Papers

We present two-dimensional (2D) particle-in-cell simulations of a magnetized, collisionless, relativistic pair plasma subjected to combined velocity and magnetic-field shear, a scenario typical at intermittent structures in plasma turbulence. We create conditions where only the Kelvin-Helmholtz (KH) and Drift-Kink (DK) instabilities can develop, while tearing modes are forbidden. The interaction of DKI and KHI generates qualitatively new structures, marked by a thickened shear layer with very weak electromagnetic field, modulated by KH vortices. Over a range of moderately strong velocity shears explored, the interaction of DKI and KHI results in a significant enhancement of dissipation over cases with only velocity shear or only magnetic shear. Moreover, we observe a new and efficient way of particle acceleration where particles are stochastically accelerated by the motional electric field exterior to the shear layer as they meander in an S-shaped pattern in and out of it. This process takes advantage of the bent geometry of the shear layer caused by the DK-KHI interaction and is responsible for most of the highest-energy particles produced in our simulations. These results further our understanding of dissipation and particle acceleration at intermittent structures, which are present in plasma turbulence across a wide range of astrophysical contexts such as in AGN jet sheaths, potentially relevant to limb-brightened emission, etc., and highlight the sensitivity of dissipation to multiple interacting instabilities, thus providing a strong motivation for further studies of their nonlinear interaction at the kinetic level.

Inhomogeneous mixing and the consequent mesoscopic layered structure have been observed in many physical systems, including magnetically confined fusion plasmas. Especially, in plasmas, mixing can be enhanced through turbulence spreading by intermittent coherent structures (blobs/voids), or suppressed due to the formation of transport barriers (sheared zonal flows). Interestingly, blobs/voids and zonal flows are not independent, and they can co-exist in a state of inhomogeneous mixing, often called the E x B staircase. In this paper, we first introduce recent experimental progress on the physics of blobs/voids: how turbulence spreading by blobs/voids occurs, the consequences of enhanced turbulence spreading for the scrape-off layer (SOL) power decay length, and the interaction between blobs/voids and zonal flows. Then, we provide a brief review of experimental results on staircases, or more generally layered mesoscopic transport barriers. Staircases are often elusive in experiments, requiring integrating multi-diagnostic data, utilizing high-dimensional diagnostics, or extracting hidden information from signals for their identification. Our understanding is still incomplete. This paper serves as an initial step toward applying insights gained from inhomogeneous mixing due to blobs/voids to the understanding of a staircase.

Financial networks based on Pearson correlations have been intensively studied. However, previous studies may have led to misleading and catastrophic results because of several critical shortcomings of the Pearson correlation. The local Gaussian correlation coefficient, a new measurement of statistical dependence between variables, has unique advantages including capturing local nonlinear dependence and handling heavy-tailed distributions. This study constructs financial networks using the local Gaussian correlation coefficients between tail regions of stock returns in the Shanghai Stock Exchange. The work systematically analyzes fundamental network metrics including node centrality, average shortest path length, and entropy. Compared with the local Gaussian correlation network among positive tails and the conventional Pearson correlation network, the properties of the local Gaussian correlation network among negative tails are more sensitive to the stock market risks. This finding suggests researchers should prioritize the local Gaussian correlation network among negative tails. Future work should reevaluate existing findings using the local Gaussian correlation method.

Airway clearance by coughing is a key mechanism for mucus transport, particularly in obstructive lung diseases associated with altered mucus rheology. We investigate the dynamics of a viscoplastic liquid film driven by flow in a turbulent air layer, which is a model for air-driven mucus transport that incorporates yield-stress effects. Our theoretical analysis is based on a long-wave model for the liquid film flow, and we complement this with experiments, in which layers of Newtonian and yield-stress liquids are exposed to air flow in a rectangular duct. We demonstrate how perturbations to the layer depth can lead to localised yielding and wave generation. Rapid wave growth occurs when the fluid ahead of the oncoming wave is unyielded, so that as the wave propagates, it consumes this static fluid while depositing a much thinner film behind. This mechanism causes dramatic "blow-out" events in experiments, where liquid hits the roof of the tank. By contrast, in Newtonian thin films, multiple surface waves typically form, and blow-out only occurs in experiments when a Newtonian film is sufficiently thick.

We demonstrate low-noise Kerr soliton frequency combs with repetition rates below 1~GHz in ultrahigh-Q crystalline magnesium fluoride resonators. Single soliton states with repetition rates of 0.90 GHz, 1.19 GHz, 1.59 GHz, 2.48 GHz, and 4.10 GHz are observed with continuous-wave laser excitation. The near-GHz soliton repetition frequency exhibits a single-sideband phase noise of -137 dBc/Hz at a 100 kHz offset, surpassing state-of-the-art microwave generators. These “centi-combs” bridge the gap between conventional mode-locked lasers and microresonator frequency combs, providing a new route towards real-time sampling, optical-to-microwave synchronization, and hybrid optical clock networks in a compact form. This work expands the operational range of Kerr soliton microcombs from the terahertz to the sub-gigahertz domain, opening new frontiers for frequency comb technologies.

Photon echo (PE) techniques offer a promising approach to optical quantum memory, yet their implementation in conventional platforms, such as rare-earth-ion-doped crystals, is hindered by limited bandwidths. Semiconductor quantum dot (QD) ensembles, featuring THz-scale inhomogeneous broadening and sub-picosecond dynamics, provide an attractive alternative for ultrafast applications. However, achieving coherent control across such broad spectral ranges remains challenging due to detuning and spatial field inhomogeneities, which reduce PE efficiency. In this work, we experimentally demonstrated adiabatic rapid passage (ARP)-enhanced PE in dense, self-assembled InAs QD ensembles exhibiting THz-scale inhomogeneous broadening and operating at telecom wavelengths, achieving a 3.2-fold increase in echo efficiency. Chirped control pulses designed to satisfy adiabatic conditions across the ensemble enable broadband rephasing. Numerical simulations based on a two-level model reproduce the key experimental observations, including the ARP-induced enhancement, thereby validating the underlying physical picture. These results establish ARP as a robust and scalable approach for coherent control in InAs QD ensembles, with potential applications for ultrafast and broadband optical communication in the THz spectral region.

The field of weather and climate science is at a pivotal moment, defined by simultaneous forces of institutional disruption and unprecedented technological advancements. While a shifting research and employment landscape has created career uncertainty, prompting many scientists to consider or pursue opportunities in the private sector, it has simultaneously spurred an expansion of the ecosystem through the emergence of new computational tools and the growing role of industry innovators and stakeholders. This perspective paper argues that this new, expanded ecosystem presents extraordinary opportunities for students and early-career professionals. We outline the emerging scientific frontiers powered by high-resolution simulations and artificial intelligence, suggest a practical path for navigating a more fluid career landscape, and propose how education and training must evolve. We argue that these changes expand rather than diminish the reader's capacity to do what drew most of us to this field: helping people prepare for what the atmosphere is about to do.

The coherent Ising machine (CIM) is a nonconventional hardware architecture for finding approximate solutions to large-scale combinatorial optimization problems. It operates by annealing a laser gain parameter to adiabatically deform a high-dimensional energy landscape over a set of soft spins, going from a simple convex landscape to the more complex optimization landscape of interest. We address how the evolving energy landscapes guides the optimization dynamics against problems with hidden planted solutions. We study the Sherrington-Kirkpatrick spin-glass with ferromagnetic couplings that favor a hidden configuration by combining the replica method, random matrix theory, the Kac-Rice method and dynamical mean field theory. We characterize energy, number, location, and Hessian eigenspectra of global minima, local minima, and critical points as the landscape evolves. We find that low energy global minima develop soft-modes which the optimization dynamics can exploit to descend the energy landscape. Even when these global minima are aligned to the hidden configuration, there can be exponentially many higher energy local minima that are all unaligned with the hidden solution. Nevertheless, the annealed optimization dynamics can evade this cloud of unaligned high energy local minima and descend near to aligned lower energy global minima. Eventually, as the landscape is further annealed, these global minima become rigid, terminating any further optimization gains from annealing. We further consider a second optimization problem, the Wishart planted ensemble, which contains a hidden planted solution in a landscape with tunable ruggedness. We describe CIM phase transitions between recoverability and non-recoverability of the hidden solution. Overall, we find intriguing relations between high-dimensional geometry and dynamics in analog machines for combinatorial optimization.

We address the Kramers escape problem for Brownian particles in bistable substrates with deformable double-well shapes. The shape deformability is considered of three distinct forms: in one, the positions of the two degenerate minima can be shifted continuously without affecting the barrier height. In the second the minima positions are kept fix while the barrier height is continuously shifted. In the third the minima positions and barrier height can be tuned simultaneously by changing a single real parameter that we refer to as deformability parameter. We obtain the analytical expression of the Kramers escape rate for the three different double-well models, and identify a critical value of the deformability parameter above which non-Gaussian corrections become relevant. Remarkable enough in the latter region, statistical mechanics predicts a first-order transition in quantum tunneling using the functional action associated with the family of deformable double-well potentials.

Spiral wave solutions are found in linear and weakly nonlinear irrotational water wave equations. These unsteady spiral waves evolve from suitable initial conditions; they are not induced by external forcing. In the linear case, a long-time asymptotic result is obtained via the method of stationary phase. The asymptotic approximation is found to be in good agreement with the exact solution and reveals hyperbolic spiral structure. Numerical simulations show that these spiral waves persist in the presence of weak nonlinearity. While spiral solutions are frequently found in excitable media governed by reaction-diffusion systems, they comprise a new class of interesting two space one time dimensional solutions in fundamental linear and nonlinear dispersive wave systems.

We present a scalable machine learning framework for analyzing Parker Solar Probe (PSP) solar wind data using distributed processing and the quantum-inspired Kernel Density Matrices (KDM) method. The PSP dataset (2018–2024) exceeds 150 GB, challenging conventional analysis approaches. Our framework leverages Dask for large-scale statistical computations and KDM to estimate univariate and bivariate distributions of key solar wind parameters, including solar wind speed, proton density, and proton thermal speed, as well as anomaly thresholds for each parameter. We reveal characteristic trends in the inner heliosphere, including increasing solar wind speed with distance from the Sun, decreasing proton density, and the inverse relationship between speed and density. Solar wind structures play a critical role in enhancing and mediating extreme space weather phenomena and can trigger geomagnetic storms; our analyses provide quantitative insights into these processes. This approach offers a tractable, interpretable, and distributed methodology for exploring complex physical datasets and facilitates reproducible analysis of large-scale in situ measurements. Processed data products and analysis tools are made publicly available to advance future studies of solar wind dynamics and space weather forecasting. The code and configuration files used in this study are publicly available to support reproducibility.

Discontinuity capturing (DC) operators are commonly employed to numerically solve problems involving sharp gradients in the solution. Despite their success, the application of DC operators to the direct van der Waals simulation (DVS) remains challenging. The DVS framework models non-equilibrium phase transitions by admitting interfacial regions in which the derivative of pressure with respect to density is negative. In these regions, we demonstrate that classical DC operators may violate the free energy dissipation law and produce unphysical wave structures. To address this limitation, we propose the phase-field/discontinuity capturing (PF/DC) operator. Numerical results show that PF/DC yields stable and accurate solutions in both bulk fluids and interfacial regions. Finally, we apply the proposed method to simulate cavitating flow over a three-dimensional bluff body, obtaining excellent agreement with experimental data and significant improvements over results produced using classical DC operators.

We describe the design, construction, and characterization of a Bitter-type electromagnet that produces a spatially-dependent magnetic field used for Zeeman slowing in cold-atom experiments. The coil consists of stacked copper arcs separated by PTFE spacers of varying thicknesses, generating a near-optimal field profile using a single power supply. With an electrical resistance of $26.5(3)$~m$\Omega$ and self-inductance of $19.1(1)$~$\mu$H, our design achieves a fast electrical switching time of $\tau \approx 100$~$\mu$s in a compact, 30-cm-long package. Water circulating helically through holes in the copper and channels in the spacers ensures efficient thermal management, limiting the temperature rise to $\sim 5^\circ$~C over $36$~s of continuous operation at $200$~A.

The chiral-induced spin selectivity (CISS) effect, whereby chiral molecules preferentially transmit electrons of one spin orientation, remains one of the most intriguing and debated phenomena at the interface of spintronics, molecular electronics, and quantum materials. Despite extensive experimental observations across diverse platforms - including transport junctions, photoemission, and enantioselective chemistry - a comprehensive theoretical framework is still lacking. In this perspective, we critically examine the spinterface mechanism as a unifying explanation for the CISS effect. The spinterface model, which hypothesizes a feedback interaction between electron motion in chiral molecules and fluctuating surface magnetic moments, is shown to quantitatively reproduce experimental data across various systems and conditions. We contrast it with some existing theoretical models, highlighting key experimental features. Importantly, we also address open questions and criticisms of this model, including the nature of surface magnetism, the role of dissipation, and the applicability of the mechanism to non-helical or electrode-free systems. By offering falsifiable predictions and reconciling theory with experimental raw data, this work aims to sharpen the dialogue surrounding the microscopic origin of CISS and stimulate further experimental and theoretical progress.

The discretization of velocity space plays a crucial role in the accuracy and efficiency of multiscale Boltzmann solvers. Conventional velocity space discretization methods suffer from uneven node distribution and mismatch issues, limiting the performance of numerical simulations. To address this, a Gaussian quadrature scheme with a parameterized weight function is proposed, combined with a polar coordinate transformation for flexible discretization of velocity space. This method effectively mitigates node mismatch problems encountered in traditional approaches. Numerical results demonstrate that the proposed scheme significantly improves accuracy while reducing computational cost. Under highly rarefied conditions, the proposed method achieves a speed-up of up to 50 times compared to the conventional Newton-Cotes quadrature, offering an efficient tool with broad applicability for numerical simulations of rarefied and multiscale gas flows.

A generalised concept of the signal-to-noise ratio (or equivalently the ratio of predictable components, or RPC) is provided, based on proper scoring rules. This definition is the natural generalisation of the classical RPC, yet it allows one to define and analyse the signal-to-noise properties of any type of forecast that is amenable to scoring, thus drastically widening the applicability of these concepts. The methodology is illustrated through numerical examples of ensemble forecasts, scored using the continuous ranked probability score (CRPS), and of probability forecasts of a binary event, scored using the logarithmic score. Numerical examples are carried out using both synthetic data with prescribed signal-to-noise ratios as well as seasonal ensemble hindcasts of the North Atlantic Oscillation (NAO) index. The latter have previously been interpreted as having a signal-to-noise "paradox", or anomalous signal-to-noise ratio, using the RPC statistic. For the synthetic data, the RPC statistic as well as the scoring rule-based ones agree regarding which data sets exhibit anomalous signal-to-noise ratios, but exhibit different variance, indicating different statistical properties. For the NAO data, on the other hand, the different statistics are more equivocal on whether the signal-to-noise ratio is anomalous.

In the field of machine learning coarse-grained potentials in molecular dynamics, many propagators require that the effective Hamiltonian is quadratic in momentum, thus limiting the family of coarse-graining functions. In this paper, we derive a general family of coarse-graining embedding functions for which Langevin dynamics samples correctly. These equations have significant implications for molecular simulations and pave the way for Langevin dynamics on non-geometric coarse-graining representations, such as those provided by principal components of component analysis or latent embeddings of molecules obtained from neural networks.

Conceptual and theoretical models describing the dynamics of the atmosphere often assume a hierarchy of dynamic regimes, each operating over some limited range of spatial scales. The largest scales are presumed to be governed by quasi-two-dimensional geostrophic turbulence, mesoscale dynamics by gravity waves, and the smallest scales by 3D isotropic turbulence. In theory, this hierarchy should be observable as clear scale breaks in turbulent kinetic energy spectra as one physical mechanism transitions to the next. Here, we show that this view is not supported by global dropsonde and radiosonde datasets of horizontal winds. Instead, the structure function for horizontal wind calculated for vertical separations between 200 m and 8 km has a Hurst exponent of $H_v \approx 0.6$, which is inconsistent with either gravity waves ($H_v = 1$) or 3D turbulence ($H_v = 1/3$). For horizontal separations between 200 km and 1800 km, the Hurst exponent is $H_h \approx 0.4$, which is inconsistent with quasi-geostrophic dynamics ($H_h = 1$). We argue that sonde observations are most consistent with a lesser known "Lovejoy-Schertzer" model for stratified turbulence where, at all scales, the dynamics of the atmosphere obey a single anisotropic turbulent cascade with $H_v=3/5$ and $H_h =1/3$. While separation scales smaller than 200 m are not explored here due to measurement limitations, the analysis nonetheless supports a single cohesive theoretical framework for describing atmospheric dynamics, one that might substitute for the more traditional hierarchy of mechanisms that depends on spatial scale.

Africa holds the world's highest solar irradiance yet has <2% of global photovoltaic (PV) capacity, leaving 600 million people without electricity access. However, climate change impacts on its 10 TW potential remain understudied. Using four decades of ERA5 reanalysis data (1980-2020) at 0.25 degree resolution, we quantify the contributions of key climate factors to historical changes in African PV potential through multivariate decomposition. Continental PV potential increased by 3.2%, driven primarily by enhanced solar radiation (+1.2 degree Celsius, contributing -23%). East Africa gained >6% from radiation enhancement, while North Africa declined by 0.5% as extreme heat (+2 degree Celsius) overwhelmed radiation benefits. Critically, stability analysis using the coefficient of variation (CV) reveals that high-irradiance subtropical zones are highly variable (CV=0.4), in contrast to stable equatorial regions (CV=0.1), challenging the assumption that resource abundance ensures reliability. These findings reframe Africa's solar strategy: North Africa requires prioritizing heat-resilient technology over capacity maximization; subtropical zones demand grid-storage co-investment; and East Africa presents globally competitive opportunities for rapid, stable deployment. By resolving spatiotemporal heterogeneities and quantifying climate-driver contributions, our analysis provides an actionable framework for climate-resilient solar deployment, critical for Africa's energy transition and climate mitigation.

Mechanical energy is lost to friction during a shot with a trebuchet. The losses are mainly due to sliding friction at the bearings for the throwing arm and at the hinge for the swinging counterweight, but the aerodynamic force on the sling also contributes. Generalized forces for these sliding and aerodynamic frictions are derived and included in the equations for the internal movement of the engine. The equations are solved by the use of perturbation theory and calculated losses are compared with results from an experimental engine of small dimensions. Scaling to full-size trebuchets is discussed.

In this article, we have developed a computational model to determine the droplet formation regime and its transition in a square microfluidic flow-focusing device that eventually dictate the droplet shape, size, and its formation frequency. We have methodically explored the influences of various physicochemical parameters on the droplet dynamics and flow regime transition, which are essential in the development of new methods for on-demand droplet generation. On the basis of the droplet formation mechanism, we have formulated flow maps for different liquid-liquid systems, and have also proposed a scaling law to predict the droplet length for a wide range of operating condition resulting from the variation of flow rates, and viscosities of the continuous phase as well as the interfacial tension. This work can effectively contribute in providing helpful guidelines on the design and operations of droplet-based flow-focusing microfluidic systems.

Predictive dosimetry is central to enabling personalized radiopharmaceutical therapy (RPT), particularly in prostate specific membrane antigen (PSMA) targeted theranostics. In this work, we develop a three layer computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling with machine learning (ML) to predict both physical (AUC, absorbed dose) and biological (BED, EQD2) dosimetric endpoints in tumors and major organs. In the first layer, we generated 640 virtual patients using PBPK simulations of F-18, Ga-68, and Cu-64 labeled PSMA PET tracers paired with Lu-177 PSMA therapy, producing 15360 tumor and organ time activity curves (TACs) under realistic biological variability and PET-like noise. In the second layer, TACs were transformed into quantitative kinetic features and mapped to physical and biological dose metrics. In the third layer, ML models (Random Forest, Extra Trees, Ridge, Gradient Boosting, and XGBoost) were trained to predict RPT doses from PET derived features, with performance evaluated using mean absolute percentage error (MAPE) and R2. Cu-64 PSMA-617 based PET yielded the most robust predictions, achieving tumor dose MAPE as low as 8 percent and 10 to 20 percent for normal organs, while F-18 DCFPyL showed volume dependent performance and Ga-68 PSMA-11 exhibited higher variability. SHAP analysis revealed that peak uptake, clearance, and early kinetic features dominated predictive performance across organs and endpoints. This PBPK ML framework enables scalable, physiology informed predictive dosimetry and provides a foundation for trial design and patient specific treatment planning in PSMA targeted RPT. These results demonstrate that pre therapy PET can serve as a reliable surrogate for post therapy dosimetry, enabling scalable personalization of PSMA targeted RPT.

PIP-II is a superconducting linac that is in the initial acceleration chain for the Fermilab accelerator complex. The RF system consists of a warm front-end with an RFQ and buncher cavities along with 25 superconducting cryo-modules comprised of cavities with five different acceleration \(\beta\). The LLRF system for the linac has to provide field and resonance control for a total of 125 RF cavities. Various components of the LLRF system have been tested with and without beam at the PIP-II test stands. The LLRF system design is derived from the LCLS-II project with its self-excited loop architecture used in the majority of the cryo-modules. The PIP-II beam loading at 2 mA is much higher than the LCLS-II linac. The control system architecture is analyzed and evaluated for the operational limits of feedback gains and their ability to meet the project regulation requirements for cavity field amplitude and phase regulation.

We experimentally investigate the rheology of dark chocolate pastes in both industrially relevant pre-refined form and simplified model systems. Steady and oscillatory shear experiments reveal yielding, pronounced shear-thinning, and stress-dependent hysteresis governed by solid loading. Fitting the viscosity data with the Maron-Pierce model provides stress-dependent maximum flowable fractions $\phi_{\rm{m}}(\sigma)$, defining yield loci in the $(\phi, \sigma)$ plane. Their variation with stress quantifies the coupled roles of friction and adhesion in setting flow limits. Large-amplitude oscillatory shear tests characterize transitions from elastic to viscous behavior and identify distinct recovery pathways near jamming. Contact-stress decomposition separates hydrodynamic and frictional contributions, confirming that adhesive contact networks dominate stress transmission in pre-refined pastes. These results establish chocolate pastes as dense, adhesive suspensions whose flow is controlled by the interplay of friction and adhesion, offering quantitative benchmarks for constitutive modeling and linking chocolate processing to the broader physics of constraint rheology.

Accurate long-term forecasting of spatiotemporal dynamics remains a fundamental challenge across scientific and engineering domains. Existing machine learning methods often neglect governing physical laws and fail to quantify inherent uncertainties in spatiotemporal predictions. To address these challenges, we introduce a physics-consistent neural operator (PCNO) that enforces physical constraints by projecting surrogate model outputs onto function spaces satisfying predefined laws. A physics-consistent projection layer within PCNO efficiently computes mass and momentum conservation in Fourier space. Building upon deterministic predictions, we further propose a diffusion model-enhanced PCNO (DiffPCNO), which leverages a consistency model to quantify and mitigate uncertainties, thereby improving the accuracy and reliability of forecasts. PCNO and DiffPCNO achieve high-fidelity spatiotemporal predictions while preserving physical consistency and uncertainty across diverse systems and spatial resolutions, ranging from turbulent flow modeling to real-world flood/atmospheric forecasting. Our two-stage framework provides a robust and versatile approach for accurate, physically grounded, and uncertainty-aware spatiotemporal forecasting.

Quantum theory implies, and empirical evidence confirms, that while particles $\textit{can}$ exhibit wave-like behavior in interferometric experiments, this behavior is so limited as $\textit{not}$ to allow for third- and higher-order interference. The article at hand shows that this possibility-impossibility structure suggests the universal validity of a principle that regulates statistical correlations between spatiotemporally localized events, $\textit{independently}$ of the nature of the objects that may or may not partake in these events. Roughly, the said principle mandates that $\textit{any}$ joint influence of $m$ mutually spacelike separated events on $\textit{another}$ event, be such, that it can be separated by $\textit{at least}$ $\lceil \frac{m}{2} \rceil$ mediating events, and in some cases, by $\textit{no more}$ than $\lceil \frac{m}{2} \rceil$ mediating events. The structure of quantum interference thus teaches us that events can influence each other in a non-separable fashion, but that this non-separability has a certain exactly quantifiable limit.

From the Earth's atmosphere and oceans to stellar radiation zones, inertia-gravity waves, which are called gravito-inertial waves (hereafter GIWs) in Astrophysics, are transporting momentum and mixing matter when they are damped through heat and viscous diffusions and when they break. Their short-time scale dynamics is governed by the buoyancy force and the Coriolis acceleration. Because of the transport they trigger, they modify the long-term evolution of the large-scale planetary atmospheric (oceanic) circulation and of the structure and rotation of stars. In many state-of-the-art models, the so-called Traditional Approximation of Rotation (hereafter denoted TAR), where the local projection of the rotation vector along the horizontal direction is neglected, is assumed. We aim to identify the applicability regime of this approximation and to propose a non-traditional parametrisation of wave - zonal flow interactions, in which the full Coriolis acceleration is taken into account. We build a prototype local non-traditional Cartesian model in which we take into account the full Coriolis acceleration, buoyancy, and heat and viscous diffusions. On the one hand, the TAR is strongly underestimating GIWs damping in the sub-inertial regime. In this regime a non-traditional modelling must be adopted to predict the correct altitude where momentum is deposited, which is closer to the excitation region of waves than the one predicted using the TAR. On the other hand, non-traditional modellings of GIWs convective and shear-induced breakings are proposed. Taking into account the full Coriolis acceleration leads to a stronger inhibition of the efficiency of the convective and shear-induced overturnings and to a weaker transport than those predicted when assuming the TAR. Finally, a fully non-traditional parametrisation of GIWs - mean zonal flows interaction is derived.

The movement of the infective form of the T. Cruzi parasite within the human blood is not completely understood. Video microscopy observations confirm forward motility of the protozoa and relate it to the deformation of the body, nonetheless there are open questions relating the deformation of the protozoan with its motion in blood, for which a computational model would be very helpful. Hereby we introduce a simple computational 2D model to test whether it is mechanistically possible for the parasite to direct its movement through the blood stream towards preferred organs in the bloodstream. We model the infective form of the T. cruzi, the causative agent of Chagas disease; by means of a network of harmonic springs that represents its body, without explicitly modeling the flagellum. We coupled this with a particle model of a laminar fluid flow, which we implemented using the Dissipative Particles Dynamics method. Our parasite model swims on still fluid, its center of mass displacement being larger than the end to end deformation. Laminar flow dragged the model parasite of the order of 5 $\mu m$; having minor effects on its structure. The parasite model does not exhibit directed motion in a moving fluid, suggesting that the large-scale displacement of the real parasite is not attributable to its own motility but is instead caused by it being dragged by the flow. We highlight two potential improvements for the model: incorporating the capacity to describe 3D motion and including a specific depiction of the flagellum.

The two-tier Lowry model brings dynamic simulations of population and employment directly into the planning process. By linking regional modelling with neighbourhood design, the framework enables planners to explore how alternative planning scenarios may evolve over time. The upper tier captures regional flows of people, jobs, and services, while the lower tier allocates these to fine-grain zones such as neighbourhoods or parcels. Implemented in CityEngine, the approach allows interactive visualisation and evaluation of multi-scale scenarios. A case study in South Yorkshire (UK) illustrates how regional forecasts can be translated into local design responses, connecting quantitative modelling with 3D spatial planning.

Frequency combs consist of a spectrum of evenly spaced spectral lines. Optical frequency combs enable technologies ranging from timing, LiDAR, and ultra-stable signal sources. Microwave frequency combs are analogous to optical frequency combs, but often leverage electronic nonlinearity for comb generation. Generating microwave frequency combs using piezoelectric mechanical resonators would enable this behavior in a more compact form factor, thanks to the shorter acoustic wavelengths. In this work, we demonstrate a microwave frequency comb leveraging thermal nonlinearity in high quality factor ($Q$) overmoded acoustic resonators in thin film lithium niobate. By providing input power at 257 MHz, which is the sum frequency of two acoustic modes at 86 MHz and 171 MHz, we generate parametric down conversion and comb generation. We explore the nonlinear mixing regimes and the associated conditions for comb generation. Comb spacing is observed to vary significantly with drive frequency and power, and its general behavior is found to rely heavily on initial conditions. This demonstration showcases the potential for further improvement in compact and efficient microwave frequency combs, leveraging nonlinear acoustic resonators.