Papers

Fibre Bragg gratings (FBGs) are widely used in optical sensing and communication systems. Femtosecond laser inscription (FLI) enables hydrogen-free, thermally stable, high-resolution, and complex structures of FBG fabrication, but its practical application is limited by manual operation, low throughput, and sensitivity to laser alignment. In this study, we present an AI-powered FLI system that enables automated, stable, and efficient FBG fabrication. By integrating a Multi-Layer Perceptron (MLP) model for real-time fabrication position correction, the system maintains precise laser alignment (-0.6 to 0.2 microns of the fibre core plane) and ensures consistent processing. Strong and weak FBGs were fabricated in different types of fibres, and their spectral characteristics-including central wavelength, reflectivity, and FWHM-exhibited high stability and repeatability. The results demonstrate that the proposed AI-powered FLI system significantly reduces manual intervention while achieving reliable FBG performance. This approach holds great promise for scalable, high-throughput FBG production and can be extended to the fabrication of arbitrary FBG structures across various fibre types. With further training and model refinement, the AI-powered FLI provides a scalable and intelligent platform for next-generation automated FBG manufacturing.

The advancement of next-generation Wi-Fi technology heavily relies on sensing capabilities, which play a pivotal role in enabling sophisticated applications. In response to the growing demand for large-scale deployments, contemporary Wi-Fi sensing systems strive to achieve high-precision perception while maintaining minimal bandwidth consumption and antenna count requirements. Remarkably, various deep learning-driven perception technologies have demonstrated the ability to surpass conventional resolution limits. However, the theoretical underpinnings of this phenomenon have not been thoroughly investigated in existing research. We find that under hardware-constrained conditions, the performance gains of deep learning in Wi-Fi sensing primarily originate from two aspects: prior information and temporal correlation, which act as specific forms of side information that reduce the estimation error bound. We construct a deep learning-based Wi-Fi sensing system using only a single transceiver pair and design experiments to validate these gains. The system achieves an average human pose estimation error of 0.2189 m and an average localization error of 0.6124 m, while operating in real time at 42 fps on commodity hardware.

Short-term forecasting of airport fog (visibility < 1.0 km) presents challenges in geographic generalization because many machine learning models rely on location-specific features and fail to transfer across sites. This study investigates whether fundamental thermodynamic and radiative processes can be encoded in a coordinate-free (location-independent) feature set to enable geographic transferability. A gradient boosting classifier (XGBoost) trained on Santiago, Chile (SCEL, 33S) data from 2002-2009 was evaluated on a 2010-2012 holdout set and under strict zero-shot tests at Puerto Montt (SCTE), San Francisco (KSFO), and London (EGLL). The model achieved AUC values of 0.923-0.947 across distances up to 11,650 km and different fog regimes (radiative, advective, marine). Consistent SHAP feature rankings show that visibility persistence, solar angle, and thermal gradients dominate predictions, suggesting the model learned transferable physical relationships rather than site-specific patterns. Results suggest that physics-informed, coordinate-free feature engineering can yield geographically transferable atmospheric forecasting tools.

Metasurfaces – planar arrays of subwavelength nanostructures – are typically realized with high-index dielectrics, while low-index platforms are often dismissed for their weaker contrast. Here, we identify and experimentally verify regimes where a low-index platform (SiO$_2$) surpasses a high-index counterpart (TiO$_2$). We demonstrate that a low index suppresses higher-order Bloch modes, enabling the design of efficient devices with relaxed feature sizes. Low-index metasurfaces also offer two intrinsic advantages: a broad, well-behaved chromatic response without the need for explicit dispersion engineering, and strong tolerance to fabrication errors. We validate these features experimentally with silica metagratings, metalenses, and structured-light phase plates at $\lambda=632\ nm$. The metagratings reach $\geq$50% absolute diffraction efficiency over a $200\ nm$ bandwidth, the metalenses deliver 75% absolute diffraction efficiency with diffraction-limited performance, and the vortex phase plates achieve 80% conversion efficiency at the design wavelength and 60% with $100\ nm$ wavelength detuning. These results delineate conditions where low-index metasurfaces outperform high-index designs, suggesting a route to scalable, broadband, fabrication error-resilient flat optics.

Autoregressive neural surrogates offer computational acceleration for fluid dynamics but inherently suffer from error accumulation and non-physical drift during long-term rollouts. Although hybrid strategies combining surrogate models and physics-based solvers have been proposed, they are limited to manual implementations for low-dimensional benchmarks. In this study, we propose an OpenFOAM-based hybrid framework, XRePIT (eXtensible Residual-based Physics-nformed Transfer learning), characterized by its fastness, robustness, and scalability. Unlike prior manual implementations (e.g., RePIT), XRePIT integrates a fully automated open-source workflow that manages the state transition between a neural surrogate and a traditional numerical solver (OpenFOAM) based on a monitored residual threshold. Using 3D buoyancy-driven flow as a testbed, we demonstrate that this residual-guided coupling enables stable long-term simulation-ell beyond the stability horizon of standalone surrogates. Our results indicate that the hybrid loop achieves up to 2.91x wall-clock acceleration while maintaining relative L2 errors within O(1E-03) Furthermore, we benchmark the framework's extensibility by introducing a finite-volume-based Fourier neural operator (FVFNO), confirming that the stabilizing effect of the residual guardrail is agnostic to the underlying neural architecture. This study provides a deployable methodology for fast, robust, and automated hybrid simulation in 3D unsteady flow.

Dynamic phase transitions of periodically forced mean-field ferromagnets are often described by a single order parameter and a scalar conjugate field. Building from previous work, we show that, at the critical period $P_c$ of the mean-field Ginzburg-Landau (MFGL) dynamics with energy $F(m)=am^2+bm^4-hm$, the correct conjugate field is the entire even-Fourier component part of the applied field. The correct order parameter is $z_k=\sqrt{\bigl|\,m_k^2-|m_{k,c}|^2\,\bigr|}$, where $m_k$ is the $k^{th}$ Fourier component of the magnetization m(t), and $m_{k,c}$ is the $k^{th}$ Fourier component at the critical period. Using high-accuracy limit-cycle integration and Fourier analysis, we first confirm that, for periodic fields that contain only odd components, the symmetry-broken branch below $P_c$ exhibits $z_k \propto \varepsilon^{1/2}$ (computationally tested for modes $k\le30$), where $\varepsilon=(P_c-P)/P_c$. This provides strong evidence that the 1/2 scaling holds for all Fourier modes. We then find three robust facts: (1) Exactly at $P_c$, adding a small perturbation composed of even Fourier components with an overall field multiplier $h_{mult}$ yields $z_k \propto h_{mult}^{1/3}$ across many $k$. (2) Mode-resolved deviations obey a parity rule: $|\delta m_{2n}| \propto h_{mult}^{1/3}$ and $|\delta m_{2n+1}| \propto h_{mult}^{2/3}$. (3) These scalings persist in two MFGL models with higher-order nonlinearities.

We present two new quantum algorithms for reaction-diffusion equations that employ the truncated Chebyshev polynomial approximation. This method is employed to numerically solve the ordinary differential equation emerging from the linearization of the associated nonlinear differential equation. In the first algorithm, we use the matrix exponentiation method (Patel et al., 2018), while in the second algorithm, we repurpose the quantum spectral method (Childs et al., 2020). Our main technical contribution is to derive the sufficient conditions for the diagonalization of the Carleman embedding matrix, which is indispensable for designing both quantum algorithms. We supplement this with an efficient iterative algorithm to diagonalize the Carleman matrix. Our first algorithm has gate complexity of O(d$\cdot$log(d)+T$\cdot$polylog(T/$\varepsilon$)). Here $d$ is the size of the Carleman matrix, $T$ is the simulation time, and $\varepsilon$ is the approximation error. The second algorithm is polynomial in $log(d)$, $T$, and $log(1/\varepsilon)$ - the gate complexity scales as O(polylog(d)$\cdot$T$\cdot$polylog(T/$\varepsilon$)). In terms of $T$ and $\varepsilon$, this is comparable to the speedup gained by the current best known quantum algorithm for this problem, the truncated Taylor series method (Costa et.al., 2025). Our approach has two shortcomings. First, we have not provided an upper bound, in terms of d, on the condition number of the Carleman matrix. Second, the success of the diagonalization is based on a conjecture that a specific trigonometric equation has no integral solution. However, we provide strategies to mitigate these shortcomings in most practical cases.

We study the noncommutative $U(1)$ gauge theory on the $\kappa$-Minkowski space-time at the semiclassical approximation. We construct exact solutions of the deformed Maxwell equations in vacuum, describing localized signals propagating in a given direction. The propagation velocity appears to be arbitrary. We figure out that the wave packets with different values of the propagation velocity are related by noncommutative gauge transformations. Moreover, we show that spatial distances between particles are gauge-dependent as well. We explain how these two gauge dependencies compensate each other, recovering gauge invariance of measurement results. According to our analysis, the gauge ambiguity of the speed of light can be absorbed into a redefinition of the unit of length and, therefore, cannot be measured experimentally.

Some geophysical observations commonly collect only one component (1C) of a three component (3C) vector field. For example, Distributed Acoustic Sensing (DAS) records seismograms derived from displacement differences along the axis of segments of a fiber optic cable. In practice, multiple observations from such 3C vector fields are available, but commonly along non-orthogonal directions – i.e.\ desirable sets of observations along orthogonal basis vectors are not available. For DAS, the theory of (finite) frames allows the recovery of 3C vector observations as long as the set of measurements occur on axis-vectors that mathematically span 3D space. A reconstruction algorithm from finite frame theory is described and then applied to geometry data from a borehole at the Utah FORGE geothermal project. The results demonstrate recovery of high precision 3C vectors along the fiber optic cable from 1C input vector-projections.

When aiming for atomic resolution electron magnetic circular dichroism (EMCD) in STEM mode, the high convergence angle of the electron probe can lead to unforeseen artefacts and strong reductions of the signal to noise ratio (SNR) in the measurement of magnetic magnitudes. In this work, the EMCD signal is measured in STEM mode at semi-convergence angles ranging from \alpha=\ 2-10\ mrad\ using iron samples with high structural perfection. We observe that the relative EMCD signal remains very similar for all convergence angles, which is in good agreement with simulations. The improvement in the signal-to-noise ratio as compared to earlier works is due to better crystalline quality of the Fe sample, high control of the sample orientation, better control over sample thickness, and factors such as higher beam current and the use of a fast and sensitive hybrid-pixel electron detector. One of the key factors to remove experimental ambivalences is the use of an acquisition geometry where the two conjugate EMCD spectra are acquired simultaneously. Furthermore the high crystalline quality Fe sample enables the acquisition of statistical entities of spectra at the same sample orientation, which in turn results in high SNR EMCD signals. We obtain high SNR EMCD signals for all semi convergence angles and could measure the orbital to spin magnetic moment ratio ml/ms with high precision. We observed that at high convergence angles, the ml/ms ratio gets sensitive to minute changes in sample orientation.

We investigate how peer pressure influences the opinions of Large Language Model (LLM) agents across a spectrum of cognitive commitments by embedding them in social networks where they update opinions based on peer perspectives. Our findings reveal key departures from traditional conformity assumptions. First, agents follow a sigmoid curve: stable at low pressure, shifting sharply at threshold, and saturating at high. Second, conformity thresholds vary by model: Gemini 1.5 Flash requires over 70% peer disagreement to flip, whereas ChatGPT-4o-mini shifts with a dissenting minority. Third, we uncover a fundamental "persuasion asymmetry," where shifting an opinion from affirmative-to-negative requires a different cognitive effort than the reverse. This asymmetry results in a "dual cognitive hierarchy": the stability of cognitive constructs inverts based on the direction of persuasion. For instance, affirmatively-held core values are robust against opposition but easily adopted from a negative stance, a pattern that inverts for other constructs like attitudes. These dynamics echoing complex human biases like negativity bias, prove robust across different topics and discursive frames (moral, economic, sociotropic). This research introduces a novel framework for auditing the emergent socio-cognitive behaviors of multi-agent AI systems, demonstrating their decision-making is governed by a fluid, context-dependent architecture, not a static logic.

Although coupled ion-electron transfer (CIET) has emerged as a powerful framework to rationalize the kinetics of Faradaic reactions, its mechanism has lacked a fully microscopic, first-principles description. Here we show that the coupling of electron-transfer to ion-transfer can be understood as an extension of Marcus theory in which the ensemble of diabatic nuclear configurations is conditioned on a classical collective variable describing the interfacial anisotropy. This formalism enables direct construction of the CIET free-energy surface from constrained ab initio trajectories, providing a first-principles route to electrochemical current-overpotential relations. We demonstrate this method for CO2 redox on a gold electrode and find that the resulting two-dimensional saddle-point barriers differ substantially from one-dimensional treatments that consider only electron- or ion-transfer coordinates individually.

Glass materials play an increasingly important role in advanced technologies due to their superior physical properties. However, precise machining of glass remains a major challenge because of its brittleness and sensitivity to thermal and mechanical stresses. In this study, we present a novel approach that combines spherical-aberration-assisted filamentation with Laser-Induced Deep Etching (LIDE) to achieve unprecedented high-precision micro-hole machining in fused silica substrates. By deliberately introducing spherical aberration into an intense femtosecond laser beam, thin, uniformly elongated, and stable filaments are generated, which effectively suppress unwanted plasma formation and thermal deformation typical of standard filamentation. Using this method, we fabricated micro-holes with diameters as small as 10 $\mu$m across various sizes, maintaining an almost zero taper even in 1 mm-thick samples. The sidewalls exhibited nanoscale smoothness (Ra = 38.1 nm, RMS = 53.9 nm), and the hole area demonstrated excellent repeatability with only \sim 1.0% variation across multiple trials. This simple optical configuration drastically reduces cost compared with existing approaches that rely on specialized components, while moderately satisfying critical requirements for geometrical versatility, minimal damage, precision, and repeatability. This work represents a significant step forward in precision glass machining and lays a foundation for future microstructured electronic, optical, and microfluidic devices.

Tissue dynamics play a crucial role in biological processes ranging from inflammation to morphogenesis. However, these noisy multicellular dynamics are notoriously hard to predict. Here, we introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies. This generative model combines graph neural networks, normalizing flows and WaveNet algorithms to represent tissues as neural stochastic differential equations where cells are edges of an evolving graph. Cell interactions are encoded in a dual signaling graph capable of handling signaling cascades. The dual graph architecture of our neural networks reflects the architecture of the underlying biological tissues, substantially reducing the amount of data needed for training, compared to convolutional or fully-connected neural networks. Taking epithelial tissue experiments as a case study, we show that our model not only captures stochastic cell motion but also predicts the evolution of cell states in their division cycle. Finally, we demonstrate that our method can accurately generate the experimental dynamics of developmental systems, such as the fly wing, and cell signaling processes mediated by stochastic ERK waves, paving the way for its use as a digital twin in bioengineering and clinical contexts.

Machine Learned Interatomic Potentials (MLIPs) offer a powerful combination of abilities for accelerating theoretical spectroscopy calculations utilising both ensemble sampling and trajectory post-processing for inclusion of vibronic effects, which can be very challenging for traditional ab initio MD approaches. We demonstrate a workflow that enables efficient generation of MLIPs for the solvatochromic dye nile red system, in a variety of solvents. We use iterative active learning techniques to make this process as efficient as possible in terms of number and size of Density Functional Theory (DFT) calculations. Additionally, we compare the efficacy of various methodologies: generating distinct MLIPs for each adiabatic state, using one ground state MLIP in combination with delta-ML of excitation energies, and using a three-headed multiheaded ML model. To evaluate the validity of the resulting models, we compare predicted absorption and emission spectra to experimental spectra. We found that the incorporation of larger solvent systems into training data, and the use of delta models to predict the excitation energies, enables the accurate and affordable prediction of UV-Vis spectra with accuracy equivalent to the ground truth method, which is time-dependent DFT in this case.

A development effort to improve the performance of superconducting radio-frequency half-wave resonators (SRF HWRs) is underway at the Facility for Rare Isotope Beams (FRIB), where 220 such resonators are in operation. Our goal was to achieve an intrinsic quality factor (Q0) of >= 2E10 at an accelerating gradient (Ea) of 12 MV/m. FRIB production resonators were prepared with buffered chemical polishing (BCP). First trials on electropolishing (EP) and post-EP low temperature baking (LTB) of FRIB HWRs allowed us to reach higher gradient (15 MV/m, limited by quench) with a higher quality factor at high gradient, but Q0 was still below our goal. Trapped magnetic flux during the Dewar test was found to be a source of Q0 reduction. Three strategies were used to reduce the trapped flux: (i) adding a local magnetic shield (LMGS) to supplement the “global” magnetic shield around the Dewar for reduction of the ambient magnetic field; (ii) performing a “uniform cool-down” (UC) to reduce the thermoelectric currents; and (iii) using a compensation coil to further reduce the ambient field with active field cancellation (AFC). The LMGS improved the Q0, but not enough to reach our goal. With UC and AFC, we exceeded our goal, reaching Q0 = 2.8E10 at Ea = 12 MV/m.

We explore the Hubble tension within an anisotropic cosmological framework by revisiting the Bianchi type-I model introduced in Le Delliou et al. 2020. Motivated by ongoing debates surrounding back-reaction effects and observed anomalies in the cosmic microwave background (CMB), we investigate whether a departure from isotropy in the late Universe could reconcile the observed discrepancies in Hubble constant measurements. Using a Bayesian inference framework, we constrain the model parameters employing multiple nested sampling algorithms: bilby, PyMultiNest, and nessai. We perform the analysis under both uniform and Gaussian priors, allowing us to systematically assess the sensitivity of the inferred cosmological parameters to different prior assumptions. This dual-prior strategy balances agnostic parameter exploration with constraints informed by theory and observation. Our findings demonstrate the reliability of our inference pipeline across different samplers and emphasize the crucial role of prior selection in non-standard cosmological model testing. The results suggest that anisotropic models remain viable contenders in addressing current cosmological tensions: even though the present model does not show alleviation of the Hubble tension, the data points towards anisotropies. Future work may extend this methodology to more complex anisotropic scenarios and incorporate additional cosmological probes such as CMB polarization and gravitational wave standard sirens.

Plasmonic nanoantennas offer new avenues to manipulate the propagation of light in materials due to their near field enhancement and ultrafast response time. Here we investigate the epsilon-near-zero (ENZ) response in an L-shaped nanoantenna structure under the phenomenon of plasmonic analog of enhancement in the index of refraction. Using a quantum mechanical approach, we analyze the modulation in the response of probe field and emergence of ENZ frequency region both in the linear and nonlinear plasmonic system. We also demonstrate the active tuning of ENZ frequency region in a nanoantenna structure by modulating the phase of control pulse. The analytical and 3D FDTD simulation results show a significant spectral shift in the ENZ modes. Our proposed method offers the possibility to design and control optical tunable ENZ response in plasmonic metasurfaces without the use of ENZ material. Such metasurfaces can be used in on-chip photonic integrated circuits, further localization of incident fields, slow light operations and various quantum technologies.

We present a quantum algorithm for simulating rovibrational Hamiltonians on fault-tolerant quantum computers. The method integrates exact curvilinear kinetic energy operators and general-form potential energy surfaces expressed in a hybrid finite-basis/discrete-variable representation. The Hamiltonian is encoded as a unitary quantum circuit using a quantum read-only memory construction based on the Walsh-Hadamard transform, enabling high-accuracy quantum phase estimation of rovibrational energy levels and dynamics simulations. Our technique provides asymptotic reductions in both logical qubit count and T-gate complexity that are exponential in the number of atoms and at least polynomial in the total Hilbert-space size, relative to existing block-encoding techniques based on linear combinations of unitaries and variational basis representation. Compared with classical variational methods, it offers exponential memory savings and polynomial reductions in time complexity. The quantum volume required for computing the rovibrational spectrum of water can be reduced by up to 100 000 times compared with other quantum methods, increasing to at least 1 million for a classically intractable 30-dimensional (12-atom) molecular system. For this case with a six-body coupled potential, estimating spectroscopic-accuracy energy levels would require about three months on a 1 MHz fault-tolerant quantum processor with fewer than 300 logical qubits, versus over 30 000 years on the fastest current classical supercomputer. These estimates are approximate and subject to technological uncertainties, and realizing the asymptotic advantage will require substantial quantum resources and continued algorithmic progress.

All simulation approaches eventually face limits in computational scalability when applied to large spatiotemporal domains. This challenge becomes especially apparent in molecular-level particle simulations, where high spatial and temporal resolution leads to rapidly increasing computational demands. To overcome these limitations, hybrid methods that combine simulations with different levels of resolution offer a promising solution. In this context, we present a machine learning-based decision model that dynamically selects between simulation methods at runtime. The model is built around a Multilayer perceptron (MLP) that predicts the expected discrepancy between particle and continuum simulation results, enabling the localized use of high-fidelity particle simulations only where they are expected to add value. This concurrent approach is applied to the simulation of membrane fabrication processes, where a particle simulation is coupled with a continuum model. This article describes the architecture of the decision model and its integration into the simulation workflow, enabling efficient, scalable, and adaptive multiscale simulations.

Local intensity enhancement by plasmonic nanoparticles is widely used in optics and photonics. However, the effect is usually based on dipole resonances in the particles. Recently, it has been shown that quadrupole and octupole resonances can exhibit comparable, or even higher near-field enhancement. In this work, we focus on the near-field enhancement by a metasurface composed of gold-disc dimers arranged in a rectangular array. We find that, owing to an octupole plasmon resonance coupled to a surface lattice resonance, exceptionally high near-field enhancement in the dimer gaps can be achieved in the visible spectral range. To gain insight into the effect, we develop an analytical model for the effective dipole and octupole polarizabilities of the particles in an array, and discover, that at decreasing array periods, the dipole polarizability tends to vanish, while the octupole polarizability rapidly increases. Hence, octupole resonances can find applications in high-density arrays of plasmonic resonators. We propose a method to numerically evaluate multipole polarizabilities of a single particle, applying it to the gold dimer that we consider. The influence of the array on the effective polarizabilities is then verified by numerical calculations and a good agreement is obtained. Our results may open new avenues for investigating the properties of periodic plasmonic structures based on higher-order multipole resonances and their applications.

Intense light-matter interaction largely relies on the use of high-power light sources, creating fields comparable to, or even stronger than, the field keeping the electrons bound in atoms. Under such conditions, the interaction induces highly nonlinear processes such as high harmonic generation, in which the low-frequency photons of a driving laser field are upconverted into higher-frequency photons. These processes have enabled numerous groundbreaking advances in atomic, molecular, and optical physics, and they form the foundation of attosecond science. Until recently, however, such processes were typically described using semi-classical approximations, since the quantum properties of the light field were not required to explain the observables. This has changed in the recent past. Ongoing theoretical and experimental advances show that fully quantized descriptions of intense light-matter interactions, which explicitly incorporate the quantum nature of the light field, open new avenues for both fundamental research and technological applications at the fully quantized level. These advances emerge from the convergence of quantum optics with strong-field physics and ultrafast science. Together, they have given rise to the field of quantum optics and quantum electrodynamics of strong-field processes.

Modern mechanical sensors increasingly measure motion with precision sufficient to resolve the fundamental thermal noise floor over a broad band. Compared to traditional sensors – achieving this limit only near resonance – this capability provides massive gains in acquisition rates along with access to otherwise obscured transient signals. However, these stronger measurements of motion are naturally accompanied by increased back-action. Here we show how resolving the broadband thermal noise spectrum reveals back-action-induced correlations in the noise from many mechanical modes, even those well-separated in frequency. As a result, the observed spectra can deviate significantly from predictions of the usual single-mode and (uncorrelated) multimode models over the broad band, notably even at the mechanical resonance peaks. This highlights that these effects must be considered in all systems exhibiting measurement back-action, regardless of whether the resonances are spectrally isolated or the readout noise is high enough that the noise peaks appear consistent with simpler models. Additionally, these correlations advantageously allow the thermal noise spectrum to reach a minimum – equivalent to that of a single mode – in a band far from the resonance peak, where the mechanical susceptibility is comparatively stable against frequency noise.

Solar flares, email exchanges, and many natural or social systems exhibit bursty dynamics, with periods of intense activity separated by long inactivity. These patterns often follow power- law distributions in inter-event intervals or event rates. Existing models typically capture only one of these features and rely on non-local memory, which complicates analysis and mechanistic interpretation. We introduce a novel self-reinforcing point process whose event rates are governed by local, Markovian nonlinear dynamics and post-event resets. The model generates power-law tails for both inter-event intervals and event rates over a broad range of exponents observed empirically across natural and human phenomena. Compared to non-local models such as Hawkes processes, our approach is mechanistically simpler, highly analytically tractable, and also easier to simulate. We provide methods for model fitting and validation, establishing this framework as a versatile foundation for the study of bursty phenomena.

The present research is a theoretical study about the transient friction created in circular pipe mean flow, whenever an incompressible Newtonian fluid is accelerated through a monotonously-increased mean-pressure gradient. The resulting friction stress is the sum of two components, one laminar and the other purely turbulent, not synchronised between them. Each component is analysed separately, in a series of theoretical experiments that explore various possibilities, depending on the degree of asynchrony between them. It is found that in some cases the transient friction is higher than in equal-Re steady-sate flow, but in some others it is noticeably lower. This work provides an analytical explanation for most of the important and interesting phenomena reported in the literature. To do so, it takes advantage of the Theory of Underlying Laminar Flow (TULF), already introduced in previous works of same authors. The TULF predicts quite approximately what is observed in experiments, including the transient skin-friction coefficient and the presence of mean-velocity overshoots. Additionally, the role of the time constant in turbulent mean flow is examined and related to the turbulence's frozen time. Finally, a study of the logarithmic layer evolution in the transient flow is accomplished, which results destroyed during the increase of turbulence occurring along the transient. In summary, the present work unveils new knowledge about transient friction in unsteady flows.

Designing and optimising the geometry of industrial process equipment remains slow and still largely ad hoc: engineers make small tweaks to one standard shape at a time, build prototypes, and hope for gains. We introduce HARPPP, an autonomous design loop that couples a compact, programmable geometry model to power-controlled CFD and evolutionary search. The geometry model is a single mathematical description that reproduces every standard impeller as a special case while spanning an unlimited set of manufacturable shapes. Working with Johnson Matthey on an industrial vessel, HARPPP explored a 23-parameter impeller-baffle space at constant power (3024 W), executing 3,000 simulation cycles in 15 days. The search uncovered multiple design families that outperform a Rushton/4-baffle baseline in both mixing intensity and uniformity, including twisted-plate impellers and pitched/curved baffles (intensity +18 to +78 percent; uniformity CoV -16 to -64 percent). A clear intensity-uniformity Pareto frontier emerged, enabling application-specific choices. Because HARPPP treats the simulator as the objective, it generalises to other equipment wherever credible physics models exist.

Machine learning has the potential to improve the reconstruction of the dark matter profile of galaxies with respect to traditional methods, like rotation curves. We demonstrate on the simulation suite Illustris-TNG that a steerable equivariant convolutional neural network (CNN) is able to infer the dark matter profiles within and around individual galaxies from photometric and interferometric data, improving on a standard CNN. Within the in silico environment of the simulations, our architecture is able to capture the dark matter distribution within galaxies without a parametrization of the profile. We perform an interpretability analysis to understand the internal mechanisms of the trained model and the most important data features used to estimate the dark matter profiles. The equivariant CNN recovers the dark matter profile of galaxies within the stellar mass range $[10^{10} - 10^{12} ]$ $M_{\odot}$ with excellent precision and accuracy: the mean squared error is reduced by a factor of ~ 3 from its value under the training distribution, demonstrating that the network has learnt from the data features. While this holds within the controlled 'in silico' environment of the simulation, we argue that few additional steps are needed before this method can be reliably applied to galaxies in the real field observations.

Collective action against algorithmic systems provides an opportunity for a small group of individuals to strategically manipulate their data to get specific outcomes, from classification to recommendation models. This effectiveness will invite more growth of this type of coordinated actions, both in the size and the number of distinct collectives. With a small group, however, coordination is key. Currently, there is no formal analysis of how coordination challenges within a collective can impact downstream outcomes, or how multiple collectives may affect each other's success. In this work, we aim to provide guarantees on the success of collective action in the presence of both coordination noise and multiple groups. Our insight is that data generated by either multiple collectives or by coordination noise can be viewed as originating from multiple data distributions. Using this framing, we derive bounds on the success of collective action. We conduct experiments to study the effects of noise on collective action. We find that sufficiently high levels of noise can reduce the success of collective action. In certain scenarios, large noise can sink a collective success rate from $100\%$ to just under $60\%$. We identify potential trade-offs between collective size and coordination noise; for example, a collective that is twice as big but with four times more noise experiencing worse outcomes than the smaller, more coordinated one. This work highlights the importance of understanding nuanced dynamics of strategic behavior in algorithmic systems.

Formation and dynamics of Taylor bubble in power-law liquids flowing through a circular co-flow microchannel are numerically investigated using coupled level set and volume-of-fluid method. Aqueous solutions of polyacrylamide (PAAm) are used as power-law liquids. Influences of PAAm concentration, gas-liquid velocities, and surface tension on bubble characteristics are explored. Various mechanism of bubble breakup are observed in different concentration of PAAm. Based on the bubble length with respect to the channel diameter, two different flow regimes are identified. Flow pattern maps are constructed based on inlet velocities, and scaling laws are proposed to estimate the bubble length.

In the present study, Taylor bubble formation in two-phase gas-non-Newtonian Carreau liquid flowing through a confined co-flow microchannel is investigated. Systematic analysis are carried out to explore the influences of rheological properties, inlet velocities, and surface tension on Taylor bubble length, shape, velocity and liquid film thickness. Aqueous solutions of carboxymethyl cellulose (CMC) with different mass concentrations are considered as the non-Newtonian liquids to understand the fundamentals of flow behaviour. With increasing solution viscosity and liquid phase inlet velocity, Taylor bubble formation frequency and velocity increased, however, the bubble length was found to decrease. Velocity profiles inside the Taylor bubble and liquid slug were analyzed, and distinct velocity distributions were found for different CMC concentrations. Flow pattern maps are constructed based on inlet velocities for Carreau liquids in co-flow microchannel. This study essentially provides useful guidelines in designing non-Newtonian microfluidic system for precise control and manipulation of Taylor bubbles.