Nearly 110 million people are forcibly displaced people worldwide. However, estimating the scale and patterns of internally displaced persons in real time, and developing appropriate policy responses, remain hindered by traditional data streams. They are infrequently updated, costly and slow. Mobile phone location data can overcome these limitations, but only represent a population segment. Drawing on an anonymised large-scale, high-frequency dataset of locations from 25 million mobile devices, we propose an approach to leverage mobile phone data and produce population-level estimates of internal displacement. We use this approach to quantify the extent, pace and geographic patterns of internal displacement in Ukraine during the early stages of the Russian invasion in 2022. Our results produce reliable population-level estimates, enabling real-time monitoring of internal displacement at detailed spatio-temporal resolutions. Accurate estimations are crucial to support timely and effective humanitarian and disaster management responses, prioritising resources where they are most needed.

Migraine literacy among the public is known to be low, and this lack of understanding has a negative impact on migraineurs' quality of life. To understand this impact, we use text mining methods to study migraine discussion on the Reddit social media platform. We summarize the findings in the form of "four things people should know about chronic migraines": it is a serious disease that affects people of all ages, it can be triggered by many different factors, it affects women more than men, and it can get worse in combination with the COVID-19 virus.

We present the Quantum Memory Matrix (QMM) hypothesis, which addresses the longstanding Black Hole Information Paradox rooted in the apparent conflict between Quantum Mechanics (QM) and General Relativity (GR). This paradox raises the question of how information is preserved during black hole formation and evaporation, given that Hawking radiation appears to result in information loss, challenging unitarity in quantum mechanics. The QMM hypothesis proposes that space-time itself acts as a dynamic quantum information reservoir, with quantum imprints encoding information about quantum states and interactions directly into the fabric of space-time at the Planck scale. By defining a quantized model of space-time and mechanisms for information encoding and retrieval, QMM aims to conserve information in a manner consistent with unitarity during black hole processes. We develop a mathematical framework that includes space-time quantization, definitions of quantum imprints, and interactions that modify quantum state evolution within this structure. Explicit expressions for the interaction Hamiltonians are provided, demonstrating unitarity preservation in the combined system of quantum fields and the QMM. This hypothesis is compared with existing theories, including the holographic principle, black hole complementarity, and loop quantum gravity, noting its distinctions and examining its limitations. Finally, we discuss observable implications of QMM, suggesting pathways for experimental evaluation, such as potential deviations from thermality in Hawking radiation and their effects on gravitational wave signals. The QMM hypothesis aims to provide a pathway towards resolving the Black Hole Information Paradox while contributing to broader discussions in quantum gravity and cosmology.

The study examines how Gacha games affect gambling practices by analyzing when players start playing and how often they play and how much money they spend on games. The modified Problem Gambling Severity Index (PGSI) serves as the research tool to examine the impact of these variables on mobile Gacha game gambling-like severity. A survey distributed to online Gacha game communities provided the data which researchers analyzed by applying linear regression and multiple regression and T-test methods. The research indicates entry age demonstrated low association with gambling severity but gameplay frequency together with duration directly influenced higher gambling severity ratings. The research findings demonstrated that the level of Gacha game spending directly influences gambling severity scores among players. The research findings demonstrate how addictive potential exists in mobile Gacha games which requires stronger regulation of these platforms for young vulnerable gamers. Research needs to study the extended influence of Gacha game exposure while examining how cognitive biases affect gambling behavior patterns.

Biofilms exposed to flow experience shear stress, which leads to a competitive interaction between the growth and development of a biofilm and shearing. In this study, Pseudonomas fluorescene biofilm was grown in a microfluidic channel and exposed to forced flow of an aqueous solution of variable velocity. It can be observed that under certain conditions preferential flow paths form with a dynamic, but quasi-steady state interaction of growth, detachment, and re-attachment. We find that the regimes for preferential flow path development are determined by nutrient availability and the ratio of shear stress versus the biofilm's ability to resist shear forces. The intermittent regime of flow paths is mainly driven by the supply with nutrients, which we confirm by comparison with a numerical model based on coarse-grained molecular dynamics and Lattice Boltzmann hydrodynamics.

It is noteworthy that limiting compactness of a static bounded configuration is characterized by a general principle: \textit{one, by equipartition of mass between inside and outside, and the other by vanishing of energy inside.} The former implies gravitational energy being half of mass leading to limiting compactness $M/R = 4/9$ of Buchdahl star while for the latter, the two are equal giving $M/R = 1/2$ of black hole with horizon. \emph{This is the relativistic Virial theorem respectively for massive and massless particles.} It is remarkable that it prescribes that there can exist only two equilibrium states which also define limiting compactness of the object. Consequently, it leads to a profound prediction that the ultimate endproduct of gravitational collapse could only be one of the two, Buchdahl star or black hole.

We present the results of a novel classification scheme for all items, objects, concepts, and crucially -- things -- in the known and unknown universe. Our definitions of meat, soup and vegetable are near-exhaustive and represent a new era of scientific discovery within the rapidly-developing field of Arbitrary Classification. While the definitions of vegetable (growing in the ground), meat (growing in an animal) and soup (containing both vegetable and meat) may appear simple at first, we discuss a range of complex cases in which progress is rapidly being made, and provide definitions and clarifications for as many objects as a weekend of typing will allow.

Issuing timely severe weather warnings helps mitigate potentially disastrous consequences. Recent advancements in Neural Weather Models (NWMs) offer a computationally inexpensive and fast approach for forecasting atmospheric environments on a 0.25{\deg} global grid. For thunderstorms, these environments can be empirically post-processed to predict wind gust distributions at specific locations. With the Pangu-Weather NWM, we apply a hierarchy of statistical and deep learning post-processing methods to forecast hourly wind gusts up to three days ahead. To ensure statistical robustness, we constrain our probabilistic forecasts using generalised extreme-value distributions across five regions in Switzerland. Using a convolutional neural network to post-process the predicted atmospheric environment's spatial patterns yields the best results, outperforming direct forecasting approaches across lead times and wind gust speeds. Our results confirm the added value of NWMs for extreme wind forecasting, especially for designing more responsive early-warning systems.

Conductive ferroelectric domain walls (DWs) represent a promising topical system for the development of nanoelectronic components and device sensors to be operational at elevated temperatures. DWs show very different properties as compared to their hosting bulk crystal, in particular with respect to the high local electrical conductivity. The objective of this work is to demonstrate DW conductivity up to temperatures as high as 400 {\deg}C which extends previous studies significantly. Experimental investigation of the DW conductivity of charged, inclined DWs is performed using 5 mol% MgO-doped lithium niobate single crystals. Currentvoltage (IV) sweeps as recorded over repeated heating cycles reveal two distinct thermal activation energies for a given DW, with the higher of the activation energies only measured at higher temperatures. Depending on the specific sample, the higher activation energy is found above 160 {\deg}C to 230 {\deg}C. This suggests, in turn, that more than one type of defect/polaron is involved, and that the dominant transport mechanism changes with increasing temperature. First principles atomistic modelling suggests that the conductivity of inclined domain walls cannot be solely explained by the formation of a 2D carrier gas and must be supported by hopping processes. This holds true even at temperatures as high as 400 {\deg}C. Our investigations underline the potential to extend DW current based nanoelectronic and sensor applications even into the so-far unexplored temperature range up to 400 {\deg}C.

The concept of acoustoelasticity pertains to changes in elastic wave velocity within a medium when subjected to initial stresses. However, existing acoustoelastic expressions are predominantly developed for waves propagating parallel or perpendicular to the principal stress directions, where no shear stresses are involved. In our previous publication, we demonstrated that the impact of shear stresses on longitudinal wave velocity in concrete, when body waves propagate in the shear deformation plane, is negligible. This finding allows us to revise the acoustoelastic expression for longitudinal waves propagating inclined to the principal stress directions in stressed concrete. The revised expression reveals that the acoustoelastic effect for such longitudinal waves can be expressed using acoustoelastic parameters derived from waves propagating parallel and perpendicular to the uniaxial principal stress direction. To validate our theoretical statement, experiments were conducted on a concrete cylinder subjected to uniaxial stress. Despite slight fluctuations in the experimental observations, the overall trend of acoustoelastic effects for inclined propagating longitudinal waves aligns with the theory. This proposed theory holds potential for monitoring changes in the magnitudes and directions of principal stresses in the plane stress state.

High-order methods are well-suited for the numerical simulation of complex compressible turbulent flows, but require additional stabilization techniques to capture instabilities arising from the underlying non-linear hyperbolic equations. This paper provides a detailed comparison of the effectiveness of entropy stable discontinuous Galerkin methods for the stabilization of compressible (wall-bounded) turbulent flows. For this investigation, an entropy stable discontinuous Galerkin spectral element method is applied on Gauss-Legendre and Gauss-Lobatto nodes. In the compressible regime, an additional stabilization technique for shock capturing based on a convex blending of a low-order finite volume with the high-order discontinuous Galerkin operator is utilized. The present investigation provides a systematic study from convergence tests, to the Taylor-Green vortex and finally to a more intricate turbulent wall-bounded 3D diffuser flow, encompassing both weakly compressible and compressible regimes. The comparison demonstrates that the DGSEM on Gauss-Lobatto nodes is less accurate due to the lower integration accuracy. Conversely, it is faster than the DGSEM on Gauss-Legendre nodes due to a less severe time step restriction and simpler numerical operator. To the author's knowledge, this is the first time for which a comparison of entropy stable DGSEM on Gauss-Lobatto and Gauss-Legendre has been performed for compressible, wall-bounded turbulent flows with separation.

Advances in ultra-intense laser technology have increased repetition rates and average power for chirped-pulse laser systems, which offers a promising solution for many applications including energetic proton sources. An important challenge is the need to optimize and control the proton source by varying some of the many degrees of freedom inherent to the laser-plasma interactions. Machine learning can play an important role in this task, as our work examines. Building on our earlier work in Desai et al. 2024, we generate a large $\sim$1.5 million data point synthetic data set for proton acceleration using a physics-informed analytic model that we improved to include pre-pulse physics. Then, we train different machine learning methods on this data set to determine which methods perform efficiently and accurately. Generally, we find that quasi-real-time training of neural network models using single shot data from a kHz repetition rate ultra-intense laser system should typically be feasible on a single GPU. We also find that a less sophisticated model like a polynomial regression can be trained even faster and that the accuracy of these models is still good enough to be useful. We provide our source code and example synthetic data for others to test new machine learning methods and approaches to automated learning in this regime.

The traditional design approaches for high-degree-of-freedom metamaterials have been computationally intensive and, in many cases, even intractable due to the vast design space. In this work, we introduce a novel fixed-attention mechanism into a deep learning framework to address the computational challenges of metamaterial design. We consider a 3D plasmonic structure composed of gold nanorods characterized by geometric parameters and demonstrate that a Long Short-Term Memory network with a fixed-attention mechanism can improve the prediction accuracy by 48.09% compared to networks without attention. Additionally, we successfully apply this framework for the inverse design of plasmonic metamaterials. Our approach significantly reduces computational costs, opening the door for efficient real-time optimization of complex nanostructures.

We present a technique for detecting ultra-high frequency (UHF) radio fields using a three-photon Rydberg excitation scheme in a continuously laser cooled sample of Rb-87 atoms. By measuring Autler-Townes splitting, we demonstrate resonant detection of UHF fields with frequency range 500-900 MHz through probing F -> G transitions, achieving a lowest minimum detectable field of 2.5(6) mV/cm at 899 MHz. We also demonstrate continuous time detection of a modulated RF signal, with a 3 dB bandwidth of 4.7(4) kHz at a carrier frequency of 899 MHz. Our approach employs atom loss spectroscopy rather than electromagnetically induced transparency (EIT), which enables detection whilst the atomic sample is simultaneously laser cooled. We investigate this operating regime to determine the feasibility of combining the benefits of reduced thermal dephasing (and subsequent increased sensitivity) of cold atoms with the continuous operation associated with thermal atoms. Our continuous time detection scheme provides an advantage over existing pulsed cold atom systems as we avoid the slow experimental duty cycles typically associated with replenishing the cold atom ensemble. We characterize the excitation scheme by varying laser detunings and analyze the impact and limitations due to various broadening mechanisms on the detection sensitivity.

Evaporation from porous media is a key phenomenon in the terrestrial environment and is linked to accumulation of solutes at or near the evaporative surface. The current study aims at improved understanding of solute accumulation near evaporating surfaces. Analytical and numerical modelling studies have suggested the development of local instabilities due to density differences during evaporation in case of saturated porous media with high permeability. These instabilities lead to density-driven downward flow through fingering, and thus a redistribution of solutes. To experimentally investigate this, we performed evaporation experiments on two types of porous media (F36 and W3) with intrinsic permeabilities that differed by two orders of magnitude. Using magnetic resonance imaging (23Na-MRI), we monitored the development of solute accumulation and subsequent redistribution during evaporation with a continuous supply of water at the bottom of the samples. Significant differences between the Na enrichment patterns were observed for the two porous media. The F36 sample showed an initial enrichment at the surface within the first hour, but soon after a downwards moving plume developed that redistributed NaCl back into the column. Average depth profiles of Na concentrations showed that the surface concentration reached only 2.5 M, well below the solubility limit. In contrast, the W3 sample with lower permeability showed enrichment in a shallow near-surface zone where a concentration of over 6 M was reached. Comparison of experimental results with numerical simulations using DuMux showed qualitative agreement between measured and modelled solute concentrations. This study experimentally confirms the importance of density-driven redistribution of solutes in case of saturated porous media, which has implications for predicting evaporation rates and the time to the start of salt crust formation.

In this paper, we introduce a formulation of Physics-Informed Neural Networks (PINNs), based on learning the form of the Fourier decomposition, and a training methodology based on a spread of randomly chosen boundary conditions. By training in this way we produce a PINN that generalises; after training it can be used to correctly predict the solution for an arbitrary set of boundary conditions and interpolate this solution between the samples that spanned the training domain. We demonstrate for a toy system of two coupled oscillators that this gives the PINN formulation genuine predictive capability owing to an effective reduction of the training to evaluation times ratio due to this decoupling of the solution from specific boundary conditions.

A High-Pressure Ion Trap operating at pressure ~1 Torr is a core component of the portable hand-held mass-spectrometric gas analyzer. A comprehensive mathematical model of the HPIT is described in this paper. The influence of the instrumental parameters (gas composition, pressure, and temperature; applied voltages; ion trap size and geometry) and ion properties (mass, diffusion, and mobility) on the ion trap analytical parameters (mass-spectral peak position, height, and width) is examined. The model explained the difference between a high-pressure and regular low-pressure ion trap.

The intermittency of coherent turbulent structures in the tropical cyclone boundary layer makes them challenging to fully characterize, especially regarding their impact on momentum dynamics in the eyewall. Furthermore, the fine spatial and temporal model resolution needed to resolve these structures has long impeded their understanding. Using the output of a large eddy simulation (with 31-m horizontal and 15-m vertical grid spacing), we investigate the three-dimensional structure of conditionally averaged eddies associated with extreme Reynolds stress events in the near-surface region of a category 5 tropical cyclone. For ejection events, we find (using two- and three-dimensional analysis) that the structure of the conditional eddy is a vortex core roughly inclined toward the direction of the mean flow in the inflowing boundary layer, outer and inner eyewall region. For sweep events, this vortex core is less coherent. The nature of the educed eddies found in this study suggests a similarity between the coherent structures in simpler wall-bounded turbulent flows and those in complex environmental flows.

Heterogeneously-integrated electro-optic modulators (EOM) are demonstrated using the hybrid-mode concept, incorporating thin-film lithium niobate (LN) by bonding with silicon nitride (SiN) passive photonics. At wavelengths near 1550 nm, these EOMs demonstrated greater than 30 dB extinction ratio, 3.8 dB on-chip insertion loss, a low-frequency half-wave voltage-length product ($V_\pi L$) of 3.8 V.cm, and a 3-dB EO modulation bandwidth exceeding 110 GHz. This work demonstrates the combination of multi-layer low-loss SiN waveguides with high-performance LN EOMs made in a scalable fabrication process using conventional low-resistivity silicon (Si) wafers.

Stochastic Subspace Identification (SSI) is widely used in modal analysis of engineering structures, known for its numerical stability and high accuracy in modal parameter identification. SSI methods are generally classified into two types: Data-Driven (SSI-Data) and Covariance-Driven (SSI-Cov), which have been considered to originate from different theoretical foundations and computational principles. In contrast, this study demonstrates that SSI-Cov and SSI-Data converge to the same solution under the condition of infinite observations, by establishing a unified framework incorporating instrumental variable analysis. Further, a novel modal identification approach, Principal Component Stochastic Subspace Identification (PCSSI), is proposed based on this framework. This method employs Principal Component Analysis (PCA) to extract key components of the signal subspace and project the observed data onto this space, enhancing modal identification stability while significantly reducing computational complexity. Through 5000 Monte Carlo numerical simulations, the statistical analysis shows that PCSSI consistently outperforms traditional SSI methods in terms of numerical stability and noise reduction, demonstrating clear advantages over both SSI-Cov and SSI-Data. Its effectiveness is further validated using experimental data from a scaled bridge model. Compared to conventional SSI approaches, PCSSI demonstrates superior robustness under complex engineering conditions, especially when dealing with limited data or high noise levels, underscoring its strong potential for practical applications.

Exploring shock-shock interactions has been limited by experimental constraints, particularly in laser-induced shock experiments due to specialized equipment requirements. Herein, we introduce a tabletop approach to systematically investigate the excitation and superposition of dual laser-induced shock waves in water. Utilizing two laser pulses, spatio-temporally separated and focused into a confined water layer, we identify the optimal superposition leading to the highest combined shock pressure. Our results demonstrate that combining two shock waves each of $\sim$0.6~GPa pressure yields an overall shock pressure of $\sim$3~GPa. Our findings, suggesting an inherent nonlinear summation from the laser excitation process itself and highlights a new pathway for energy-efficient laser shock wave excitation.

Hibernation is an adaptation to extreme environmental seasonality that has been studied for almost 200 years, but our mechanistic understanding of the underlying physiological system remains lacking due to the partially observed nature of the system. During hibernation, small mammals, such as the Arctic ground squirrel, exhibit dramatic oscillations in body temperature, typically one of the only physiological states measured, of up to 40 $^{\circ}$C. These spikes are known as interbout arousals and typically occur 10-20 times throughout hibernation. The physiological mechanism that drives interbout arousals is unknown, but two distinct mechanisms have been hypothesized. Using model selection for partially observed systems, we are able to differentiate between these two mechanistic hypotheses using only body temperature data recorded from a free-ranging Arctic ground squirrel. We then modify our discovered physiological model of Arctic ground squirrel to include environmental information and find that we can qualitatively match body temperature data recorded from a wide range of species, including a bird, a shrew, and a bear, which also dynamically modulate body temperature. Our results suggest that a universal, environmentally sensitive mechanism could regulate body temperature across a diverse range of species -- a mechanistic restructuring of our current understanding of the physiological organization across species. While the findings presented here are applicable to thermophysiology, the general modeling procedure is applicable to time series data collected from partially observed biological, chemical, physical, mechanical, and cosmic systems for which the goal is to elucidate the underlying mechanism or control structure.

Ice nucleation plays a pivotal role in many natural and industrial processes, and molecular simulations play have proven vital in uncovering its kinetics and mechanisms. A fundamental component of such simulations is the choice of an order parameter (OP) that quantifies the progress of nucleation, with the efficacy of an OP typically measured by its ability to predict the committor probabilities. Here, we leverage a machine learning framework introduced in our earlier work (Domingues,~\emph{et al.}, \emph{J. Phys. Chem. Lett.}, 15, 1279, {\bf 2024}) to systematically investigate how key implementation details influence the efficacy of standard Steinhardt OPs in capturing the progress of both homogeneous and heterogeneous ice nucleation. Our analysis identify distance and $q_6$ cutoffs, as the primary determinants of OP performance, regardless of the mode of nucleation. We also examine the impact of two popular refinement strategies, namely chain exclusion and hydration shell inclusion, on OP efficacy. We find neither strategy to exhibit a universally consistent impact. Instead, their efficacy depends strongly on the chosen distance and $q_6$ cutoffs. Chain exclusion enhances OP efficacy when the underlying OP lacks sufficient selectivity, whereas hydration shell inclusion is beneficial for overly selective OPs. Consequently, we demonstrate that selecting optimal combinations of such cutoffs can eliminate the need for these refinement strategies altogether. These findings provide a systematic understanding of how to design and optimize OPs for accurately describing complex nucleation phenomena, offering valuable guidance for improving the predictive power of molecular simulations.

Rays and skates tend to have different fin kinematics depending on their proximity to a ground plane such as the sea floor. Near the ground, rays tend to be more undulatory (high wavenumber), while far from the ground, rays tend to be more oscillatory (low wavenumber). It is unknown whether these differences are driven by hydrodynamics or other biological pressures. Here we show that near the ground, the time-averaged lift on a ray-like fin is highly dependent on wavenumber. We support our claims using a ray-inspired robotic rig that can produce oscillatory and undulatory motions on the same fin. Potential flow simulations reveal that lift is always negative because quasisteady forces overcome wake-induced forces. Three-dimensional flow measurements demonstrate that oscillatory wakes are more disrupted by the ground than undulatory wakes. All these effects lead to a suction force toward the ground that is stronger and more destabilizing for oscillatory fins than undulatory fins. Our results suggest that wavenumber plays a role in the near-ground dynamics of ray-like fins, particularly in terms of dorsoventral accelerations. The fact that lower wavenumber is linked with stronger suction forces offers a new way to interpret the depth-dependent kinematics of rays and ray-inspired robots.

Airy waves, known for their non-diffracting and self-accelerating properties, have been extensively studied in spatial and temporal domains, but their spatiotemporal (ST) counterparts remain largely unexplored. We report the first experimental realization of a spatiotemporal Airy rings wavepacket, which exhibits an Airy function distribution in the radial dimension of the ST domain. The wavepacket demonstrates abrupt autofocusing under the combined effects of diffraction and dispersion, achieving a 110 um spatial and 320 fs temporal focus with a sharp intensity contrast along the propagation direction - ideal for nonlinear microscopy and multiphoton 3D printing. Notably, the wavepacket retains its autofocusing capability even after spatial obstruction, showcasing robust self-healing. Furthermore, by embedding a vortex phase, we create an ST-Airy vortex wavepacket that confines transverse orbital angular momentum (t-OAM) within a compact ST volume, enabling new avenues for studying light-matter interactions with t-OAM. Our findings advance the fundamental understanding of ST Airy waves and highlight their potential for transformative applications in ultrafast optics, structured light, and precision laser processing.

This study demonstrates the feasibility of using smaller capillary emitters to achieve higher specific impulse ($I_\text{sp}$) in electrospray propulsion. Four ionic liquids were characterized using capillary emitters with tip diameters from 15 to 50 $\mu$m. Smaller diameter capillaries produced smaller and more stable Taylor cones. This stabilization enabled steady cone-jet operation at significantly lower flow rates compared to larger emitters. This was unexpected because when the jet diameter is much smaller than far-field geometric features, the minimum flow rate is thought to be solely determined by the physical properties of the propellant. Using the smaller emitters and acceleration voltages of 10 kV, specific impulses up to 3000 s could be achieved with efficiencies above 50%, approximately doubling the $I_\text{sp}$ observed with larger emitters. For one of the liquids and the smallest emitters, the beam consisted solely of ions at the lowest flow rates, similarly to studies using externally wetted and porous emitters. Another important finding was that at sufficiently low flow rates, a significant fraction of the propellant fed to the emitter is not accelerated by the electrostatic field. These propellant losses make the time-of-flight technique unreliable for determining the $I_\text{sp}$.

Optimizing the performance of magnetic confinement fusion devices is critical to achieving an attractive fusion reactor design. Negative triangularity (NT) scenarios have been shown to achieve excellent levels of energy confinement, while avoiding edge localized modes (ELMs). Modeling turbulent transport in the edge and SOL is key in understanding the impact of NT on turbulence and extrapolating the results to future devices and regimes. Previous gyrokinetic turbulence studies have reported beneficial effects of NT across a broad range of parameters. However, most simulations have focused on the inner plasma region, neglecting the impact of NT on the outermost edge. In this work, we investigate the effect of NT in edge and scrape-off layer (SOL) simulations, including the magnetic X-point and separatrix. For the first time, we employ a multi-fidelity approach, combining global, non-linear gyrokinetic simulations with drift-reduced fluid simulations, to gain a deeper understanding of the underlying physics at play. First-principles simulations using the GENE-X code demonstrate that in comparable NT and PT geometries, similar profiles are achieved, while the turbulent heat flux is reduced by more than 50% in NT. Comparisons with results from the drift-reduced fluid turbulence code GRILLIX suggest that the turbulence is driven by trapped electron modes (TEMs). The parallel heat flux width on the divertor targets is reduced in NT, primarily due to a lower spreading factor $S$.

We assess a prognostic formulation of triple coherence relating to energy exchange between mesoscale eddies and the internal wavefield and compare with observations from the Sargasso Sea. This effort involves updates to a theory articulated in M\"uller (1976) that balances eddy induced wavefield perturbations with nonlinearity using a relaxation time scale approximation. Agreement of the prognostic formulations with data is remarkable and is consistent with eddy-wave coupling dominating the regional internal wave energy budget. The goodness of this effort reinforces a prior hypothesis that the character of the internal wavefield in the Sargasso Sea is set by this interaction, which, in turn, serves as an amplifier of tertiary energy inputs from larger vertical scales that characterize internal swell. Extraction of eddy energy happens at the horizontal and vertical scales that characterize baroclinic instability and potential vorticity fluxes. With this knowledge and confidence, we then speculate on the role that this coupling plays with regards to mesoscale eddy dynamics in the Southern Recirculation Gyre of the Gulf Stream. We argue that this nonlinear relaxation effectively provides a local eddy enstrophy damping consistent with potential vorticity flux observations from the Local Dynamics Experiment. This happens at spatial scales somewhat smaller than the energy extraction scale and locates the end of the potential enstrophy cascade in the spectral domain as the energy containing scale of the internal wavefield. The dynamical consequence is that mesoscale eddy - internal wave coupling is responsible for the maintenance of gyre scale potential vorticity gradients.

The interaction between light and vapors in the presence of magnetic fields is fundamental to many quantum technologies and applications. Recently, the ability to geometrically confine atoms into periodic structures has enabled the creation of hybrid atomic-diffractive optical elements. However, the application of magnetic fields to such structures remains largely unexplored, offering potential for both fundamental and applied insights. Here, we present measurements of an atomic-diffractive optical element subject to magnetic fields. In contrast to the well-known polarization rotation in Faraday configurations, the diffractive atomic elements exhibit additional rotation terms, which we validate both theoretically and experimentally. Moreover, we find that the introduction of spatially varying magnetic fields leads to a reduction in fringe visibility, which can be leveraged for gradiometric applications. Our study sheds light on the fundamental magneto-optic properties of geometrically confined atomic systems and provides guidelines for chip-scale gradient magnetic measurements.

As stated in the 2019 European Strategy for Particle Physics (ESPP), it is of the utmost importance that the HL-LHC upgrade of the accelerator and the experiments be successfully completed in a timely manner. All necessary efforts should be devoted to achieving this goal. We also recall two of the principal recommendations of the 2019 ESPP for future accelerator initiatives, namely that 1) An electron-positron Higgs factory is the highest priority for the next collider (Rec. c). 2) Europe, together with its international partners, should investigate the technical and financial feasibility of a future hadron collider at CERN with a centre-of-mass energy of at least 100 TeV and with an electron-positron Higgs and electroweak factory as a possible first stage (Rec. e). A major objective in particle physics is always to operate an accelerator that allows a leap of an order of magnitude in the constituent centre-of-mass energy with respect to the previous one. We support FCC-ee and FCC-hh as the preferred option for CERN future, as it addresses both of the above recommendations. The guidance for the 2025 ESPP requests, in addition to the preferred option, the inclusion of ``prioritised alternatives to be pursued if the chosen preferred option turns out not to be feasible or competitive''. Proposed alternatives to the preferred FCC option include linear, muon colliders and LHeC accelerators. In response to this request we propose reusing the existing LHC tunnel for an electron-positron collider, called LEP3, as a back-up alternative if the FCC cannot proceed. LEP3 leverages much of the R\&D conducted for FCC-ee, offers high-precision studies of Z, W, and Higgs bosons below the tt threshold, and offers potential physics performance comparable or superior to other fallback options at a lower cost while supporting continued R\&D towards a next-generation energy frontier machine.

Density functional theory (DFT) has transformed our ability to investigate and understand electronic ground states. In its original formulation, however, DFT is not suited to addressing (e.g.) degenerate ground states, mixed states with different particle numbers, or excited states. All these issues can be handled, in principle exactly, via ensemble DFT (EDFT). This Perspective provides a detailed introduction to and analysis of EDFT, in an in-principle exact framework that is constructed to avoid uncontrolled errors and inconsistencies that may be associated with {\it ad hoc} extensions of conventional DFT. In particular, it focuses on the "ensemblization" of both exact and approximate density functionals, a term we coin to describe a rigorous approach that lends itself to the construction of novel approximations consistent with the general ensemble framework, yet applicable to practical problems where traditional DFT tends to fail or does not apply at all. Specifically, symmetry considerations and ensemble properties are shown to enable each other in shaping a practical DFT-based methodology that extends beyond the ground state and, in doing so, highlights the need to look outside the standard ground state Kohn-Sham treatment.

The Advanced Wakefield Experiment, AWAKE, is a well-established international collaboration and aims to develop the proton-driven plasma wakefield acceleration of electron bunches to energies and qualities suitable for first particle physics applications, such as strong-field QED and fixed target experiments ($\sim$50-200GeV). Numerical simulations show that these energies can be reached with an average accelerating gradient of $\sim1$GeV/m in a single proton-driven plasma wakefield stage. This is enabled by the high energy per particle and per bunch of the CERN SPS 19kJ, 400GeV and LHC ($\sim$120kJ, 7TeV) proton bunches. Bunches produced by synchrotrons are long, and AWAKE takes advantage of the self-modulation process to drive wakefields with GV/m amplitude. By the end of 2025, all physics concepts related to self-modulation will have been experimentally established as part of the AWAKE ongoing program that started in 2016. Key achievements include: direct observation of self-modulation, stabilization and control by two seeding methods, acceleration of externally injected electrons from 19MeV to more than 2GeV, and sustained high wakefield amplitudes beyond self-modulation saturation using a plasma density step. In addition to a brief summary of achievements reached so far, this document outlines the AWAKE roadmap as a demonstrator facility for producing beams with quality sufficient for first applications. The plan includes: 1) Accelerating a quality-controlled electron bunch to multi-GeV energies in a 10m plasma by 2031; 2) Demonstrating scalability to even higher energies by LS4. Synergies of the R&D performed in AWAKE that are relevant for advancing plasma wakefield acceleration in general are highlighted. We argue that AWAKE and similar advanced accelerator R&D be strongly supported by the European Strategy for Particle Physics Update.

This study demonstrates the application of cosmic-ray muography as a non-invasive method to assess sediment accumulation and tidal influences in the Shanghai Outer Ring Tunnel, an immersed tube tunnel beneath the Huangpu River in Shanghai, China. A portable, dual-layer plastic scintillator detector was deployed to conduct muon flux scans along the tunnel's length and to continuously monitor muon flux to study tidal effects. Geant4 simulations validated the correlation between muon attenuation and overburden thickness, incorporating sediment, water, and concrete layers. Key findings revealed an 11\% reduction in muon flux per meter of tidal water level increase, demonstrating a strong anti-correlation (correlation coefficient: -0.8) with tidal cycles. The results align with geotechnical data and simulations, especially in the region of interest, confirming muography's sensitivity to sediment dynamics. This work establishes muography as a robust tool for long-term, real-time monitoring of submerged infrastructure, offering significant advantages over conventional invasive techniques. The study underscores the potential for integrating muography into civil engineering practices to enhance safety and operational resilience in tidal environments.

Time-varying gravity field survey is one of the important methods for seismic risk assessment. To obtain accurate timevarying gravity data, it is essential to establish a gravity reference, which can be achieved using absolute gravimeters. Atom gravimeters, as a recently emerging type of absolute gravimeter, have not yet been practically validated for their reliability in mobile gravity surveys. To study and evaluate the operational status and performance metrics of the A-Grav atom gravimeter under complex field conditions, the University of Science and Technology of China, Hefei National Laboratory, and the Anhui Earthquake Agency conducted a joint observation experiment using an atom gravimeter (AGrav) and relative gravimeters (CG-6) within the North China Seismic Gravity Monitoring Network. The experiment yielded the following results: 1) The standard deviations for mobile observations of the atom gravimeter is 2.1 {\mu}Gal; 2)The mean differences in point values and segment differences between the atom gravimeter and the relative gravimeter at the same locations is 5.8(17.1) {\mu}Gal and 4.4(11.0) {\mu}Gal, respectively, with point value differences of less than 2.0 {\mu}Gal compared to the FG5X absolute gravimeter at the same location; 3) The results of hybrid gravity adjustment based on absolute gravity control and the point value precision at each measurement point, with an average point value precision of 3.6 {\mu}Gal. The results indicate that the A-Grav atom gravimeter has observation accuracy and precision comparable to the FG5X absolute gravimeter, demonstrating good stability and reliability in field mobile measurements, and can meet the requirements for seismic gravity monitoring. This work provides a technical reference for the practical application of atom gravimeters in control measurements and time-varying gravity monitoring for earthquakes.

Exceptional points (EPs) have consistently held a central role in non-Hermitian physics due to their unique physical properties and potential applications. They have been intensively explored in parity-time ($\mathcal {P}\mathcal {T}$)-symmetric systems or other non-Hermitian systems; however, they barely investigated in pseudo-Hermitian systems with non-Markovian environments. In this work, we study higher-order EPs in three coupled cavities (denoted as $a$, $b_1$, and $b_2$) under pseudo-Hermitian conditions. Specifically, the cavity $a$ simultaneously interacts with two Markovian environments, while the cavity $b_1$ and $b_2$ couples with the respective Markovian environments. Through coherent perfect absorption (CPA) of two input fields with the cavity $a$, we obtain an effective gain for the system. Under certain parametric conditions, the effective Hamiltonian of the system holds pseudo-Hermiticity, where the third-order exceptional point (EP3) can be observed by measuring the output spectrum of the system. Moreover, we generalize the results to the non-Markovian regimes (only two environments coupling with the cavity $a$ are non-Markovian, while the other two environments coupling with cavities $b_1$ and $b_2$ are Markovian), which leads to the emergence of fourth-order exceptional points (EP4) and fifth-order exceptional points (EP5). In particular, EP4 and EP5 in the non-Markovian limit (corresponding to the infinite spectral width) can return to EP3 under the Markovian approximation. Finally, we extend the systems to more general non-Hermitian ones without pseudo-Hermitian constraints and find the higher-order EPs (EP6 and EP7), where all four environments are non-Markovian. The study presents expansions of non-Hermitian physics into the field of non-Markovian dynamics and anticipates the profound impact in quantum optics and precision measurement.

Reducing electromagnetic scattering from an object has always been a task, inspiring efforts across disciplines such as materials science and electromagnetic theory. The pursuit of electromagnetic cloaking significantly advanced the field of metamaterials, yet achieving broadband, conformal cloaking for complex, non-trivial objects remains an unresolved challenge. Here, we introduce the concept of 'tailor-made metasurfaces' - machine-designed aperiodic structures optimized to suppress scattering from arbitrary objects by accounting for their layout, including resonant or large-scale features. Specifically, we demonstrated a wideband ~20% fractional bandwidth scattering suppression of more than 20-30 dB for various generic test objects, including randomly distributed wire meshes, spheres, and polygons. The demonstrated evolutionary optimization marks a leap forward in electromagnetic design, enabling the development of high-performance structures to meet complex technological demands.

Mucociliary clearance in mammals serves as the primary defense mechanism for removing particulate matter deposited in the pulmonary airways. Dysfunctions in this process are linked to serious respiratory diseases and can hinder effective drug delivery to the lungs. Microfluidic systems have emerged as a promising alternative for replicating lung functions in non-cellular physiological environments, offering a simpler and more controllable approach compared to in vivo and in vitro assays. Here we present a microfluidic platform featuring a closed-loop circular microchannel, integrating thousand 75 micrometer high magnetic pillars arranged in a square array. Made of polydimethylsiloxane and loaded with iron microparticles, the pillars are studied using scanning electron microscopy and magnetometry; their internal structure and bending response to a magnetic field are quantitatively analyzed. Using a combination of experimental data and finite element simulations, we found that the magnetic torque induced by permanent magnets dominates over magnetic force, generating fluid flow in the microchannel. Under the application of a rotating field, the time-dependent deflection of the pillars closely mimics the behavior of lung cilia, exhibiting alternating recovery phases and rapid whip-like movements. The velocity profiles of viscous and viscoelastic fluids are examined, and shown to display Poiseuille-type flow. By varying the viscosity of the fluids across four orders of magnitude, we identified a transition in propulsion regimes between viscous and elastic-driven flows. This active microfluidic platform offers a promising approach for modeling mucociliary clearance in drug delivery applications.

We demonstrate third-order conjugate exceptional points (EPs) in a gain-loss assisted multi-mode 1D complementary photonic bandgap waveguide. Our study reveals the higher-order mode conversion phenomenon facilitated by parametrically encircled third-order conjugate EPs, showcasing the potential for on-chip mode conversion

Pump-probe saturation spectroscopy in the sub-terahertz region was performed in the rotational transition (J, K) = (16, 0) <- (15, 0) for gas-phase acetonitrile molecules in the counter-propagating configuration. We observed Lamb-dips at much lower excitation powers than previously reported. The linewidth in the zero-pressure limit was 10 kHz, which was estimated from the intensity and pressure dependence. This corresponds to the transit-time broadening.

We study the SIRS epidemic model, both analytically and on a square lattice. The analytic model has two stable solutions, post outbreak/epidemic (no infected, $I=0$) and the endemic state (constant number of infected: $I>0$). When the model is implemented with noise, or on a lattice, a third state is possible, featuring regular oscillations. This is understood as a cycle of boom and bust, where an epidemic sweeps through, and dies out leaving a small number of isolated infecteds. As immunity wanes, herd immunity is lost throughout the population and the epidemic repeats. The key result is that the oscillation is an intrinsic feature of the system itself, not driven by external factors such as seasonality or behavioural changes. The model shows that non-seasonal oscillations, such as those observed for the omicron COVID variant, need no additional explanation such as the appearance of more infectious variants at regular intervals or coupling to behaviour. We infer that the loss of immunity to the SARS-CoV-2 virus occurs on a timescale of about ten weeks.

Bismuth is a particularly promising alternative plasmonic metal because of its theoretically predicted wide spectral bandwidth. In this study, we experimentally demonstrated the correlation between the shape and size of individual bismuth plasmonic antennas and their optical properties. To this end, we employed a combination of scanning transmission electron microscopy and electron energy loss spectroscopy. Bar-shaped and bowtie bismuth plasmonic antennas of various sizes were fabricated by focused ion beam lithography of a polycrystalline bismuth thin film. Our experimental findings demonstrate that these antennas support localised surface plasmon resonances and their dipole modes can be tuned through their size from the near-infrared to the entire visible spectral region. Furthermore, our findings demonstrate that bismuth exhibits a plasmon dispersion relation that is nearly identical to that of gold while maintaining its plasmonic performance even at higher plasmon energies, thus rendering it a promising low-cost alternative to gold.

Neuromorphic devices, leveraging novel physical phenomena, offer a promising path toward energy-efficient hardware beyond CMOS technology by emulating brain-inspired computation. However, their progress is often limited to proof-of-concept studies due to the lack of flexible spiking neural network (SNN) algorithm frameworks tailored to device-specific characteristics, posing a significant challenge to scalability and practical deployment. To address this, we propose QUEST, a unified co-design framework that directly trains SNN for emerging devices featuring multilevel resistances. With Skyrmionic Magnetic Tunnel Junction (Sk-MTJ) as a case study, experimental results on the CIFAR-10 dataset demonstrate the framework's ability to enable scalable on-device SNN training with minimal energy consumption during both feedforward and backpropagation. By introducing device mapping pattern and activation operation sparsity, QUEST achieves effective trade-offs among high accuracy (89.6%), low bit precision (2-bit), and energy efficiency (93 times improvement over the ANNs). QUEST offers practical design guidelines for both the device and algorithm communities, providing insights to build energy-efficient and large-scale neuromorphic systems.

Magnetospheric twists, that is magnetospheres with a toroidal component, are under scrutiny due to the key role the twist is believed to play in the behaviour of neutron stars. Notably, its dissipation is believed to power magnetar activity, and is an important element of the evolution of these stars. We exhibit a new class of twisted axi-symmetric force-free magnetospheric solutions. We solve the Grad-Shafranov equation by introducing an ansatz akin to a multipolar expansion. We obtain a hierarchical system of ordinary differential equations where lower-order multipoles source the higher-order ones. We show that analytical approximations can be obtained, and that in general solutions can be numerically computed using standard solvers. We obtain a class of solutions with a great flexibility in initial conditions, and show that a subset of these asymptotically tend to vacuum. The twist is not confined to a subset of field lines. The solutions are symmetric about the equator, with a toroidal component that can be reversed. This symmetry is supported by an equatorial current sheet. We provide a first-order approximation of a particular solution that consists in the superposition of a vacuum dipole and a toroidal magnetic field sourced by the dipole, where the toroidal component decays as $1/r^4$. As an example of strongly multipolar solution, we also exhibit cases with an additional octupole component.

We investigate the reconstruction of the transmission matrix of a time-evolving atmospheric channel with an online recursive optimization routine, using wave-optics simulations. We demonstrate that this estimation technique is able to keep up with the evolution of the channel and enables a significant improvement of communication-relevant quantities such as the total power transmitted through the channel, and the coupling of the received light into a single mode fiber. Moreover, we show that this approach is robust against measurement noise, and that it notably reduces the probability and duration of power outages, even in strong turbulence.

Spherically averaged periodic pair potentials offer the enticing promise to provide accurate results at a drastically reduced computational cost compared to the traditional Ewald sum. In this work, we employ the pair potential by Yakub and Ronchi [\textit{J.~Chem.~Phys.}~\textbf{119}, 11556 (2003)] in \emph{ab initio} path integral Monte Carlo (PIMC) simulations of the warm dense uniform electron gas. Overall, we find very accurate results with respect to Ewald reference data for integrated properties such as the kinetic and potential energy, whereas wavenumber resolved properties such as the static structure factor $S(\mathbf{q})$, the static linear density response $\chi(\mathbf{q})$ and the static quadratic density response $\chi^{(2)}(\mathbf{q},0)$ fluctuate for small $q$. In addition, we perform an analytic continuation to compute the dynamic structure factor $S(\mathbf{q},\omega)$ from PIMC results of the imaginary-time density--density correlation function $F(\mathbf{q},\tau)$ for both pair potentials. Our results have important implications for future PIMC calculations, which can be sped up significantly using the YR potential for the estimation of equation-of-state properties or $q$-resolved observables in the non-collective regime, whereas a full Ewald treatment is mandatory to accurately resolve physical effects manifesting for smaller $q$, including the evaluation of compressibility sum rules, the interpretation of x-ray scattering experiments at small scattering angles, and the estimation of optical and transport properties.

Anomalous heat diffusion is investigated for biological tissues displaying a fractal structure and long-term thermal memory, which is modeled via a fractional derivative. For increasing values of the fractional derivation order, the tissue temperature displays three kinds of bio-heat regimes: damped (or sub-diffusive), critical damping and under-damped oscillations. The temperature profiles depend on the fractal dimension of the tissue but notably also on a parameter related to its topology: the spectral dimension. The parametric analysis reveals that these two parameters have antagonistic effects on the pseudo period of the temperature oscillations and their amplitudes. We discuss how our results might impact some treatment protocols.

Many hard combinatorial problems can be mapped onto Ising models, which replicate the behavior of classical spins. Recent advances in probabilistic computers are characterized by parallelization and the introduction of novel hardware platforms. An interesting application of probabilistic computers is to operate them in `reverse' mode, where the network self-organizes its behavior to find the input bits that result in an output state. This can be used, for example, as a factorizer of semiprimes. One issue with simulating probabilistic computers on standard logic devices, such as field-programmable gate arrays, is that the update rules for each spin involve many multiplications, evaluation of a hyperbolic tangent, and a high-resolution numerical comparison. We simplify these rules, which improves the spatial and temporal circuit complexity when simulating a probabilistic computer on a field-programmable gate array. Applying our method to factorizing semiprimes, we achieve at least an order-of-magnitude reduction in the on-chip resources and the time-to-solution compared to recently reported methods. For a 32-bit semiprime, we achieve an average factorization in $\sim$100 s. Our approach will inspire new physical realizations of probabilistic computers because we relax some of their update-rule requirements.

Coherence between multiple low-frequency components latent in the flow fields characterizes the nonlinear aspects of fluid dynamics. This study reveals the existance of the distinct frequency components and their interaction relation of the classical Mode A of the cylinder wake. Primaries are one-third of the Karman vortex shedding frequency (third-subharmonic) and bubble pumping, known as the previous study. However, when the spanwise domain size in numerical simulations is sufficiently large, their interaction is obscured by the presence of numerous frequency components. To address this, we introduce a process in which distinct frequency components gradually emerge by starting with a small spanwise domain size and then gradually increasing it from 3.3D to 4.7D, where D represents the diameter of the cylinder. From 3.3D to 3.5D, only the vortex shedding frequency harmonics are present. Third-subharmonic frequency appeared ranging from 3.5D to 3.7D. Bispectral mode decomposition reveals that the harmonics of the third-subharmonic frequency govern the flow in this domain size. The bubble pumping is emergence in the flow fields between 3.7D and 3.8D. The frequency component after this emergence is not only the harmonics of bubble pumping and periodic nature is disrupt. Nonlinear interactions between bubble pumping, the Karman vortex, and the third-subharmonic component complicate the temporal behavior of the flow field. Utilizing the constraint of the spanwise domain size, our approach effectively reveals the interaction relationship between frequency components inherent a flow field with a significant number of frequency components.

Rydberg atoms are widely employed in precision spectroscopy and quantum information science. To minimize atomic decoherence caused by dc Stark effect, the electric field noise at the Rydberg atom location should be kept below $\sim 10$ mV/cm. Here we present a simple yet effective electronic circuit, referred to as a clamp switch, that allows one to realize such conditions. The clamp switch enables precise low-noise electric field control while allowing application of fast high-voltage ionization pulses through the same electrode(s), enabling atom detection via electric-field ionization and electron or ion counting. We outline the circuit design and analyze its noise suppression performance for both small and large input signals. In application examples, we employ the clamp switch to reduce the spectral width and increase the signal strength of a Rydberg line by a factor of two, to estimate the electric-field noise in the testing chamber, and to perform electric-field calibration using Rydberg Stark spectroscopy. The clamp switch improves coherence times and spectroscopic resolution in fundamental and applied quantum science research with Rydberg atoms.

Understanding the optical properties of various components in water Cherenkov (WC) neutrino experiments is essential for accurate detector characterization, which is critical for precise measurements. Of particular importance is the characterization of surface reflectivity within the Cherenkov volume. We present a methodology for surface reflectivity characterization using a goniometer setup, addressing the challenges associated with measurements in the air and water (or other optical media). Additionally, we discuss the broader implications of Bidirectional Reflectance Distribution Function (BRDF) measurements using a goniometer, including their industrial applications.

The Stirling engine is a type of heat engine known as its high efficiency. It is applied in solar thermal power, cogeneration, space nuclear power, and other fields. Although there are many different types of Stirling engines, their airflow paths are always linear. This article designs two types of Stirling engines with loop airflow path: the O-type engines without regenerator and the 8-type engines with regenerator. The modeling and simulation of the O-type engines show its excellent performance compared with the conventional Stirling engine. At the same time, its design without regenerator makes it more practical and has greater potential in terms of power. The 8-type engines use its unique 8-type airflow path to allow gas to enter the regenerator in advance, eliminating the almost useless four heat exchanges, resulting in higher thermal efficiency and better robustness.

Metal-based thermal metasurfaces exhibit stable spectral characteristics under temperature fluctuations, in contrast to more traditional gray- and near black-bodies, as well as some dielectric metasurfaces, whose emission spectra shift with changing temperatures. However, they often suffer from limited quality (Q) factors due to significant non-radiative ohmic losses. In this study, we address the challenge of achieving high emissivity and Q-factors in metal-based thermal emitters. By leveraging the coupling between a magnetic dipole resonance and two bound-state-in-continuum (BIC) resonances to achieve electromagnetically induced absorption (EIA) in an asymmetric metallic ring structure, we design a metal-based thermal metasurface with a near-unity emissivity (0.96) and a Q factor as high as 320 per simulations. Experimental validation yields an emissivity of 0.82 and a Q factor of 202, representing an approximately five-fold improvement in the experimentally measured Q factor compared to the state-of-the-art metal-based thermal metasurfaces. Our work offers a promising approach for developing efficient, narrow-band, directional thermal emitters with stable emission spectra across a wide temperature range.

The hyperfine structure of $^{229}$Th$^{3+}$ ions in the nuclear ground state is investigated via laser spectroscopy of trapped Th$^{3+}$ ions that are sympathetically cooled by laser-cooled $^{88}$Sr$^+$ ions in a linear Paul trap. The isotope shift to $^{230}$Th$^{3+}$ and the hyperfine constants for the magnetic dipole (A) and electric quadrupole (B) interactions for the 5F$_{5/2}$ and 6D$_{5/2}$ electronic states of $^{229}$Th$^{3+}$ are determined. These measurements provide nuclear moments of $^{229}$Th with reduced uncertainty and serve as a preparation for improved hyperfine spectroscopy of the 8.4 eV nuclear isomeric state in $^{229}$Th$^{3+}$ ions.

We present a new approach to controlling magnetization in gold nanoparticles using the Inverse Faraday Effect combined with Laguerre-Gauss beams carrying orbital angular momentum. By tailoring the tilt of isophase planes, we induce drift photocurrents that generate magnetic fields tilted by up to 25{\deg} relative to the beam axis. The magnetic orientation can be reversed by switching polarization chirality or the orbital angular momentum sign, and it can be rotated azimuthally by repositioning the particle, accessing any angle over 2{\pi} steradians. This unprecedented level of control extends all-optical magnetization to three-dimensional orientations, potentially at ultrafast timescales given the near-instantaneous nature of the Inverse Faraday Effect. Our results pave the way for advanced spin-based applications, from triggering spin waves in magnetic materials to designing next-generation magnetic memory and logic devices.

The educational value of a fully diagrammatic approach in a scientific field has never been explored. We present Quantum Picturalism (QPic), an entirely diagrammatic formalism for all of qubit quantum mechanics. This framework is particularly advantageous for young learners as a novel way to teach key concepts such as entanglement, measurement, and mixed-state quantum mechanics in a math-intensive subject. This eliminates traditional obstacles without compromising mathematical correctness - removing the need for matrices, vectors, tensors, complex numbers, and trigonometry as prerequisites to learning. Its significance lies in that a field as complex as Quantum Information Science and Technology (QIST), for which educational opportunities are typically exclusive to the university level and higher, can be introduced at high school level. In this study, we tested this hypothesis, examining whether QPic reduces cognitive load by lowering complex mathematical barriers while enhancing mental computation and conceptual understanding. The data was collected from an experiment conducted in 2023, whereby 54 high school students (aged 16-18) underwent 16 hours of training spread over eight weeks. The post-assessments illustrated promising outcomes in all three specific areas of focus: (1) whether QPic can alleviate technical barriers in learning QIST, (2) ensures that the content and teaching method are age appropriate, (3) increases confidence and motivation in science and STEM fields. There was a notable success rate in terms of teaching outcomes, with 82% of participants successfully passing an end-of-training exam and 48% achieving a distinction, indicating a high level of performance. The unique testing and training regime effectively reduced the technical barriers typically associated with traditional approaches, as hypothesized.

The novel neural networks show great potential in solving partial differential equations. For single-phase flow problems in subsurface porous media with high-contrast coefficients, the key is to develop neural operators with accurate reconstruction capability and strict adherence to physical laws. In this study, we proposed a hybrid two-stage framework that uses multiscale basis functions and physics-guided deep learning to solve the Darcy flow problem in high-contrast fractured porous media. In the first stage, a data-driven model is used to reconstruct the multiscale basis function based on the permeability field to achieve effective dimensionality reduction while preserving the necessary multiscale features. In the second stage, the physics-informed neural network, together with Transformer-based global information extractor is used to reconstruct the pressure field by integrating the physical constraints derived from the Darcy equation, ensuring consistency with the physical laws of the real world. The model was evaluated on datasets with different combinations of permeability and basis functions and performed well in terms of reconstruction accuracy. Specifically, the framework achieves R2 values above 0.9 in terms of basis function fitting and pressure reconstruction, and the residual indicator is on the order of $1\times 10^{-4}$. These results validate the ability of the proposed framework to achieve accurate reconstruction while maintaining physical consistency.

The internal structures of large organizations determine much of what occurs inside including the way in which tasks are performed, the workers that perform them, and the mobility of those workers within the organization. However, regarding this latter process, most of the theoretical and modeling approaches used to understand organizational worker mobility are highly stylized, using idealizations such as structureless organizations, indistinguishable workers, and a lack of social bonding of the workers. In this article, aided by a decade of precise, temporally resolved data of a large US government organization, we introduce a new model to describe organizations as composites of teams within which individuals perform specific tasks and where social connections develop. By tracking the personnel composition of organizational teams, we find that workers that change jobs are highly influenced by preferring to reunite with past co-workers. In this organization, 34\% of all moves lead to worker reunions, a percentage well-above expectation. We find that the greater the time workers spend together or the smaller the team they share both increase their likelihood to reunite, supporting the notion of increased familiarity and trust behind such reunions and the dominant role of social capital in the evolution of large organizations.

This comprehensive study investigates the intricate relationship between water scarcity and agricultural production, emphasizing its critical global significance. The research, through multidimensional analysis, investigates the various effects of water scarcity on crop productivity, especially the economic water scarcity (AEWS) which is the main factor of influence. The study stresses the possibility of vertical farming as a viable solution to the different kinds of water scarcity problems, hence, it emphasizes its function in the sustainable agricultural development. Although the study recognizes that some problems still remain, it also points out the necessity of more research to solve the issues of scalability and socio-economic implications. Moving forward, interdisciplinary collaboration and technological innovation are essential to achieving water-secure agriculture and societal resilience.

Developments in scalable quantum networks rely critically on optical quantum memories, which are key components enabling the storage of quantum information. These memories play a pivotal role for entanglement distribution and long-distance quantum communication, with remarkable advances achieved in this context. However, optical memories have broader applications, and their storage and buffering capabilities can benefit a wide range of future quantum technologies. Here we present the first demonstration of a cryptography protocol incorporating an intermediate quantum memory layer. Specifically, we implement Wiesner's unforgeable quantum money primitive with a storage step, rather than as an on-the-fly procedure. This protocol imposes stringent requirements on storage efficiency and noise level to reach a secure regime. We demonstrate the implementation with polarization encoding of weak coherent states of light and a high-efficiency cold-atom-based quantum memory, and validate the full scheme. Our results showcase a major capability, opening new avenues for quantum memory utilization and network functionalities.

Conventional autonomous quantum refrigerators rely on uncorrelated heat exchange between the working system and baths via two-body interactions enabled by single-photon transitions and positive-temperature work baths, inherently limiting their cooling performance. Here, we introduce distinct qutrit refrigerators that exploit correlated heat transfer via two-photon transitions with the hot and cold baths, yielding a genuine enhancement in performance over conventional qutrit refrigerators that employ uncorrelated heat transfer. These refrigerators achieve at least a twofold enhancement in cooling power and reliability compared to conventional counterparts. Moreover, we show that cooling power and reliability can be further enhanced simultaneously by several folds, even surpassing existing cooling limits, by utilizing a synthetic negative-temperature work bath. Such refrigerators can be realized by combining correlated heat transfer and synthetic work baths, which consist of a four-level system coupled to hot and cold baths and two conventional work baths via two independent two-photon transitions. Here, the composition of two work baths effectively creates a synthetic negative-temperature work bath under suitable parameter choices. Our results demonstrate that correlated heat transfers and baths with negative temperatures can yield thermodynamic advantages in quantum devices. Finally, we discuss the experimental feasibility of the proposed refrigerators across various existing platforms.

A dynamic concordia discors, a finely tuned equilibrium between opposing forces, is hypothesized to drive historical transformations. Similarly, a precise interplay of excitation and inhibition, the 80:20 ratio, is at the basis of the normal functionality of neural systems. In artificial neural networks, reinforcement learning allows for fine-tuning internal signed connections, optimizing adaptive responses to complex stimuli, and ensuring robust performance. At present, engineered structures of competing components are, however, largely unexplored, particularly because their emergent phases are closely linked with frustration mechanisms in the hosting network. In this context, the spin glass theory has shown how an apparently uncontrollable non-ergodic chaotic behavior arises from the complex interplay of competing interactions and frustration among units, leading to multiple metastable states preventing the system from exploring all accessible configurations over time. Here, we tackle the problem of disentangling topology and dynamics in systems with antagonistic interactions. We make use of the signed Laplacian operator to demonstrate how fundamental topological defects in lattices and networks percolate, shaping the geometrical arena and complex energy landscape of the system. This unveils novel, highly robust multistable phases and establishes deep connections with spin glasses when thermal noise is considered, providing a natural topological and algebraic description of their still-unknown set of pure states at zero temperature.

Polymeric wavelength shifters are of particular interest for large liquid argon detectors. Inspired by the success of polyethylene naphthalate (PEN), other new polymers exhibiting a similar type of excimer fluorescence were investigated. We report on the preliminary results of the first cryogenic wavelength shifting test of a solution-cast film of PVN, poly(2-vinyl naphthalene). Significant brittleness was identified as a factor potentially limiting the use of PVN. However, clear signs of wavelength shifting were observed, with the overall efficiency and time response comparable to those of PEN.

In this paper, I present SEMIDV - a compact semiconductor device simulator incorporating quantum effects. SEMIDV solves the Poisson-Drift-Diffusion equations for semiconductor devices and provides a user-friendly Python interface for scripting and data analysis. Localization landscape theory is introduced to provide quantum corrections to the Drift-Diffusion equation. This theory directly solves the ground state of the Schrodinger equation without further approximation, offering an efficient solution for quantum effect modeling. Additionally, a compact mobility model considering ballistic transport is developed to capture the ballistic length dependence of mobility and the velocity overshoot effect in short-channel devices. Finally, a study on a nanosheet FET using SEMIDV is conducted. I analyze the electrical characteristics of a state-of-the-art GAA/RibbonFET with a 6 nm gate length and discuss the effects of velocity overshoot and quantum confinement on currents and capacitances. A design for an ultra-short-channel transistor with a gate length down to 4.5 nm with a Vdd = 0.45 V is proposed to push the boundaries of integrated circuit technology further.

In this second chapter, we analyse transmission problems between a dielectric and a dispersive negative material. In the first part, we consider a transmission problem between two half-spaces, filled respectively by the vacuum and a Drude material, and separated by a planar interface. In this setting, we answer to the following question: does this medium satisfy a limiting amplitude principle? This principle defines the stationary regime as the large time asymptotic behavior of a system subject to a periodic excitation. In the second part, we consider the transmission problem of an infinite strip of Drude material embedded in the vacuum and analyse the existence and dispersive properties of guided waves. In both problems, our spectral analysis enlighten new and unusual physical phenomena for the considered transmission problems due to the presence of the dispersive negative material. In particular, we prove the existence of an interface resonance in the first part and the existence of slow light phenomena for guiding waves in the second part.

We have designed a Micro-Ring Resonator (MRR) based device that allows for the post-selection of high order NOON states via heralding. NOON states higher than $N=2$ cannot be generated deterministically. By tuning the coupling parameters of the device we can minimize the amplitudes of the 'accidental' states to maximize the probability of obtaining the NOON state upon a successful heralding event. Our device can produce a 3-photon NOON state output with 100% certainty upon a successful heralding detection, which occurs with probability $\frac{8}{27}$ for optimal tunable device parameters. A successful heralding event allows for non-destructive time of flight tracking of the NOON state thus establishing a significantly enhanced level of engineering control for integration of the NOON state into scalable systems for quantum sensing and metrology. We further discuss extensions of our technique to even higher NOON states having $N=4,5$.

We investigate in this chapter the mathematical models for electromagnetic wave propagation in dispersive isotropic passive linear media for which the dielectric permittivity $\varepsilon$ and magnetic permeability $\mu$ depend on the frequency. We emphasize the link between physical requirements and mathematical properties of the models. A particular attention is devoted to the notions of causality and passivity and its connection to the existence of Herglotz functions that determine the dispersion of the material. We consider successively the cases of the general passive media and the so-called local media for which $\varepsilon$ and $\mu$ are rational functions of the frequency. This leads us to analyse the important class of non dissipative and dissipative generalized Lorentz models. In particular, we discuss the connection between mathematical and physical properties of models through the notions of stability, energy conservation, dispersion and modal analyses, group and phase velocities and energy decay in dissipative systems.

This article describes the Wave Kinetic Solver \WavKinSns, a software developed in the Julia language for solving different types of wave kinetic equations in an efficient and modular manner. \WavKinS already solves the wave kinetic equation describing the interaction of waves in several physical systems, such as acoustic waves, Bose-Einstein condensates, internal gravity waves and many others. Thanks to the structures and routines already implemented in \WavKinSns, we expect that developing a new solver for a specific physical system to be straightforward. \WavKinS is an Open Source project distributed with EUPL-1.2 license agreement.

Recurrent Neural Network models have elucidated the interplay between structure and dynamics in biological neural networks, particularly the emergence of irregular and rhythmic activities in cortex. However, most studies have focused on networks with random or simple connectivity structures. Experimental observations find that high-order cortical connectivity patterns affect the temporal patterns of network activity, but a theory that relates such complex structure to network dynamics has yet to be developed. Here, we show that third- and higher-order cyclic correlations in synaptic connectivities greatly impact neuronal dynamics. Specifically, strong cyclic correlations in a network suppress chaotic dynamics, and promote oscillatory or fixed activity. The change in dynamics is related to the form of the unstable eigenvalues of the random connectivity matrix. A phase transition from chaotic to fixed or oscillatory activity coincides with the development of a cusp at the leading edge of the eigenvalue support. We also relate the dimensions of activity to the network structure.

Improving the representation of precipitation in Earth system models (ESMs) is critical for assessing the impacts of climate change and especially of extreme events like floods and droughts. In existing ESMs, precipitation is not resolved explicitly, but represented by parameterizations. These typically rely on resolving approximated but computationally expensive column-based physics, not accounting for interactions between locations. They struggle to capture fine-scale precipitation processes and introduce significant biases. We present a novel approach, based on generative machine learning, which integrates a conditional diffusion model with a UNet architecture to generate accurate, high-resolution (0.25{\deg}) global daily precipitation fields from a small set of prognostic atmospheric variables. Unlike traditional parameterizations, our framework efficiently produces ensemble predictions, capturing uncertainties in precipitation, and does not require fine-tuning by hand. We train our model on the ERA5 reanalysis and present a method that allows us to apply it to arbitrary ESM data, enabling fast generation of probabilistic forecasts and climate scenarios. By leveraging interactions between global prognostic variables, our approach provides an alternative parameterization scheme that mitigates biases present in the ESM precipitation while maintaining consistency with its large-scale (annual) trends. This work demonstrates that complex precipitation patterns can be learned directly from large-scale atmospheric variables, offering a computationally efficient alternative to conventional schemes.

Transmission spectroscopy has provided unprecedented insight into the makeup of exoplanet atmospheres. A transmission spectrum contains contributions from a planet's morning and evening limbs, which can differ in temperature, composition and aerosol properties due to atmospheric circulation. While high-resolution ground-based observations have identified limb asymmetry in several ultra-hot/hot exoplanets, space-based studies of limb asymmetry are still in their early stages. The prevalence of limb asymmetry across a broad range of exoplanets remains largely unexplored. We conduct a comparative analysis of retrievals on transmission spectra, including traditional 1D approaches and four 2D models that account for limb asymmetry. Two of these 2D models include our newly proposed dynamical constraints derived from shallow-water simulations to provide physically-motivated temperature differences between limbs. Our analysis of WASP-39 b using JWST observations and previous combined datasets (HST, VLT, and Spitzer) strongly favors 2D retrievals over traditional 1D approaches, confirming significant limb asymmetry in this hot Jupiter. Within our 2D framework, unconstrained models recover larger temperature contrasts than dynamically-constrained models, with improved fits to specific spectral features, although Bayesian evidence cannot definitively distinguish between these 2D approaches. Our results support the presence of homogeneous C/O in both the morning and evening atmospheres, but with temperature differences leading to variations in clouds and hazes. Using this treatment, we can study a larger sample of hot Jupiters to gain insights into atmospheric limb asymmetries on these planets.

The spherical cow approximation is widely used in the literature, but is rarely justified. Here, I propose several schemes for extending the spherical cow approximation to a full multipole expansion, in which the spherical cow is simply the first term. This allows for the computation of bovine potentials and interactions beyond spherical symmetry, and also provides a scheme for defining the geometry of the cow itself at higher multipole moments. This is especially important for the treatment of physical processes that are suppressed by spherical symmetry, such as the spindown of a rotating cow due to the emission of gravitational waves. I demonstrate the computation of multipole coefficients for a benchmark cow, and illustrate the applicability of the multipolar cow to several important problems.

Creep failure under high temperatures is a complex multiscale and multi-mechanism issue involving inherent microstructural randomness. To investigate the effect of microstructures on the uniaxial/multiaxial creep failure, a dual-scale stochastic analysis framework is established to introduce the grain boundary (GB) characteristics into the macroscopic analysis. The nickel-base superalloy Inconel 617 is considered in this study. Firstly, the damage mechanisms of GBs are investigated based on the crystal plasticity finite element (CPFE) method and cohesive zone model (CZM). Subsequently, based on the obtained GB damage evolution, a novel Monte Carlo (MC) approach is proposed to establish the relationship between the GB orientation and area distribution and macroscopic creep damage. Finally, a dual-scale stochastic multiaxial creep damage model is established to incorporate the influence of the random GB orientation and area distribution. With the numerical application of the proposed creep damage model, the random initiation and growth of creep cracks in the uniaxial tensile specimen and the pressurized tube are captured and analyzed. The proposed stochastic framework effectively considers the inherent randomness introduced by GB characteristics and efficiently realizes full-field multiscale calculations. It also shows its potential applications in safety evaluation and life prediction of creep components and structures under high temperatures.

Vicsek Model is widely used in simulations of dry active matter. We re-examined two typical phase transitions in the original Vicsek model by using the velocity correlation length. One is the noise-driven disordered-to-ordered phase transition driven by noise, which was initially considered as a second-order transition (continuous transition), but was later demonstrated by Chate's detailed study to be a first-order transition. The other one is the disordered-to-ordered phase transition driven by average distance between particles, which is a second-order transition and satisfies the hyper-scaling relation of continuous transitions. We have discovered the change of correlation length during transition indicates a critical point in continuous transition while not in the discontinuous situation. We have also provided a method to classify phase transitions in active matter systems by using the correlation length and summarized previous work within the same framework. Finally, we end up with a potential application in experiments of bactirial swarms and robotic swarms. We hope our work paves the way for both theory and experiment development of active matter.

Planar rotors can be realized by confining molecular ions or charged nanoparticles together with atomic ions in a Paul trap. We study the case of molecular ions or charged nanoparticles that have an electric dipole moment which couples to modes of the common vibrational motion in the trap. We calculate the strength of the coupling with specific vibrational modes for rotor masses ranging from $10^2$ atomic units, as typical for diatomic molecules, to $10^{6}\,$ atomic units, corresponding to nanoclusters. Either, the coupling manifests as a resonant energy exchange between rotational states and one of ion crystal vibrational modes. Or, in the off-resonant case, the dipole-phonon coupling results in energy shifts. In both cases we discuss how the effect may be experimentally detected using sideband-resolved laser spectroscopy and measurements of decoherence.

We introduce a general procedure for computing higher-order moments of correlation functions in open quantum systems, extending the scope of our recent work on Memory Kernel Coupling Theory (MKCT) [W. Liu, Y. Su, Y. Wang, and W. Dou, arXiv:2407.01923 (2024)]. This approach is demonstrated for arbitrary system-bath coupling that can be expressed as polynomial, $H_{SB} = \hat{V} (\alpha_0 + \alpha_1 \hat{q} + \alpha_2 \hat{q}^2+ \dots)$, where we show that the recursive commutators of a system operator obey a universal hierarchy. Exploiting this structure, the higher-order moments are obtained by evaluating the expectation values of the system and bath operators separately, with bath expectation values derived from the derivatives of a generating function. We further apply MKCT to compute the dipole autocorrelation function for the spin-boson model with both linear and quadratic coupling, achieving agreement with the hierarchical equations of motion approach. Our findings suggest a promising path toward accurate dynamics for complex open quantum systems.

Recent observations of the solar wind ions by the SPAN-I instruments on board the Parker Solar Probe (PSP) spacecraft at solar perihelia (Encounters) 4 and closer find ample evidence of complex anisotropic non-Maxwellian velocity distributions that consist of core, beam, and `hammerhead' (i.e., anisotropic beam) populations. The proton core populations are anisotropic, with T_perp/T||>1, and the beams have super-Alfvenic speed relative to the core (we provide an example from Encounter 17). The alpha-particle population show similar features as the protons. These unstable VDFs are associated with enhanced, right-hand (RH) and left-hand (LH) polarized ion-scale kinetic wave activity, detected by the FIELDS instrument. Motivated by PSP observations, we employ nonlinear hybrid models to investigate the evolution of the anisotropic hot-beam VDFs and model the growth and the nonlinear stage of ion kinetic instabilities in several linearly unstable cases. The models are initialized with ion VDFs motivated by the observational parameters. We find rapidly growing (in terms of proton gyroperiods) combined ion-cyclotron (IC) and magnetosonic (MS) instabilities, which produce LH and RH ion-scale wave spectra, respectively. The modeled ion VDFs in the nonlinear stage of the evolution are qualitatively in agreement with PSP observations of the anisotropic core and `hammerhead' velocity distributions, quantifying the effect of the ion kinetic instabilities on wind plasma heating close to the Sun. We conclude that the wave-particle interactions play an important role in the energy transfer between the magnetic energy (waves) and random particle motion leading to anisotropic solar wind plasma heating.

Using molecular dynamics simulations, we investigate the aggregation behavior of neutral stiff (rod-like) and flexible polymer chains mediated by attractive crowders. Attractive crowders serve as bridging agents, inducing aggregation through effective intra-polymer attractions. The critical monomer-crowder attraction strength ($\epsilon_{mc}^*$) required for aggregation differs notably between rigid rods and flexible polymers. Interestingly, this aggregation threshold closely matches the critical attraction required for the extended-to-collapsed (coil-globule) transition of a single flexible polymer chain, suggesting a fundamental connection between single-chain collapse and multi-chain aggregation. Furthermore, we demonstrate that $\epsilon_{mc}^*$ decreases with increasing system density and larger crowder sizes, highlighting the synergistic roles of crowding effects and crowder dimensions. Aggregate morphologies exhibit strong dependence on polymer flexibility: rigid rods predominantly form elongated cylindrical bundles, whereas flexible polymers aggregate into compact spherical clusters. These findings provide comprehensive insights into how bridging interactions driven by attractive crowders regulate polymer aggregation dynamics and morphologies, emphasizing the importance of polymer rigidity, crowder size, and system density.

Context: The paradigm of convection in solar-like stars is questioned based on recent solar observations. Aims: The primary aim is to study the effects of surface-driven entropy rain on convection zone structure and flows. Methods: Simulations of compressible convection in Cartesian geometry with non-uniform surface cooling are used. The cooling profile includes localized cool patches that drive deeply penetrating plumes. Results are compared with cases with uniform cooling. Results: Sufficiently strong surface driving leads to strong non-locality and a largely subadiabatic convectively mixed layer. In such cases the net convective energy transport is done almost solely by the downflows. The spatial scale of flows decreases with increasing number of cooling patches for the vertical flows whereas the horizontal flows still peak at large scales. Conclusions: To reach the plume-dominated regime with a predominantly subadiabatic bulk of the convection zone requires significantly more efficient entropy rain than what is realized in simulations with uniform cooling. It is plausible that this regime is realized in the Sun but that it occurs on scales smaller than those resolved currently. Current results show that entropy rain can lead to largely mildly subadiabatic convection zone, whereas its effects for the scale of convection are more subtle.

The simulation of sand--water mixtures requires capturing the stochastic behavior of individual sand particles within a uniform, continuous fluid medium, such as the characteristic of migration, deposition, and plugging across various scenarios. In this paper, we introduce a Granule-in-Cell (GIC) method for simulating such sand--water interaction. We leverage the Discrete Element Method (DEM) to capture the fine-scale details of individual granules and the Particle-in-Cell (PIC) method for its continuous spatial representation and particle-based structure for density projection. To combine these two frameworks, we treat granules as macroscopic transport flow rather than solid boundaries for the fluid. This bidirectional coupling allows our model to accommodate a range of interphase forces with different discretization schemes, resulting in a more realistic simulation with fully respect to the mass conservation equation. Experimental results demonstrate the effectiveness of our method in simulating complex sand--water interactions, while maintaining volume consistency. Notably, in the dam-breaking experiment, our simulation uniquely captures the distinct physical properties of sand under varying infiltration degree within a single scenario. Our work advances the state of the art in granule--fluid simulation, offering a unified framework that bridges mesoscopic and macroscopic dynamics.

Flat bands (FBs) play a crucial role in condensed matter physics, offering an ideal platform to study strong correlation effects and enabling applications in diffraction-free photonics and quantum devices. However, the study and application of FB properties are susceptible to interference from dispersive bands. Here, we explore the impact of bond dissipation on systems hosting both flat and dispersive bands by calculating the steady-state density matrix. We demonstrate that bond dissipation can drive particles from dispersive bands into FBs and establish the general conditions for this phenomenon to occur. Our results demonstrate that dissipation can facilitate FB preparation, property measurement, and utilization. This opens a new avenue for exploring FB physics in open quantum systems, with potential implications for strongly correlated physics.

Positron Emission Tomography (PET) is a vital molecular imaging tool widely used in medical diagnosis and treatment evaluation. Traditional PET systems typically rely on complete detector rings to achieve full angular coverage for uniform and statistically robust sampling of coincidence events. However, incomplete-ring PET scanners have emerged in various scenarios due to hardware failures, cost constraints, or specific clinical needs. In such cases, conventional reconstruction algorithms often suffer from performance degradation due to reduced data completeness and geometric inconsistencies. This thesis proposes a coarse-to-fine reconstruction framework for incomplete-ring PET scanners. The framework first employs an Attention U-Net model to recover complete sinograms from incomplete ones, then uses the OSEM algorithm for preliminary reconstruction, and finally applies a two-stage architecture comprising a Coarse Prediction Module (CPM) and an Iterative Refinement Module (IRM) for fine reconstruction. Our approach utilizes neighboring axial slices and spectral transform features as auxiliary guidance at the input level to ensure spatial and frequency domain consistency, and integrates a contrastive diffusion strategy at the output level to improve correspondence between low-quality PET inputs and refined PET outputs. Experimental results on public and in-house brain PET datasets demonstrate that the proposed method significantly outperforms existing approaches in metrics such as PSNR (35.6421 dB) and SSIM (0.9588), successfully preserving key anatomical structures and tracer distribution features, thus providing an effective solution for incomplete-ring PET imaging.

Bioelectrical interfaces represent a significant evolution in the intersection of nanotechnology and biophysics, offering new strategies for probing and influencing cellular processes. These systems capitalize on the subtle but powerful electric fields within living matter, potentially enabling applications beyond cellular excitability, ranging from targeted cancer therapies to interventions in genetic mechanisms and aging. This perspective article envisions the translation, development and application of next-generation solid-state bioelectrical interfaces and their transformative impact across several critical areas of medical research.

Orthorhombic II-IV nitride semiconductors offer an expanded and more tunable material set with unique properties, while maintaining close compatibility with the wurtzite crystal structure of the III-nitrides. In particular, MgSiN2, a II-IV nitride closely lattice matched to GaN and AlN has a band gap suitable for photonic applications in the UV-C wavelength region. MgSiN2 is also a promising candidate to exhibit ferroelectricity, which has only been observed in very few nitride materials. This study builds on our previous work on the metal-organic chemical vapor deposition (MOCVD) of MgSiN2 thin films grown on GaN-on-sapphire and c-plane sapphire substrates by exploring higher growth temperature windows, resulting in higher crystalline quality and improved interfaces. Correlations between the growth conditions (Mg:Si precursor molar flow rate ratio, reactor pressure, and growth temperatures from 900C to 960C) and the resultant film quality are investigated for films grown on GaN-on-sapphire. High-resolution transmission electron microscopy (HR-TEM) reveals high-quality orthorhombic single-crystal MgSiN2, confirming successful epitaxial growth on GaN. Optical transmittance measurements indicate the direct band gap is 6.34-6.36 eV and indirect band gap is 5.77-5.81 eV, affirming the realization of an ultrawide-band gap II-IV nitride semiconductor that is structurally compatible with existing III-nitride device platforms.

Solar flares are major space weather events that result from the explosive conversion of stored magnetic energy into bulk motion, plasma heating, and particle acceleration. While the standard flare model has proven highly successful in explaining key morphological features of flare observations, many aspects of the energy release are not yet understood. In particular, the turbulent three-dimensional structure of the flare current sheet is thought to play an important role in fast reconnection, particle acceleration, and bursty dynamics. Although direct diagnosis of the magnetic field dynamics in the corona remains highly challenging, rich information may be gleaned from flare ribbons, which represent the chromospheric imprints of reconnection in the corona. Intriguingly, recent solar imaging observations have revealed a diversity of fine structure in flare ribbons that hints at corresponding complexity in the reconnection region. We present high-resolution three-dimensional MHD simulations of an eruptive flare and describe our efforts to interpret fine-scale ribbon features in terms of the current sheet dynamics. In our model, the current sheet is characterized by many coherent magnetic structures known as plasmoids. We derive a model analogue for ribbons by generating a time series of field-line length maps (L-maps) and identifying abrupt shortenings as flare reconnection events. We thereby demonstrate that plasmoids imprint transient 'spirals' along the analogue of the ribbon front, with a morphology consistent with observed fine structure. We discuss the implications of these results for interpreting SolO, IRIS, and DKIST observations of explosive flare energy release.

Utilizing ab initio simulations, we study the spin-dependent electronic transport characteristics within Fe$_4$GeTe$_2$-based van der Waals heterostructures. The electronic density of states for both free-standing and device-configured Fe$_4$GeTe$_2$ (F4GT) confirms its ferromagnetic metallic nature and reveals a weak interface interaction between F4GT and PtTe$_2$ electrodes, enabling efficient spin filtering. We observe a decrease in the magnetic anisotropy energy of F4GT in the device configuration, indicating reduced stability of magnetic moments and heightened sensitivity to external conditions. The transmission eigenstates of PtTe$_2$/ monolayer F4GT/PtTe$_2$ heterostructures demonstrate interference patterns affected by relative phases and localization, notably different in the spin-up and spin-down channels. The ballistic transport through a double-layer F4GT with a ferromagnetic configuration sandwiched between two PtTe$_2$ electrodes is predicted to exhibit an impressive spin polarization of 97$\%$ with spin-up electrons exhibiting higher transmission probability than spin-down electrons. Moreover, we investigate the spin transport properties of Fe$_4$GeTe$_2$/GaTe/Fe$_4$GeTe$_2$ van der Waals heterostructures sandwiched between PtTe$_2$ electrodes to explore their potential as magnetic tunnel junctions (MTJs) in spintronic devices. The inclusion of GaTe as a 2D semiconducting spacer between F4GT layers results in a tunnel magnetoresistance (TMR) of 487$\%$ at low bias and decreases with increasing bias voltage. In general, our findings underscore the potential of F4GT / GaTe / F4GT heterostructures to advance spintronic devices based on van der Waals materials.

Over the course of the past decade, advances in the radial velocity and transit techniques have enabled the detection of rocky exoplanets in the habitable zones of nearby stars. Future observations with novel methods are required to characterize this sample of planets, especially those that are non-transiting. One proposed method is the Planetary Infrared Excess (PIE) technique, which would enable the characterization of non-transiting planets by measuring the excess infrared flux from the planet relative to the star's spectral energy distribution. In this work, we predict the efficacy of future observations using the PIE technique by potential future observatories such as the MIRECLE mission concept. To do so, we conduct a broad suite of 21 General Circulation Model (GCM) simulations with ExoCAM of seven nearby habitable zone targets for three choices of atmospheric composition with varying partial pressure of CO$_2$. We then construct thermal phase curves and emission spectra by post-processing our ExoCAM GCM simulations with the Planetary Spectrum Generator (PSG). We find that all cases have distinguishable carbon dioxide and water features assuming a 90$^\circ$ orbital inclination. Notably, we predict that CO$_2$ is potentially detectable at 15 $\mu\mathrm{m}$ with MIRECLE for at least four nearby known non-transiting rocky planet candidate targets in the habitable zone: Proxima Cenaturi b, GJ 1061 d, GJ 1002 b, and Teegarden's Star c. Our ExoCAM GCMs and PSG post-processing demonstrate the potential to observationally characterize nearby non-transiting rocky planets and better constrain the potential for habitability in our Solar neighborhood.