identify the target-feasible antichain of non-dominated trade-offs using few expensive evaluations. We introduce optimistic evaluators: history-dependent bounds on functionality and resource mappings that enable safe elimination of implementations before full evaluation. Based on these evaluators, we develop an elimination-based rejection-sampling algorithm, prove its soundness, and show that the admissible region shrinks monotonically as information accumulates. We instantiate the framework under monotonicity, Lipschitz continuity, and linear-parametric structure. For compositional co-design problems modeled by multigraphs, we show how local optimistic certificates propagate through the tractable remainder of the graph to yield system-level optimistic feasibility and resource bounds. Experiments on multi-robot fleet design, intermodal mobility systems, and synthetic monotone and Lipschitz benchmarks show substantial sample-efficiency gains over uniform sampling, Bayesian optimization, and multi-objective evolutionary algorithms.

In certain modified theories of gravity, non-minimal couplings between matter and geometry lead to the nonconservation of the energy-momentum tensor. By interpreting this as an effective dissipative process, we formulate a general class of f(R, Matter) theories with the Herglotz variational principle, a variational approach designed for dissipative systems. We demonstrate that, for an appropriate choice of the Herglotz contribution, the resulting Herglotz extension of f(R, Matter) gravity restores the covariant conservation of the energy-momentum tensor.

Three-dimensional two-layer incompressible Euler fluids are studied from a Hamiltonian perspective. A natural Hamiltonian structure for the effective 2D model described by the interface-value of the field variables is obtained by means of a Hamiltonian reduction process from the 3D Poisson structure. The problem of expressing the fluid's energy in terms of the reduced variables is considered, and it is shown that in the weakly non linear approximation our procedure gives rise to a so-called 2D Kaup-Broer-Kupershmidt Boussinesq (KBK-B) model with ``critical" parameters. A model weakly dependent on one of the two horizontal directions is also discussed, whose unidirectionalization turns out to be the well-known Kadomtsev-Petviashvili (KP) equation. A Dirac-type reduction process of the Hamiltonian structure of the KBK-B model yields a natural Hamiltonian structure for KP qua 2+1-dimensional model.

The quest for the origin of cosmic ray (CRs) is a fundamental issue in astrophysics. Shocks of supernova remnants (SNRs) have been considered as the dominant contributors to Galactic CRs below the spectral knee near $\sim 3$ petaelectronvolt (PeV). Whether SNRs are efficient accelerators of particles beyond PeV energies has long been debated. Here we report observations of very-high-energy $γ$-ray emission up to hundreds of TeV from two middle age shell-type SNRs, G150.3$+$4.5 and $γ$-Cygni, with the Large High Altitude Air Shower Observatory (LHAASO). Two (or three) distinct morphological/spectral components with convex spectral shapes are observed in both sources, with the low-energy one being more extended than the high-energy one. %Although it is possible that these high-energy components may be driven by powerful pulsars, The likely association of the high-energy component with molecular clouds at similar distances, and the weakness/absence of pulsar wind nebulae (PWNe) inside these SNRs clearly indicate for the first time that the highest energy emission is produced by collision of hadronic CRs up to PeV energies with the clouds. These results are compatible with the classic model prediction that PeV particles accelerated near the end of the free expansion phase of SNR evolution can illuminate nearby molecular clouds (MCs) to produce strong $γ$-ray emission.

The rapid scaling of artificial neural networks has exposed fundamental limitations of conventional von Neumann computing architectures. In these systems, the physical separation between memory and processing creates a bottleneck, as computational capabilities outpace the ability of memory and interconnects to supply and retrieve data. In contrast, biological neural systems inherently co-localize computation and memory through distributed, dynamical processes. Neuromorphic computing seeks to emulate this paradigm by leveraging physical substrates whose intrinsic dynamics simultaneously encode and process information. Among emerging platforms, silicon photoncis offer a compelling approach due to its high bandwidth, low-loss propagation, and inherent parallelism. This review examines the role of memory in integrated photonic neuromorphic systems, with emphasis on the physical mechanisms that provide volatile (short-term) and non-volatile (long-term) memory in silicon-on-insulator and hybrid silicon-on-insulator platforms. Drawing inspiration from digital, biological, and photonic memory architectures, we classify existing approaches based on their underlying physical principles. We cover implementations ranging from delay lines and slow-light structures to multistable dynamics and structural memory based on charge trapping and phase-change materials. We then discuss how these mechanisms support photonic neural network architectures, including feed-forward, reservoir computing, spiking and hybrid optoelectronic recurrent systems, and assess their relevance for time-dependent singal-processing tasks such as channel equalization in telecommunications. This review aims to establish a unified framework for understanding memory and learning in neuromorphic photonics and outlines key challenges and opportunities for scalable, energy-efficient neuromorphic hardware.

RDF-based systems increasingly operate in event-driven and streaming settings, where producers and consumers exchange data as discrete units of communication rather than as freely mergeable RDF statements. As existing RDF semantics and tooling do not provide an interoperable notion of what statements belong together as one message, developers often rely on out-of-standard techniques, transport-level assumptions, or heuristics, leading to interoperability problems and inefficiencies. We propose the concept of an RDF Message as an RDF Dataset intended to be interpreted atomically as a single communicative act, laying the foundation for defining RDF Message Streams and RDF Message Logs. The proposal makes message boundaries explicit across serializations, transport, and storage systems, which in turn enables incremental consumption and reproducible replay in use cases such as IoT observations, archived RDF streams, nanopublications, or processing SPARQL CONSTRUCT results. Building on this, RDF Message Profiles, such as Linked Data Event Streams or ActivityStreams, then provide the terms for describing pagination, message structure, ordering, or retention policies. As part of the W3C Community Group on RDF Stream Processing, we are now seeking broader support and comments on the proposal from the Semantic Web community.

Accurate characterization of polarization dependent light matter interactions in nanostructured systems is paramount for the development of chiral metasurfaces. It is also often challenging, because multiple anisotropic mechanisms, such as linear and circular diattenuation, birefringence, and depolarization can coexist and couple with one another. Conventional ellipsometric and chiro optical techniques typically assume isolated polarization effects and can therefore yield inaccurate estimates of the intrinsic polarization parameters. Here, we demonstrate that Mueller matrix polarimetry combined with a differential Mueller matrix decomposition provides a robust framework for retrieving the intrinsic polarization response of complex nanophotonic systems. Using plasmonic gammadion arrays and media with multiple polarization anisotropies as multi modal chiral platforms, we show that simultaneous linear and circular anisotropies produce coupled signatures in the Mueller matrix, leading to significant artifacts in conventional polarization observables. Through analytical modeling and experimental measurements, we quantify these artifacts and demonstrate that a differential decomposition accurately decouples and retrieves the underlying polarization parameters. The presented approach also successfully probes the polarization anisotropic effects in inhomogeneous media enabling a clear discrimination between the intrinsic chiral optical response and geometric phase effects arising from spin orbit interaction of light in momentum resolved scattering. These results establish differential Mueller matrix polarimetry as a powerful tool for rigorous characterization of polarization phenomena in nanostructured photonic systems and polarization engineered metasurfaces.

This work is a thorough investigation of skew-orthogonal polynomials with respect to a quartic Freud weight. We provide an explicit method to evaluate skew-orthogonal polynomials of any degree as linear combinations of orthogonal polynomials. The coefficients of these combinations can be evaluated via novel recursive relations. Moreover, we observe that skew-orthogonal polynomials with even and odd degree constitute two families of quasi-orthogonal polynomials with respect to two different semi-classical Laguerre weights, and we provide the first instance of closed recursive relations involving skew-orthogonal polynomials only.

The momentum-space derivatives of Bloch wavefunctions are essential for studying quantum geometry and the equilibrium and response properties of solids. In practical first-principles calculations, these derivatives are obtained via Wannier interpolation of position and related composite matrices. These matrices are initially evaluated on a coarse k-point grid using finite-difference approximations and then interpolated to a dense grid. The accuracy of the finite-difference approximation directly impacts the convergence and reliability of the result. In this work, we present two key improvements to the finite-difference calculation of position and composite operators for Wannier interpolation. First, we formulate a translationally equivariant scheme that preserves the underlying symmetries of the system and significantly reduces finite-difference errors. Second, we introduce a higher-order finite-difference approach that yields a more accurate approximation of the k-space derivatives by systematically increasing the convergence rate. From a real-space perspective, these improvements correspond to better approximations of the position operator at the locations of the Wannier functions. We also present a generalization of the finite-difference scheme, which may reduce the number of finite-difference points while maintaining accuracy. We demonstrate the effectiveness of our methods by applying them to the calculation of Wannier centers and spreads, electric polarization, off-diagonal position matrix elements, orbital magnetization, and spin Hall conductivity. Our results demonstrate significant reductions in finite-difference errors, elimination of symmetry-violating errors, and improved convergence with respect to k-point sampling. These methods have been implemented in the open-source packages and can be readily adopted in other Wannier-based codes with minimal computational overhead.

We use the framework of generalized entanglement wedges to revisit the connected wedge theorem (CWT). This construction identifies an entanglement wedge associated for any bulk region and allows us to rephrase the CWT in terms of the entanglement entropies of bulk regions. We establish new upper and lower bounds on the mutual information of boundary decision regions in terms of the entropies of certain bulk regions associated with a scattering configuration. We then define new bulk decision regions for which we show that a non-empty scattering configuration implies a connected entanglement wedge. This generalization of the CWT extends to asymptotically flat spacetimes.

In epistatic fitness landscapes, the fitness effect of a mutation depends on the genetic background and may even switch between deleterious and beneficial depending on the presence of another mutation. Epistatic interactions may cause both mutations to change the sign of each other's fitness effects (reciprocal sign epistasis) or only one mutation to do so (simple sign epistasis). Both these forms of epistasis influence evolutionary trajectories. While reciprocal sign epistasis has been associated with multi-peaked landscapes and their ruggedness, the role and relative frequency of simple sign epistasis in fitness landscapes have not been systematically investigated. Here, we prove that the presence of simple sign epistasis is associated with evolutionary detours, i.e., indirect, longer fitness-increasing paths to fitness peaks that include back-mutations. We also show that in experimentally resolved, weakly epistatic landscapes, simple sign epistasis occurs much more frequently than reciprocal sign epistasis. This result is consistent with the theoretical predictions we derive for most landscape models, with the exception of the block model and of landscapes dominated by pairwise allelic incompatibilities, such as RNA stability landscapes. Our results suggest that detours represent a general feature of evolutionary trajectories in weakly epistatic landscapes.

Despite steady global advances, maternal mortality remains alarmingly high in Pakistan (155 deaths per 100,000 live births in 2023); largely as a consequence of fragmented paper records, low literacy, poor access to quality healthcare, and gendered barriers that compromise care continuity. Over three years, we designed, deployed, and iteratively developed Awaaz-e-Sehat, a speech-based artificial intelligence (AI) system that generates electronic medical records (EMRs) and supports decision-making in maternal health. The tool evolved from a clinician-facing AI assistant that automated Urdu speech-to-EMR generation into a patient-centred WhatsApp-based platform, enabling women to generate their own structured clinical notes, receive AI-generated antenatal guidance, and share QR-coded records with providers anywhere in the country. This case study documents that translational journey, i.e., how the ground realities of workload, linguistic nuance, and infrastructural constraints reshaped our design. The result is not merely a new method of record-keeping, but a reimagining of antenatal care and electronic medical records themselves. In settings where clinicians are time-constrained and have little institutional incentive to document, Awaaz-e-Sehat proposes a model of care that centres patients as active participants in generating and owning their health data. By keeping patients informed about their own risk factors and integrating them into the clinical decision-support loop, the system transforms EMRs and CDSS from static institutional artefacts into dynamic tools for self-advocacy and shared accountability in maternal health.

The degeneration order of simultaneous similarity classes of $3\times 3$ nilpotent matrix tuples is determined, and is shown to be given by rank conditions.

Despite the remarkable success of the Standard Model in describing fundamental interactions, unresolved phenomena such as dark matter, dark energy, and matter-antimatter asymmetry strongly suggest the existence of physics beyond the Standard Model. The absence of new particle discoveries at the LHC indicates that such New Physics may be significantly heavier than the electroweak scale. In this context, Effective Field Theories offer a powerful framework for studying the indirect effects of heavy New Physics. This contribution reviews some of the recent advancements, computational tools, and phenomenology of Effective Field Theories, with a particular focus on the Standard Model Effective Field Theory.

Bubbles released from a needle show shape deformations that depend on the surfactant concentration of the surrounding liquid. We develop a model that predicts the surfactant concentration based on experimental early-stage observations of these deformations. Using high-speed imaging, we examine bubbles within the first 144 ms of ascent, corresponding to a vertical rise distance of approximately 40 mm and extract the instantaneous aspect ratio (AR) and analyse its temporal evolution. In clean conditions, bubbles exhibit pronounced shape oscillations resulting from the periodic exchange between surface and kinetic energy. The presence of surfactants leads to an immediate damping of these oscillations, characterised by reduced AR amplitudes and earlier peak deformations. This damping effect intensifies with increasing surfactant concentration until a near-saturation regime is reached, beyond which bubbles remain largely spherical and further increases in concentration produce indistinguishable AR profiles within the early-stage observation window. To develop the prediction model, an aspect-ratio-based analysis methodology is proposed, which yields an empirical relationship capable of estimating surfactant concentrations between 0 ppm and 2.9 ppm. We finally test the reliability of the model on unknown surfactant-laden bubbles. The model successfully detected the presence and relative extent of surfactant contamination as higher concentrations were introduced.

Prior to core collapse, the neutrino emission from red supergiants (RSGs) is so large that a nearby ($\lesssim1$kpc) RSG will become visible in current and near-future neutrino detectors. The rate of emission and the spectra of the pre-supernova (pre-SN) neutrinos from RSGs are sensitive to the temperature, density, and detailed isotopic composition of the core. During the last year of the star's life, these properties change considerably. Several factors of stellar evolution modeling - such as the treatment of mass loss and convective overshooting - alter the thermal conditions and composition of the RSG core as it approaches collapse. In this paper we present the first study of how varying the treatment of mass loss and convective overshooting together affects the pre-collapse core properties and neutrino emission of RSGs. We use the stellar evolution instrument MESA and construct a grid of 32 models with zero-age main sequence masses of $\{ 12, 15, 18, 20\}$ $M_\odot$, use the so-called 'Dutch' mass-loss scheme with wind efficiencies of $\{0.2, 0.4, 0.8, 1.0\}$, and consider two convective overshooting schemes. Our models use a large 206-isotope nuclear network in order to accurately compute the structure and composition of the star. We find that, in the last few days of the star's life, the general trend of the conditions and composition in the core of the star is one of contraction, heating, and deleptonization, but that during this phase, this general trend will be interrupted by the initiation of core silicon burning and shell burning episodes that cause the core to expand and undergo convective mixing with material of a higher proton fraction that temporarily reverses the deleptonization. The pre-SN neutrino emission reflects these changes with a gradual shift to higher energies and larger flux that becomes dominated by beta processes a few hours prior to the collapse.