CUPID, the CUORE Upgrade with Particle Identification, is a next-generation experiment to search for neutrinoless double beta decay ($0\nu\beta\beta$) and other rare events using enriched Li$_2$$^{100}$MoO$_4$ scintillating bolometers. It will be hosted by the CUORE cryostat located at the Laboratori Nazionali del Gran Sasso in Italy. The main physics goal of CUPID is to search for $0\nu\beta\beta$\ of $^{100}$Mo with a discovery sensitivity covering the full neutrino mass regime in the inverted ordering scenario, as well as the portion of the normal ordering regime with lightest neutrino mass larger than 10 meV. With a conservative background index of 10$^{-4}$ cnts/(keV$\cdot$kg$\cdot$yr), 240 kg isotope mass, 5 keV FWHM energy resolution and 10 live-years of data taking, CUPID will have a 90\% C.L. half-life exclusion sensitivity of 1.8 $\cdot$ 10$^{27}$ yr, corresponding to an effective Majorana neutrino mass ($m_{\beta\beta}$) sensitivity of 9--15 meV, and a $3\sigma$ discovery sensitivity of 1 $\cdot$ 10$^{27}$ yr, corresponding to an $m_{\beta\beta}$ range of 12--21 meV.

The notion of sub-optimal Oswatitsch solutions is introduced in order to systematically conduct a tradeoff between total pressure recovery (TPR) and intake drag coefficient (CDi) for supersonic intakes. It is shown that the Oswatitsch-optimal TPR for a biconic intake may be enhanced by adding a conical flare which modifies the terminal normal shock into a novel Lambda shock structure. The optimization problem is formulated along the lines of Axiomatic Design Theory with the conical angle pair and the cowl fineness ratio as the two design parameters. Reynolds-averaged Navier-Stokes (RANS) simulations are performed to iteratively arrive at the optimal solutions, with and without an intake length constraint, for a fixed value of the intake mass flow rate. The results are used to generate the Pareto front in the space of the objective functions, which yields the set of solutions between which TPR and Cdi may be traded off for one another. Additionally, an off-Oswatitsch solution, where only the second cone angle is altered from its optimal Oswatitsch value, is obtained and is compared with the sub-optimal Oswatitsch solutions that form the Pareto front.

We present a new approach to report in the Section 4 of BIPM Circular T daily values of the offset between UTC and the predictions of UTC broadcast by the GNSS, this quantity we name bUTC_GNSS. In this approach, the determination of UTC - bUTC_GNSS is based on data collected by several multi-GNSS stations in selected time laboratories worldwide. Test computations over a 7-month period from July 2022 to January 2023 show that the offset between UTC and bUTC_GNSS was between 30 and 50 ns for GLONASS, between 5 and 20 ns for BeiDou, and between -5 and +5 ns for GPS and Galileo. We derive the uncertainty on the reported values, which is 4.1 ns for BeiDou and GPS, 3.7 ns for Galileo and 6.6 ns for GLONASS and show that, over the test period, the reported values of UTC - bUTC_GNSS and the solutions obtained from each multi-GNSS station are all consistent within the 1-sigma uncertainties.

Diverse organisms exploit the geomagnetic field (GMF) for migration. Migrating birds employ an intrinsically quantum mechanical mechanism for detecting the geomagnetic field: absorption of a blue photon generates a radical pair whose two electrons precess at different rates in the magnetic field, thereby sensitizing cells to the direction of the GMF. In this work, using an in vitro injury model, we discovered a quantum-based mechanism of cellular migration. Specifically, we show that migrating cells detect the GMF via an optically activated, electron spin-based mechanism. Cell injury provokes acute emission of blue photons, and these photons sensitize muscle progenitor cells to the magnetic field. We show that the magnetosensitivity of muscle progenitor cells is (a) activated by blue light, but not by green or red light, and (b) disrupted by the application of an oscillatory field at the frequency corresponding to the energy of the electron-spin/magnetic field interaction. A comprehensive analysis of protein expression reveals that the ability of blue photons to promote cell motility is mediated by activation of calmodulin calcium sensors. Collectively, these data suggest that cells possess a light-dependent magnetic compass driven by electron spin dynamics.

Heating electrification presents opportunities and challenges for energy affordability. Without careful planning and policy, the costs of natural gas service will be borne by a shrinking customer base, driving up expenses for those who are left behind. This affordability issue is worsened by new fossil fuel investments, which risk locking communities into carbon-intensive infrastructure. Here, we introduce a framework to quantify the distributional effects of natural gas phasedown on energy affordability, integrating detailed household data with utility financial and planning documents. Applying our framework first to Massachusetts and then nationwide, we show that vulnerable communities face disproportionate affordability risks in building energy transitions. Households that do not electrify may bear up to 50% higher energy costs over the next decade. Targeted electrification may help to alleviate immediate energy burdens, but household heating transitions will ultimately require coordinated, neighborhood-scale strategies that consider the high fixed costs of legacy infrastructure.

Wave breaking is a critical process in the upper ocean: an energy sink for the surface wave field and a source for turbulence in the ocean surface boundary layer. We apply a novel multi-layer numerical solver resolving upper-ocean dynamics over scales from O(50cm) to O(1km), including a broad-banded wave field and wave breaking. The present numerical study isolates the effect of wave breaking and allows us to study the surface layer in wave-influenced and wave-breaking-dominated regimes. Following our previous work showing wave breaking statistics in agreement with field observations, we extend the analysis to underwater breaking-induced turbulence and related dissipation (in freely decaying conditions). We observe a rich field of vorticity resulting from the turbulence generation by breaking waves. We discuss the vertical profiles of dissipation rate which are compared with field observations, and propose an empirical universal shape function. Good agreement is found, further demonstrating that wave breaking can dominate turbulence generation in the near-surface layer. We examine the dissipation from different angles: the global dissipation of the wave field computed from the decaying wave field, the spectral dissipation from the fifth moment of breaking front distribution, and a turbulence dissipation estimated from the underwater strain rate tensor. Finally, we consider how these different estimates can be understood as part of a coherent framework.

Understanding the statistical laws governing citation dynamics remains a fundamental challenge in network theory and the science of science. Citation networks typically exhibit in-degree distributions well approximated by log-normal distributions, yet they also display power-law behaviour in the high-citation regime, presenting an apparent contradiction that lacks a unified explanation. Here, we identify a previously unrecognised phenomenon: the variance of the logarithm of citation counts per unit time follows a power law with respect to time since publication, scaling as $t^{H}$. This discovery introduces a new challenge while simultaneously offering a crucial clue to resolving this discrepancy. We develop a stochastic model in which latent attention to publications evolves through a memory-driven process incorporating cumulative advantage. This process is characterised by the Hurst parameter $H$, derived from fractional Brownian motion, and volatility. Our framework reconciles this contradiction by demonstrating that anti-persistent fluctuations ($H<\tfrac{1}{2}$) give rise to log-normal citation distributions, whereas persistent dynamics ($H>\tfrac{1}{2}$) favour heavy-tailed power laws. Numerical simulations confirm our model's explanatory and predictive power, interpolating between log-normal and power-law distributions while reproducing the $t^{H}$ law. Empirical analysis of arXiv e-prints further supports our theory, revealing an intrinsically anti-persistent nature with an upper bound of approximately $H=0.13$. By linking memory effects and stochastic fluctuations to broader network dynamics, our findings provide a unifying framework for understanding the evolution of collective attention in science and other attention-driven processes.

In this study, we investigate the influence of humidity on the performance of various non-resistive Micro Pattern Gaseous Detectors, such as GEM, Thick-GEM, and Micromegas, operated with Ar-CO$_2$ (90-10) gas mixture. The water content is introduced in a range of $0-5000~ppm_{\mathrm{V}}$. It is observed that the presence of increased humidity does not significantly degrade any of the studied performance criteria. On the contrary, our measurements suggest an improvement in discharge stability with increasing humidity levels at the highest gains and fields. No significant difference is observed at the lower gains, indicating that humidity helps to reduce the rate of spurious discharges related to electrode defects or charging-up of the insulating layers. We conclude that adding a small amount of water to the gas mixture may be beneficial for the stable operation of an MPGD.

In this work, we introduce the Equivariance Seeker Model (ESM), a data-driven method for discovering the underlying finite equivariant symmetry group of an arbitrary function. ESM achieves this by optimizing a loss function that balances equivariance preservation with the penalization of redundant solutions, ensuring the complete and accurate identification of all symmetry transformations. We apply this framework specifically to dynamical systems, identifying their symmetry groups directly from observed trajectory data. To demonstrate its versatility, we test ESM on multiple systems in two distinct scenarios: (i) when the governing equations are known theoretically and (ii) when they are unknown, and the equivariance finding relies solely on observed data. The latter case highlights ESM's fully data-driven capability, as it requires no prior knowledge of the system's equations to operate.

We present results of one-dimensional collisionless simulations of plasma turbulence and related anomalous electron current of the Electron Cyclotron Drift Instability (ECDI). Our highly resolved, long-term simulations of xenon plasma in the magnetic field performed with the WarpX particle-in-cell (PIC) code show several intermediate non-linear stages before the system enters a stationary state with significantly increased electron temperature and a finite level of energy in the electrostatic fluctuations. In early and intermediate non-linear stages, the fluctuations are driven by the electron cyclotron resonances gradually shifting from higher ($m>1$) modes to the fundamental $m=1$ resonance. Enhanced resonant growth is observed from the point when the cyclotron $m=1$ mode coincides with the most unstable ion-acoustic mode. In the final stage, the anomalous electron current existing in intermediate stages is quenched to zero. Following this quenching, our simulations reveal a transition from ECDI-driven dynamics to saturated ion-acoustic turbulence. The modification of the electron and ion distribution functions and their roles in the non-linear developments and saturation of the instability are analyzed at different non-linear stages. The non-linear development of ECDI driven by the $\mathbf{E} \times \mathbf{B}$ electron drift from the applied current and the ECDI driven by the ion beam perpendicular to the magnetic field are compared and characterized as two perspectives of the instability, observed through different Doppler-shifted frames. An extension of this work incorporating full the dynamics of magnetized ions for ECDI driven by a hydrogen ion beam is shown to develop full beam inversion, with the periodic bursts of growth-saturation cycles of ECDI.

Mid-infrared spectroscopy offers powerful label-free molecular analysis capabilities but faces significant challenges when analyzing complex biological samples. Here, we present a transformative surface-enhanced infrared absorption spectroscopy (SEIRAS) platform that overcomes fundamental limitations through key innovations. First, we demonstrate high-throughput wafer-scale fabrication of mid-IR plasmonic micro-hole-array (MHA) metasurfaces on free-standing silicon nitride membranes, yielding approximately 400 sensor chips per 6-inch wafer. Second, our gradient MHA metasurface design supports spectrally cascaded plasmonic modes, generating over 400 sharp resonance peaks across the 1200-2000 cm-1 fingerprint region. This approach enables comprehensive molecular fingerprinting using simple imaging optics in transmission mode. Third, we validate our SEIRAS platform using a model polymer system and clinical peritoneal fluid samples from ovarian cancer patients, demonstrating its capability to resolve complex molecular signatures in real biological specimens. The platform's dense spectral coverage ensures optimal on-resonance enhancement across the broad fingerprint region, revealing previously obscured vibrational bands that conventional IR spectroscopy cannot distinguish. By combining high-throughput fabrication with simplified optical readout and the capability to analyze complex biological samples, our work establishes a foundation for translating SEIRAS technology into practical biomedical applications.

This study applies the high-fidelity spectral/hp element method using the open-source Nektar++ framework to simulate the unsteady, transitional flow around complex 3D geometries representative of the Formula 1 industry. This study extends the work on a previously investigated industrial benchmark, the Imperial Front Wing (IFW), derived from the McLaren MP4-17D race car's front wing and endplate design. A combined configuration of the IFW with a wheel in contact with a moving ground in a rolling state is considered, representing the first instance of such a configuration being simulated using higher-order methods. The rolling wheel combined with the IFW (IFW-W) provides the most realistic industrial configuration until now. The spectral/hp element method is applied to this test case to solve the incompressible Navier-Stokes equations, simulating the flow at a Reynolds number of $\mathbf{2.2 \times 10^5}$. Time-averaged results from the unsteady simulation are compared to experimental Particle Image Velocimetry (PIV) data to assess the model's fidelity, offering insights into its reliability for accurately representing key flow characteristics. This research addresses the challenges and requisites associated with achieving diverse levels of flow resolution using the under-resolved DNS/implicit LES approach.

The development of quantitative methods for characterizing molecular chirality can provide an important tool for studying chirality induced phenomena in molecular systems. Significant progress has been made in recent years toward understanding the chirality of molecular normal vibrational modes, mostly focusing on vibrations of helical molecular structures. In the present study, we examine the applicability two methodologies previously used for helical structures for the quantification of the chirality of molecular normal modes across a range of small, not necessarily helical, molecules. The first approach involves the application of the Continuous Chirality Measure (CCM) to each normal mode by associating the mode with a structure formed by imposing the corresponding motion about a common origin. The second approach assigns to each normal mode a pseudoscalar defined as the product of atomic linear and angular momentum summed over all atoms. In particular, using the CCM also as a measure of the chirality of the underlying molecular structure, we establish the existence of correlation between the chirality of molecular normal modes and that of the underlying molecular structure. Furthermore, we find that normal modes associated with different frequency ranges of the molecular vibrational spectrum exhibit distinct handedness behavior.

Colloidal quantum dots (QDs) are an attractive medium for nonlinear optics and deterministic heterogeneous integration with photonic devices. Their intrinsic nonlinearities can be strengthened further by coupling QDs to low mode-volume photonic nanocavities, enabling low-power, on-chip nonlinear optics. In this paper, we demonstrated cavity-enhanced second harmonic generation via integration of colossal QDs with a silicon nitride nanobeam cavity. By pumping the cavity-QD system with an ultrafast pulsed laser, we observed a strong second harmonic generation from the cavity-coupled QD, and we estimate an enhancement factor of ~3,040. Our work, coupled with previously reported deterministic positioning of colossal QDs, can enable a scalable QD-cavity platform for low-power nonlinear optics.

We simulate thermal convection in a two-dimensional square box using the no-slip condition on all boundaries, and isothermal bottom and top walls and adiabatic sidewalls. We choose 0.1 and 1 for the Prandtl number and vary the Rayleigh number between $10^6$ and $10^{12}$. We particularly study the temporal evolution of integral transport quantities towards their steady states. Perhaps not surprisingly, the velocity field evolves more slowly than the thermal field. Its steady state is nominal in the sense that large-amplitude low-frequency oscillations persist around plausible averages. We study these oscillation characteristics.

The assembly of arbitrary 3D structures using nano- to micron-scale colloidal building blocks has broad applications in photonics, electronics, and biology. Combining optical tweezers (OT) with two-photon polymerization (TPP) enables 3D selective tweezing and immobilization of colloids (STIC) without requiring specialized particle functionalization. Unlike traditional approaches, we demonstrate that high-repetition-rate femtosecond laser pulses, rather than continuous-wave lasers, allow optical tweezing at intensities below the TPP threshold. This dual functionality enables both OT and TPP using a single laser source. This platform was applied on S. aureus cells into desired configurations, highlighting its potential for advanced cell patterning. TPP was further used to fabricate intricate 3D microstructures, including microgrooves and cylindrical constructs, facilitating spatially resolved studies of single-cell dynamics and interactions. This work highlights the potential of STIC as a versatile tool for advanced biological applications, including tissue engineering and microbial research.

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.

The high cost of sequential time integration is one major constraint that limits the speedup of a time-parallel algorithm like the Parareal algorithm due to the difficulty of coarsening time steps in a stiff numerical problem. To address this challenge, we develop a parallel-in-time approach based on the Parareal algorithm, in which we construct a novel coarse solver using a data-driven method based on Dynamic Mode Decomposition in place of a classic time marching scheme. The proposed solver computes an approximation of the solution using two numerical schemes of different accuracies in parallel, and apply High-Order Dynamic Mode Decomposition (HODMD) to reduce the cost of sequential computations. Compared to the original Parareal algorithm, the proposed approach allows for the construction of low-cost coarse solvers for many complicated stiff problems. We demonstrate through several numerical examples in fluid dynamics that the proposed method can effectively reduce the serial computation cost and improve the parallel speedup of long-time simulations which are hard to accelerate using the original Parareal algorithm.

Groups of cells, including clusters of cancerous cells, multicellular organisms, and developing organs, may both grow and break apart. What physical factors control these fractures? In these processes, what sets the eventual size of clusters? We develop a framework for understanding cell clusters that can fragment due to cell motility using an active particle model. We compute analytically how the break rate of cell-cell junctions depends on cell speed, cell persistence, and cell-cell junction properties. Next, we find the cluster size distributions, which differ depending on whether all cells can divide or only the cells on the edge of the cluster divide. Cluster size distributions depend solely on the ratio of the break rate to the growth rate - allowing us to predict how cluster size and variability depend on cell motility and cell-cell mechanics. Our results suggest that organisms can achieve better size control when cell division is restricted to the cluster boundaries or when fracture can be localized to the cluster center. Our results link the general physics problem of a collective active escape over a barrier to size control, providing a quantitative measure of how motility can regulate organ or organism size.

We propose a serrated ultra-broadband infrared absorber based on the multi-layer repetitive stacking of indium tin oxide (ITO) and silicon materials. This absorber achieves continuously tunable absorption within a wide infrared light range. By changing the size and materials of the absorber, its average absorption rate can reach a relatively high level. Simulation results reveal wavelength-dependent electric field localization: shorter wavelengths concentrate in narrower upper regions, while longer wavelengths localize in wider lower areas. This design offers potential applications in near-infrared detectors, optical communication, and smart windows.

Photocatalytic reactions often exhibit fast kinetics and high product selectivity, qualities which are desirable but difficult to achieve simultaneously in thermally driven processes. However, photo-driven mechanisms are poorly understood owing to the difficulty in realistically modeling catalysts in optically excited states. Here we apply many-body perturbation theory (MBPT) calculations to gain insight into these mechanisms by studying a prototypical photocatalytic reaction, proton desorption from a rutile TiO${}_2$ (110) surface. Our calculations reveal a qualitatively different desorption process upon photoexcitation, with an over 50% reduction in the desorption energy and the emergence of an energy barrier. We rationalize these findings with a generalizable model based on Fano theory and explain the surprising increase of excitonic effects as the proton detaches from the surface. Our model also yields a connection between how the alignment of relevant ionization potentials affects the shape of the excited-state potential energy surface. These results cannot be qualitatively captured by typical constrained density-functional theory and highlight how contemporary first-principles MBPT calculations can be applied to design photocatalytic reactions.

Fragmentation and breakage of rocks is essential to iron ore mining and extraction. The breakage energy requirements and resulting ore particle size and mineral distributions are key to understanding and optimising mining and processing practices. This study combines high-speed and experimental X-ray micro-CT (micro-computed tomography) imaging with 3D image analyses to study the fragmentation of iron ore particles. Here a case study of a hematite-rich iron ore particle is used to illustrate the application and results produced by this imaging procedure. The particle was micro-CT scanned before a high-speed camera was used to image particle breakage in a custom drop weight test, capturing the dynamic processes of particle fracturing and subsequent fragmentation at a resolution of 50 microseconds. Fragments produced were collected, micro-CT scanned and analysed in three-dimension by particle shape, size, and volume.

Kernel approximation with exponentials is useful in many problems with convolution quadrature and particle interactions such as integral-differential equations, molecular dynamics and machine learning. This paper proposes a weighted balanced truncation to construct an optimal model reduction method for compressing the number of exponentials in the sum-of-exponentials approximation of kernel functions. This method shows great promise in approximating long-range kernels, achieving over 4 digits of accuracy improvement for the Ewald-splitting and inverse power kernels in comparison with the classical balanced truncation. Numerical results demonstrate its excellent performance and attractive features for practical applications.

We present the first lithium tantalate (LT) thickness-extensional (TE) mode bulk acoustic resonators designed for piezoelectric power conversion, showcasing a low temperature coefficient of frequency (TCF) of -13.56 ppm/K. These resonators also exhibit high quality factors (Q) of 1698, and electromechanical coupling coefficients ($k^2$) of 8.8%, making them suitable for efficient power conversion applications. A grounded ring structure is leveraged for spurious mode-free response and figure of merit (FoM=$k^2 \times Q$) enhancement near the series resonance. The temperature dependency of Q and $k^2$ is experimentally tested over a wide temperature range, from $25^{\circ}\text{C}$ to $130^{\circ}\text{C}$, demonstrating the resonators thermal stability and consistent performance under varying conditions. This work highlights the potential of LT resonators in the future development of thermally stable power electronic systems, enabling more reliable and efficient piezoelectric power converters.

We examine a logical foundation of depicting a Lorentz contraction of a Coulomb field (an electric field of a point charge in uniform motion) by means of the 'Lorentz contracted' field lines. Two existing arguments for a contraction of field lines sound appealing and lead to very simple calculations yielding the correct results. However, one of them is a victim to subtle logical weaknesses, as it relies on ascribing a degree of physical reality to the electric field lines. The other one correctly proves what it sets out to prove. But it does not provide a proof, or even a suggestion, of an additional result that can be obtained by a new poof that we present here. Though our idea is very simple, the calculations used to prove it - based on a little known, half a century old result by Tsien - are somewhat more involved than those from past arguments.

We formulate an optimization of a bicycle ascent time under the constraints of the average, maximum, and minimum powers. In contrast to the first part of this study, we do not restrict the departure to flying starts with an initial speed determined by the model and its optimization. We allow for various initial speeds, from a standstill to a launched start. We accomplish this by generalizing the discontinuous piecewise constant speed model to a continuous piecewise linear speed model. Regardless of the initial speed, steepness or profile of the ascent the optimal strategy tends to a constant ground speed, in agreement with the conclusion of the previous, more restricted, formulation. This new formulation allows us to compare various initial-speed strategies and, hence, has a direct application to competitive cycling. Notably, in timetrials composed of flat and steep sections, it helps one decide whether or not to change bicycle, which requires stopping and restarting, from one that is more appropriate for flats to one that is more appropriate for uphills.

We use time-dependent density functional theory-based approaches, TD-DFT and TD-DFTB, to investigate the optical absorption of B800 part of Rhodoblastus acidophilus light-harvesting complex 2 (LH2). Both methods are shown to give qualitative agreement with experimental spectra for a single BChl a molecule and for the optimized structure of B800 complex containing nine of such molecules. We proved the absence of any sizable effects originating from the interaction between adjacent molecules, thus optical features of B800 LH2 part should not be attributed to the structural organization of pigments. In addition, time-dependent procedure itself was found to be crucial for the correct description of BChl a absorption spectrum.

With increasing water scarcity, membrane distillation technology has gained widespread attention as an innovative method for seawater desalination.However,existing studies often overlook the influence of membrane characteristics on mass transfer efficiency. This study, based on the lattice Boltzmann method,proposes a model for a novel Poly(tetraethynylpyrene) membrane material to reveal the influence of membrane characteristics on the performance of vacuum membrane distillation. The model considers the factors such as porosity, tortuosity, membrane thickness, pore size, membrane surface wettability and temperature difference on the permeate flux. The results show that the permeate flux increases linearly with the porosity and decreases exponentially with the tortuosity factor. There is an optimal membrane thickness range (2{\mu}m) beyond which the permeate flux decreases exponentially. In addition, the permeate flux increases exponentially with increasing temperature difference and pore size. Further analysis of the effect of membrane surface wettability shows that permeate flux increases with increasing hydrophobicity. Finally, the feed temperature and tortuosity factor have the largest effect on permeate flux,followed by membrane thickness and, subsequently, pore size. The model can be further extended to study other configurations of membrane distillation technologies.

The ever-increasing volume of data has necessitated a new computing paradigm, embodied through Artificial Intelligence (AI) and Large Language Models (LLMs). Digital electronic AI computing systems, however, are gradually reaching their physical plateaus, stimulating extensive research towards next-generation AI accelerators. Photonic Neural Networks (PNNs), with their unique ability to capitalize on the interplay of multiple physical dimensions including time, wavelength, and space, have been brought forward with a credible promise for boosting computational power and energy efficiency in AI processors. In this article, we experimentally demonstrate a novel multidimensional arrayed waveguide grating router (AWGR)-based photonic AI accelerator that can execute tensor multiplications at a record-high total computational power of 262 TOPS, offering a ~24x improvement over the existing waveguide-based optical accelerators. It consists of a 16x16 AWGR that exploits the time-, wavelength- and space- division multiplexing (T-WSDM) for weight and input encoding together with an integrated Si3N4-based frequency comb for multi-wavelength generation. The photonic AI accelerator has been experimentally validated in both Fully-Connected (FC) and Convolutional NN (NNs) models, with the FC and CNN being trained for DDoS attack identification and MNIST classification, respectively. The experimental inference at 32 Gbaud achieved a Cohen's kappa score of 0.867 for DDoS detection and an accuracy of 92.14% for MNIST classification, respectively, closely matching the software performance.

Particle Therapy (PT) has emerged as a powerful tool in cancer treatment, leveraging the unique dose distribution of charged particles to deliver high radiation levels to the tumor while minimizing damage to surrounding healthy tissue. Despite its advantages, further improvements in Treatment Planning Systems (TPS) are needed to address uncertainties related to fragmentation process, which can affect both dose deposition and effectiveness. These fragmentation effects also play a critical role in Radiation Protection in Space, where astronauts are exposed to high level of radiation, necessitating precise models for shielding optimization. The FOOT (FragmentatiOn Of Target) experiment addresses these challenges by measuring fragmentation cross-section with high precision, providing essential data for improving TPS for PT and space radiation protection strategies. This thesis contributes to the FOOT experiment in two key areas. First, it focuses on the performances of the vertex detector, which is responsible for reconstructing particle tracks and fragmentation vertexes with high spatial resolution. The study evaluates the detector's reconstruction algorithm and its efficiency to detect particles. Second the thesis present a preliminary calculation of fragmentation cross section, incorporating the vertex detector for the first time in these measurements.

We present the methods and results for broadband group delay dispersion measurements for all-monocrystalline and amorphous-crystalline hybrid supermirrors, as well as an all-amorphous mirror in the wavelength range from \SIrange{2.5}{4.8}{\micro \metre}. Measurements are performed using a custom-built white light interferometer that allows for balanced and unbalanced measurement configurations. We compare the results to theoretical transfer-matrix method simulations and an alternative measurement using a commercial Fourier-transform infrared spectrometer. Additionally, we investigate group delay dispersion by direct differentiation using a local polynomial smoothing approach (Savitzky-Golay filter) and find strong consistency between our results, the theoretical prediction and the estimation using this method.

For linear electromagnetic systems possessing time-reversal symmetry, we present an approach to bound ratios of internal fields excited from different ports, using only the scattering matrix (S matrix), improving upon previous related bounds by Sounas and Al\`u (2017). By reciprocity, emitted-wave amplitudes from internal dipole sources are bounded in a similar way. When applied to coupled-resonant systems, our method constrains ratios of resonant coupling/decay coefficients. We also obtain a relation for the relative phase of fields excited from the two ports and the ratio of field intensities in a two-port system. In addition, although lossy systems do not have time-reversal symmetry, we can still approximately bound loss-induced non-unitarity of the S matrix using only the lossless S matrix. We show numerical validations of the near-tightness of our bounds in various scattering systems.

The sawtooth-free hybrid scenario has been achieved recently in ASDEX Upgrade (AUG) with applied non-inductive current sources and auxiliary heating [A. Burckhart et al 2023 Nucl. Fusion 63 126056]. Control experiments in AUG suggest that the self-regulating magnetic flux pumping mechanism, characterized by anomalous current redistribution, is responsible for clamping the central safety factor (q_0) close to unity, thereby preventing the sawtooth onset. This work presents a numerical and theoretical investigation of flux pumping in the AUG hybrid scenario based on the two-temperature, visco-resistive, full magnetohydrodynamic (MHD) model with the JOREK code. To quantitatively model the flux pumping, we choose realistic parameters, plasma configurations, and source terms based on AUG experiments. During the initial saturation stage of the unstable 1/1 quasi-interchange mode (on millisecond timescales), q_0 exhibits fast under-damped oscillation and reaches a value closer to unity, which is attributed to the self-regulation of core plasma and the fast dynamo effect on the order of V/m. On the longer resistive diffusion timescale of seconds, the slow negative dynamo effect on the order of mV/m induced by the 1/1 MHD instability plays an effective role in flux pumping, which provides quantitative agreement with experimental observations for the first time. The final saturated 1/1 MHD instability exhibits features of the quasi-interchange mode and tearing mode, and the associated convective plasma flow velocity is a few m/s. The toroidal negative electric field from the slow dynamo dominantly offsets the positive current drive and continuously redistributes the current density and pressure. As a result, q_0 is maintained close to unity due to the low-shear profiles of current density and pressure in the plasma core, and the system enters a sawtooth-free and quasi-stationary helical state.

The Novatron is a fusion concept characterized by its axisymmetric mirror-cusp magnetic topology. The magnetic field exhibits good curvature and a high mirror ratio. Plasma equilibrium profiles for the Novatron are obtained by solving an axisymmetric guiding-center anisotropic boundary-value problem. These profiles are then analyzed with respect to several MHD stability criteria, including the mirror, firehose, and interchange conditions. A generalized Rosenbluth and Longmire MHD interchange criterion, where anisotropic pressure variations along flux tubes are allowed for, is subsequently employed for determining stable MHD equilibria. Additionally, a corresponding CGL double adiabatic interchange criterion is investigated for obtaining stable equilibria in the collisionless limit, both theoretically and numerically using the large scale Hybrid Particle-In-Cell code WarpX.

We investigate the photocurrent induced by bicircular light (BCL) in materials, with a focus on its multi-state geometric nature. BCL, a combination of left- and right-circularly polarized light, can generate both injection and shift currents, originating from the geometric properties of gauge-invariant shift vectors, quantum geometric tensors, and triple-phase products. Crucially, the real parts of the quantum geometric tensors and triple-phase products remain nonzero in centrosymmetric systems, facilitating photocurrent generation in contrast to the traditional shift current bulk photovoltaic effect. Using a diagrammatic approach, we systematically analyze the BCL-induced photocurrents and demonstrate the multi-state geometric nature within a one-dimensional three-site Rice-Mele model. Our findings provide a quantum geometric understanding of BCL-induced photocurrents, underscoring the importance of considering multi-band contributions in real materials.

Topological singularities of optical response functions -- such as reflection amplitudes -- enable elegant practical applications ranging from analog signal processing to novel molecular sensing approaches. A phase singularity-based refractometric sensor monitors the rapidly evolving argument of the optical field near the point of phase singularity, in contrast to the reflection zero in traditional surface plasmon polariton sensors. This raises a natural question: What happens with the sensitivity and resolution of such a sensor when it operates close to a zero of the response function, where the detected signal may be greatly influenced by various noise sources? In this paper, we systematically study the effect of the shot noise on the performance of a generic phase singularity-based refractometric sensor. We develop a theoretical model of a spectroscopic ellipsometry-based system operating near a phase singularity and couple the macroscopic optical picture of the detection with a quantum shot noise model. Within the developed model, we illustrate how the shot noise of the detector comes into play and study its effect on the sensitivity and resolution of the refractometric sensor. Our results suggest that such an ellipsometry-based phase singularity sensor remains stable even in the presence of shot noise near the point of zero reflection.

The spontaneous (so-called Quincke) rotation of an uncharged, solid, dielectric, spherical particle under a steady electric field is analyzed, accounting for the inertia of the particle and the transient fluid inertia, or ``hydrodynamic memory,'' due to the unsteady Stokes flow around the particle. The dynamics of the particle are encapsulated in three coupled nonlinear integro-differential equations for the evolution of the angular velocity of the particle, and the components of the induced dipole of the particle that are parallel and transverse to the applied field. These equations represent a generalization of the celebrated Lorenz system. A numerical solution of these `modified Lorenz equations' (MLE) shows that hydrodynamic memory leads to an increase in the threshold field strength for chaotic particle rotation, which is in qualitative agreement with experimental observations. Furthermore, hydrodynamic memory leads to an increase in the range of field strengths where multi-stability between steady and chaotic rotation occurs. At large field strengths, chaos ceases and the particle is predicted to execute periodic rotational motion.

Euler-Lagrange (EL) point-particle simulations rely on hydrodynamic force closure models to accurately predict particle dynamics in flows. The closure models currently employed for dilute particle-laden flows require the undisturbed fluid velocity estimated at the particle center. Recovering this undisturbed velocity necessitates modeling the particle-induced disturbance on the flow. In this work, we present a new framework for velocity disturbance modeling near no-slip walls, suited for dilute gas-solid flows characterized by large Stokes numbers. For small disturbance Reynolds numbers, the velocity disturbance governing equations reduce to the steady Stokes equations. To exactly satisfy the no-slip and no-penetration boundary conditions for the velocity disturbance at the location of the planar wall, we employ the method of images to derive the Green's functions of the Stokes equations for Wendland and Gaussian force regularization kernels. An additional convolution product with a mesh-spacing dependent Gaussian kernel is performed to match the solution produced by a discrete flow solver. We verify the proposed analytical model by comparing the velocity disturbance generated on a computational mesh with the model predictions. An extension of the model for finite particle Reynolds numbers is proposed by multiplying an Oseen correction factor in an unbounded domain. The correction scheme is validated using canonical test cases involving the motion of a single particle parallel and perpendicular to a wall at various Stokes numbers, particle Reynolds numbers, and particle diameter to mesh-spacing ratios. Owing to the polynomial representation of the velocity disturbance for a Wendland force regularization, the current model is computationally efficient and well-suited for large-scale, two-way coupled EL simulations.

Two-dimensional electromagnetic particle-in-cell simulations are employed to study particle motion and power deposition in inductively coupled plasmas. We show that under condition of low-frequency ($\sim\mathrm{MHz}$) and low-pressure, the electron motion is highly nonlinear in the skin region near the coil: electrons are strongly magnetized, and the energy deposition is small throughout most of the RF cycle. However, during the phase when the RF magnetic field vanishes, electrons briefly demagnetize, causing a jet-like current penetrating into the plasma. During these short time intervals the power deposition becomes high, resulting in periodic bursts in the energy deposition. We developed a new kinetic theory, which not only provides analytical expressions for the plasma current and energy deposition, but also predicts a new nonlinear relation between electron current and the RF inductive electric field. A criterion for transition between the low frequency, periodic bursty nonlinear regime and the high frequency, anomalous non-local skin effect regime is proposed and verified using a series of fully kinetic 2D particle-in-cell simulations.

We report an all-lossy index-guided dual-core photonic crystal fiber (PCF) that hosts a second-order exceptional point (EP) in the systems parameter space. By appropriately selecting a parametric encirclement scheme around the EP, the interaction between the coupled modes has been studied, and the mode conversion is subsequently observed.

The possibility of the breaking of Lorentz symmetry has been discussed in many models of quantum gravity. In this paper we follow the Lorentz violation model in Ref. [1] (i.e., our previous work) to discuss the Doppler frequency shift of photons and the Compton scattering process between photons and electrons, pointing out that following the idea in Ref. [1] we have to modify the usual quantum relation of photons in the Doppler effect. But due to the current limited information and knowledge, we could not yet determine the specific expression for the correction coefficient in the modified quantum relation of photons. However, the phenomenon called spontaneous radiation in a cyclotron maser give us an opportunity to see what the expression for this correction coefficient might look like. Therefor, under some necessary constraints, we construct a very concise expression for this correction coefficient through the discussion of different cases. And then we use this expression to analyze the wavelength of radiation in the cyclotron maser, which tends to a limited value at v is close to c, rather than to 0 as predicted by the Lorentz model. And the inverse Compton scattering phenomenon is also discussed and we find that there is a limit to the maximum energy that can be obtained by photons in the collision between extremely relativistic particles and low-energy photons, which conclusion is also very different from that obtained from the Lorentz model, in which the energy that can be obtained by the photon tends to be infinite as the velocity of particle is close to c. This paper still follows the purpose in Ref. [1] that the energy and momentum of particles (i.e., any particles, including photons) cannot be infinite, otherwise it will make some physical scenarios invalid.

The points where diffraction orders emerge or vanish in the propagating spectrum of periodic non-Hermitian systems are referred to as scattering thresholds. Close to these branch points, resonances from different Riemann sheets can tremendously impact the optical response. However, these resonances were so far elusive for two reasons. First, their contribution to the signal is partially obscured, and second, they are inaccessible for standard computational methods. Here, we explore the interplay of scattering thresholds with resonances and introduce a multi-valued rational approximation to access the hidden resonances. Our theoretical and numerical approach is used to analyze the resonances of a plasmonic line grating. This work elegantly explains the occurrence of pronounced spectral features at scattering thresholds applicable to many nanophotonic systems of contemporary and future interest.

The development of high-performance photocathodes is essential for generating high-brightness electron beams required by existing and future accelerators. This work introduces a state-of-the-art triple evaporation growth system designed for bialkali antimonide photocathodes. By enabling the simultaneous deposition of all three materials, this system significantly enhances vacuum stability and the reproducibility of photocathode fabrication. Complementing this, the novel characterization system PhoTEx allows spatially and spectrally resolved measurements of key photocathode parameters, such as quantum efficiency (QE), mean transverse energy (MTE), reflectance and lifetime. Crucially, all measurements are performed within a single compact setup, without moving the sample, preserving ultra-high vacuum conditions. The spectral resolved measurement of the reflectance allows the investigation of the color. Photocathode colorimetry may provide valuable insights into material homogeneity and aging. A Na-K-Sb photocathode was grown using the triple evaporation method, achieving an initial QE of $5.5\,\%$ at $520\,$nm. The photocathode was characterized at PhoTEx over two months, demonstrating consistent MTE measurements and a dataset with spectral response, reflectance and colorimetry data. Together, the triple evaporation growth system and PhoTEx mark a significant advancement in optimizing photocathodes with exceptional performance, paving the way for brighter and more stable electron sources for next-generation accelerator facilities.

Liquid films of nanometric thickness are prone to spinodal dewetting driven by disjoining pressure, meaning that a non-wetting liquid film of homogeneous thickness in the range of tens of nanometers will spontaneously break into droplets. The surface energy of the underlying solid substrate heavily influences the dynamics and resulting droplet configurations. Here, we study the dewetting of thin liquid films on physically flat but chemically heterogeneous substrates using the thin film equation. We use linear stability analysis (LSA) to describe and predict the system's behavior until the film ruptures and compare it to numerical simulations. Based on our calculations, we propose a potential method for measuring surface energy patterns. Furthermore, we study the non-linear dynamics and the eventually formed droplet pattern by numerical simulations.

We report a generalized nonlinear Schr\"odinger equation simulation model of an extreme learning machine based on optical fiber propagation. Using handwritten digit classification as a benchmark, we study how accuracy depends on propagation dynamics, as well as parameters governing spectral encoding, readout, and noise. Test accuracies of over 91% and 93% are found for propagation in the anomalous and normal dispersion regimes respectively. Our simulation results also suggest that quantum noise on the input pulses introduces an intrinsic penalty to ELM performance.

Tissue deformations during morphogenesis can be active, driven by internal processes, or passive, resulting from stresses applied at their boundaries. Here, we introduce the Drosophila hindgut primordium as a model for studying boundary-driven tissue morphogenesis. We characterize its deformations and show that its complex shape changes can be a passive consequence of the deformations of the active regions of the embryo that surround it. First, we find an intermediate characteristic triangular shape in the 3D deformations of the hindgut. We construct a minimal model of the hindgut primordium as an elastic ring deformed by active midgut invagination and germ band extension on an ellipsoidal surface, which robustly captures the symmetry-breaking into this triangular shape. We then quantify the 3D kinematics of the tissue by a set of contours and discover that the hindgut deforms in two stages: an initial translation on the curved embryo surface followed by a rapid breaking of shape symmetry. We extend our model to show that the contour kinematics in both stages are consistent with our passive picture. Our results suggest that the role of in-plane deformations during hindgut morphogenesis is to translate the tissue to a region with anisotropic embryonic curvature and show that uniform boundary conditions are sufficient to generate the observed nonuniform shape change. Our work thus provides a possible explanation for the various characteristic shapes of blastopore-equivalents in different organisms and a framework for the mechanical emergence of global morphologies in complex developmental systems.

The CH4 Livestock Emission (CH4rLiE) project aims at developing a prototype for methane emissions capture in a barn environment. Methane has a higher global warming potential (GWP) with respect to CO2, and methane emissions of human origin contribute about 23% to global warming. Emissions from livestock farms play a non-negligible role, as a single cow is capable of emitting about 110 kg of methane in a year. Several projects have tried to mitigate the problem by intervening on animal feed: CH4rLiE, in contrast, proposes to act on the methane already produced and diffused in the air, using a specially developed recovery system. The idea arose from the expertise acquired in the Large Hadron Collider experiments at CERN, where special gas recuperation systems are being developed to extract CF4 from gaseous detectors' exhausted gas mixture. The project focuses on the study of gas adsorption by porous materials and on the development of a prototype system for methane capture, which will be installed in a real barn. This study is being supported by an initial phase of gas diffusion simulations and by a campaign of measurements of gas concentrations in different barn areas. CH4rLiE will also provide an opportunity to explore, for the first time, the feasibility of methane recovery from the farm environment without affecting the animals' feeding or living conditions. The social benefits are extremely interesting both in terms of developing and implementing low-impact farming production processes, but also in terms of recycling expensive or environmentally unfriendly gasses.

The recent demonstrations of van der Waals (vdW) nanophotonics have opened new pathways for manipulating the light-matter interaction in an intrinsic manner, leading to fascinating achievements in tunable magneto-optics by self-hybrid polaritons, indirect bandgap lasering, and exceptionally enhanced optical nonlinearity. However, the anisotropic atomic lattice, chemically active side walls, and distinct enthalpies of formation across vdW materials, pose significant challenges in nanofabrication and material choices, hindering the realization of high-Q resonant mode on arbitrary materials. In this work, we propose an etch-free vdW structure that mimics the shallow etching, termed "artificial etching". This approach utilizes a low refractive index (LRI) perturbation layer made of photoresist, drastically reducing radiation loss and experimentally achieving a remarkable Q factor of up to 348, which is comparable to the highest values reported in vdW nanophotonics. We demonstrate room-temperature polaritons in etch-free structures using four representative materials (WS$_2$, MoS$_2$, WSe$_2$, and MoSe$_2$) through self-hybridization of high-Q (quasi-)bound states in the continuum (BIC) modes and excitons, achieving a Rabi-splitting of approximately 80 meV, which significantly surpasses the intrinsic excitonic loss. Furthermore, we showcase optical modulation of indirect bandgap emission in bulk WS$_2$ and direct exciton emission in heterostructures, achieving substantial polarization-dependent enhancement of their emission efficiencies. The proposed etch-free vdW structure provides a versatile platform for high-Q nanophononics while preserving material integrity, advancing applications in photoelectronic and quantum devices.

We show that programmable photonic circuit architectures composed of alternating mixing layers and active layers offer a high degree of flexibility. This alternating configuration enables the systematic tailoring of both the network's depth (number of layers) and width (size of each layer) without compromising computational capabilities. From a mathematical perspective, our approach can be viewed as embedding an arbitrary target matrix into a higher-dimensional matrix, which can then be represented with fewer layers and larger active elements. We derive a general relation for the width and depth of a network that guarantees representing all $N \times N$ complex matrix operations. Remarkably, we show that just two such active layers, interleaved with passive mixing layers, are sufficient to universally implement arbitrary matrix transformations. This result promises a more adaptable and scalable route to photonic matrix processors.

The shear Alfv\'en wave (SAW) continuum plays a critical role in the stability of energetic particle-driven Alfv\'{e}n eigenmodes. We develop a theoretical framework to analyze the SAW continuum in three-dimensional quasisymmetric magnetic fields, focusing on its implications for stellarator design. By employing a near-axis model and degenerate perturbation theory, the continuum equation is solved, highlighting unique features in 3D configurations, such as the interactions between spectral gaps. Numerical examples validate the theory, demonstrating the impact of flux surface shaping and quasisymmetric field properties on continuum structure. The results provide insights into optimizing stellarator configurations to minimize resonance-driven losses of energetic particles. This work establishes a basis for incorporating Alfv\'enic stability considerations into the stellarator design process, demonstrated through optimization of a quasihelical configuration to avoid high-frequency spectral gaps.

We designed an automatic differentiation-based strategy to generate optical trap arrays that change smoothly in time. Instead of repeatedly regenerating the holograms for each time step, we derive the differential form of the phase dynamics that enables the continuous evolution of the trap coordinates. This differential form is derived from the implicit differentiation of the fixed point of the Gerchberg-Saxton algorithm, which is computationally efficient. We carried out numerical and laboratory experiments to demonstrate its effectiveness in improving the phase continuity and reducing the computational burden compared to the traditional pure interpolation techniques. By combining the method with the spatial light modulator, the method is promising for the dynamic manipulation of particles in real experiments.

Despite the successful demonstration of compact free electron lasers (FELs) driven by laser wakefield accelerators (LWFAs), the shot-to-shot fluctuations inherent to LWFAs remain a major obstacle to realizing LWFA-driven FELs with high gain and robust operation. Here, we present a conceptual design for LWFA-driven FELs with enhanced robustness and reliability. By employing Bayesian optimization techniques, the beamline was optimized to achieve sufficient tolerance against these fluctuations under actual experimental conditions. Start-to-end simulations revealed that this systematic optimization significantly reduces the system's sensitivity to parametric variations. With the optimized configurations, the radiation energy can be maintained above 1 {\mu}J at a wavelength of approximately 25 nm, even when accounting for twice the root-mean-square (RMS) ranges of these inherent jitters. This proposed scheme represents a substantial advancement in the development of compact LWFA-driven FEL systems, enabling robust operation and paving the way for the realization of reliable and widely accessible sources.

Cochlear outer hair cells (OHCs) have two mechanosensitive elements: the hair bundle with mechanotrasducer channels and the piezoelectric lateral wall of the cell body. The present report examines how these elements interact with each other by incorporating OHCs into the simplest local cochlear models. In the frequency range, typically above 1 kHz, where capacitive conductance is greater than the ionic conductance, hair bundle (HB) conductance drives the piezoelectric cell body and amplified oscillation by countering viscous drag, while the cell body increases its stiffness owing to strain-induced polarization, elevating the resonance frequency. Since HB sensitivity is essential for amplification, the resonance is not pure piezoelectric, but semi-piezoelectric. In the lower frequency range, typically lower than 100 Hz, strain induced polarization contributes to drag and the HB sensitivity increases cell body stiffness.

We demonstrate a novel coherent photonic neural network using tunable phase-change-material-based couplers and neural architecture search. Compared to the MZI-based Clements network, our results indicate 85% reduction in the network footprint while maintaining the accuracy.

Hybrid quantum systems with different particle species are fundamental in quantum materials and quantum information science. In this work, we demonstrate that Hamiltonian learning in hybrid spin-boson systems can achieve the Heisenberg limit. Specifically, we establish a rigorous theoretical framework proving that, given access to an unknown hybrid Hamiltonian system, our algorithm can estimate the Hamiltonian coupling parameters up to root mean square error (RMSE) $\epsilon$ with a total evolution time scaling as $T \sim \mathcal{O}(\epsilon^{-1})$ using only $\mathcal{O}({\rm polylog}(\epsilon^{-1}))$ measurements. Furthermore, it remains robust against small state preparation and measurement (SPAM) errors. In addition, we also provide an alternative algorithm based on distributed quantum sensing, which significantly reduces the maximum evolution time per measurement. To validate our method, we apply it to the generalized Dicke model for Hamiltonian learning and the spin-boson model for spectrum learning, demonstrating its efficiency in practical quantum systems. These results provide a scalable and robust framework for precision quantum sensing and Hamiltonian characterization in hybrid quantum platforms.

Cascading failures (CF) entail component breakdowns spreading through infrastructure networks, causing system-wide collapse. Predicting CFs is of great importance for infrastructure stability and urban function. Despite extensive research on CFs in single networks such as electricity and road networks, interdependencies among diverse infrastructures remain overlooked, and capturing intra-infrastructure CF dynamics amid complex evolutions poses challenges. To address these gaps, we introduce the \textbf{I}ntegrated \textbf{I}nterdependent \textbf{I}nfrastructure CF model ($I^3$), designed to capture CF dynamics both within and across infrastructures. $I^3$ employs a dual GAE with global pooling for intra-infrastructure dynamics and a heterogeneous graph for inter-infrastructure interactions. An initial node enhancement pre-training strategy mitigates GCN-induced over-smoothing. Experiments demonstrate $I^3$ achieves a 31.94\% in terms of AUC, 18.03\% in terms of Precision, 29.17\% in terms of Recall, 22.73\% in terms of F1-score boost in predicting infrastructure failures, and a 28.52\% reduction in terms of RMSE for cascade volume forecasts compared to leading models. It accurately pinpoints phase transitions in interconnected and singular networks, rectifying biases in models tailored for singular networks. Access the code at https://github.com/tsinghua-fib-lab/Icube.

Objective: ascending aortic aneurysm growth prediction is still challenging in clinics. In this study, we evaluate and compare the ability of local and global shape features to predict ascending aortic aneurysm growth. Material and methods: 70 patients with aneurysm, for which two 3D acquisitions were available, are included. Following segmentation, three local shape features are computed: (1) the ratio between maximum diameter and length of the ascending aorta centerline, (2) the ratio between the length of external and internal lines on the ascending aorta and (3) the tortuosity of the ascending tract. By exploiting longitudinal data, the aneurysm growth rate is derived. Using radial basis function mesh morphing, iso-topological surface meshes are created. Statistical shape analysis is performed through unsupervised principal component analysis (PCA) and supervised partial least squares (PLS). Two types of global shape features are identified: three PCA-derived and three PLS-based shape modes. Three regression models are set for growth prediction: two based on gaussian support vector machine using local and PCA-derived global shape features; the third is a PLS linear regression model based on the related global shape features. The prediction results are assessed and the aortic shapes most prone to growth are identified. Results: the prediction root mean square error from leave-one-out cross-validation is: 0.112 mm/month, 0.083 mm/month and 0.066 mm/month for local, PCA-based and PLS-derived shape features, respectively. Aneurysms close to the root with a large initial diameter report faster growth. Conclusion: global shape features might provide an important contribution for predicting the aneurysm growth.

Optical properties of fullerite C$_{60}$ single crystals saturated with hydrogen and nitrogen molecules were studied in the temperature range from 20 K to 230 K using the spectral-luminescent method. Saturation was carried out under a pressure of 30 atm at various temperatures from 470 K to 720 K. At saturation temperatures above 520 K for hydrogen and 690 K for nitrogen, chemical interaction of impurity molecules and the fullerene matrix occurs, forming new chemical compounds. The results of a study of the photoluminescent properties of a new substances are presented for the first time.

Clocks based on nuclear isomer transitions promise exceptional stability and precision. The low transition energy of the Thorium-229 isomer makes it an ideal candidate, as it may be excited by a vacuum-ultraviolet laser and is highly sensitive to subtle interactions. This enables the development of powerful tools for probing new forces, which we call quintessometers. In this work, we demonstrate the potential of nuclear clocks, particularly solid-state variants, to surpass existing limits on scalar field couplings, exceeding the sensitivity of current fifth-force searches at submicron distances and significantly improving equivalence-principle tests at kilometer scales and beyond. Additionally, we highlight the capability of transportable nuclear clocks to detect scalar interactions at distances beyond $10\,$km, complementing space-based missions.

The axion is a compelling hypothetical particle that could account for the dark matter in our universe, while simultaneously explaining why quark interactions within the neutron do not appear to give rise to an electric dipole moment. The most sensitive axion detection technique in the 1 to 10 GHz frequency range makes use of the axion-photon coupling and is called the axion haloscope. Within a high Q cavity immersed in a strong magnetic field, axions are converted to microwave photons. As searches scan up in axion mass, towards the parameter space favored by theoretical predictions, individual cavity sizes decrease in order to achieve higher frequencies. This shrinking cavity volume translates directly to a loss in signal-to-noise, motivating the plan to replace individual cavity detectors with arrays of cavities. When the transition from one to (N) multiple cavities occurs, haloscope searches are anticipated to become much more complicated to operate: requiring N times as many measurements but also the new requirement that N detectors function in lock step. To offset this anticipated increase in detector complexity, we aim to develop new tools for diagnosing low temperature RF experiments using neural networks for pattern recognition. Current haloscope experiments monitor the scattering parameters of their RF receiver for periodically measuring cavity quality factor and coupling. However off-resonant data remains relatively useless. In this paper, we ask whether the off resonant information contained in these VNA scans could be used to diagnose equipment failures/anomalies and measure physical conditions (e.g., temperatures and ambient magnetic field strengths). We demonstrate a proof-of-concept that AI techniques can help manage the overall complexity of an axion haloscope search for operators.

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks. However, existing approaches mostly focus on the prediction task, and do not incorporate the necessary data assimilation. Moreover, these models suffer from error accumulation in long roll-outs, limiting their applicability to seasonal predictions or climate projections. Here, we introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework for assimilation and prediction of both weather and climate. By learning to quantify the probabilistic distribution of atmospheric states under observational, predictive, and external forcing constraints, GAP excels in a broad range of weather-climate related tasks, including data assimilation, seamless prediction, and climate simulation. In particular, GAP is competitive with state-of-the-art ensemble assimilation, probabilistic weather forecast and seasonal prediction, yields stable millennial simulations, and reproduces climate variability from daily to decadal time scales.

We develop an approach to generate random graphs to a target level of assortativity by using copula structures in graphons. Unlike existing random graph generators, we do not use rewiring or binning approaches to generate the desired random graph. Instead, we connect Archimedean bivariate copulas to graphons in order to produce flexible models that can generate random graphs to target assortativity. We propose three models that use the copula distribution function, copula density function and their mixed tensor product to produce networks. We express the assortativity coefficient in terms of homomorphism densities. Establishing this relationship forges a connection between the parameter of the copula and the frequency of subgraphs in the generated network. Therefore, our method attains a desired the subgraph distribution as well as the target assortativity. We establish the homomorphism densities and assortativity coefficient for each of the models. Numerical examples demonstrate the ability of the proposed models to produce graphs with different levels of assortativity.

The ATLAS experiment at the Large Hadron Collider (LHC) is currently preparing to replace its present Inner Detector (ID) with the upgraded, all-silicon Inner Tracker (ITk) for its High-Luminosity upgrade (HL-LHC). The ITk will consist of a central pixel tracker and the outer strip tracker, consisting of about 19,000 strip detector modules. Each strip module is assembled from up to two sensors, and up to five flexes (depending on its geometry) in a series of gluing, wirebonding and quality control steps. During detector operation, modules will be cooled down to temperatures of about -35C (corresponding to the temperature of the support structures on which they will be mounted) after being initially assembled and stored at room temperature. In order to ensure compatibility with the detector's operating temperature range, modules are subjected to thermal cycling as part of their quality control process. Ten cycles between -35C and +40C are performed for each module, with full electrical characterisation tests at each high and low temperature point. As part of an investigation into the stress experienced by modules during cooling, it was observed that modules generally showed a change in module shape before and after thermal cycling. This paper presents a summary of the discovery and understanding of the observed changes, connecting them with excess module stress, as well as the resulting modifications to the module thermal cycling procedure.

The transportation sector is the single largest contributor to US emissions and the second largest globally. Electric vehicles (EVs) are expected to represent half of global car sales by 2035, emerging as a pivotal solution to reduce emissions and enhance grid flexibility. The electrification of buildings, manufacturing, and transportation is expected to grow electricity demand substantially over the next decade. Without effectively managed EV charging, EVs could strain energy grid infrastructure and increase electricity costs. Drawing on de-identified 2023 EV telematics data from Rivian Automotive, this study found that 72% of home charging commenced after the customer plugged in their vehicle regardless of utility time of use (TOU) tariffs or managed charging programs. In fewer than 26% of charging sessions in the sample, EV owners actively scheduled charging times to align or participate in utility tariffs or programs. With a majority of drivers concurrently plugged in during optimal charging periods yet not actively charging, the study identified an opportunity to reduce individual EV owner costs and carbon emissions through smarter charging habits without significant behavioral modifications or sacrifice in user preferences. By optimizing home charging schedules within existing plug-in and plug-out windows, the study suggests that EV owners can save an average of $140 annually and reduce the associated carbon emissions of charging their EV by as much as 28%.

True random numbers are extracted through measurements of vacuum fluctuations in quantum state components. We propose an improved scheme utilizing an optimization-based simulation methodology to enhance the temporal resolution of quantum state detection and processing efficiency of vacuum fluctuation signals in continuous-variable quantum random number generators (CV-QRNGs), while simultaneously maximizing the entropy content of quantum noise sources. This work presents the first application of optimization simulation methodology to balanced homodyne detector (BHD) circuit design, with particular emphasis on improving high-frequency transmission characteristics. The design framework prioritizes system stability and S-parameter sensitivity to optimize both circuit architecture and critical component parameters. The AC amplifier circuit was implemented through ADS high-frequency simulations using two ABA-52563 RF amplifiers in a cascaded configuration, with circuit modeling performed on Rogers 4350 substrate optimized for high-frequency applications. This approach enabled the development of a switched-configuration BHD featuring: 1) 1.9 GHz bandwidth, 2) 41.5 dB signal-to-noise ratio at 1.75 GHz, 3) 30 dB common-mode rejection ratio at 100 MHz, and 4) frequency response flatness within 1.5 dB across 1.3-1.7 GHz. Additionally, the Husimi function is employed for entropy analysis to reconstruct vacuum state phase-space distributions, validating the detector's quantum measurement fidelity. The implemented system demonstrates a collective generation rate of 20.0504 Gbps across four parallel channels, with all output streams successfully passing NIST SP 800-22 statistical testing requirements.

Previous studies have suggested a conundrum in the relaxation dynamics of polydisperse supercooled liquids. It has been shown that in two dimensions, the relative relaxation times of particles of different sizes become more similar as the material is cooled, whereas the opposite happens in three dimensions: they decouple. Here we resolve this conundrum. First, we show that the coupling observed in two dimensions is an artifact of cage correction introduced to account for Mermin-Wagner fluctuations. Instead, the relative relaxation time of small and large particles in two dimensions remains constant or slightly decouples with temperature, as opposed to the substantial decoupling observed in three dimensions. Investigating these dimensional differences further, we find through mobile cluster analysis that small particles initiate relaxation in both dimensions. As the clusters grow larger, they remain dominated by small particles in three dimensions whereas in two cluster growth becomes particle-size agnostic. We explain these findings with a minimal model by studying the distributions of single-particle barrier heights in the system, showing there is a clear difference in the environments of small and large particles, depending on the dimensionality. These findings highlight the critical role of dimensionality in glass formation, providing new insights into the mechanisms underlying the glass transition in polydisperse supercooled liquids.

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the later modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2%, white matter coverage of 63.8%, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7% increase in white matter coverage and a 4.1% decrease in overreach compared to RNN-based methods.

In preparation of the operation of the CMS electromagnetic calorimeter (ECAL) barrel at the High Luminosity Large Hadron Collider (HL-LHC) the entire on-detector electronics will be replaced. The new readout electronics comprises 12240 very front end (VFE), 2448 front end (FE) and low voltage regulator (LVR) cards arranged into readout towers (RTs) of five VFEs, one FE and one LVR card. The results of testing one RT of final prototype cards at CERNs CHARM mixed field facility and PSIs proton irradiation facility are presented. They demonstrate the proper functioning of the new electronics in the expected radiation conditions.

The nitrogen reduction reaction (NRR), as an efficient and green pathway for ammonia synthesis, plays a crucial role in achieving on-demand ammonia production. This study proposes a novel design concept based on dual-iron atomic sites and nitrogen-boron co-doped graphene catalysts, exploring their high efficiency in NRR. By modulating the N and B co-doped ratios, we found that Fe2N3B@G catalyst exhibited significant activity in the adsorption and hydrogenation of N2 molecules, especially with the lowest free energy (0.32 eV) on NRR distal pathway, showing its excellent nitrogen activation capability and NRR performance. The computed electron localization function, crystal orbital Hamiltonian population, electrostatic potential map revealed that the improved NRR kinetics of Fe2N3B@G catalyst derived by N3B co-doping induced optimization of Fe-Fe electronic environment, regulation of Fe-N bond strength, and the continuous electronic support during the N2 breakage and hydrogenation. In particular, machine learning molecular dynamics (MLMD) simulations were employed to verify the high activity of Fe2N3B@G catalyst in NRR, which reveal that Fe2N3B@G effectively regulates the electron density of Fe-N bond, ensuring the smooth generation and desorption of NH3 molecules and avoiding the competition with hydrogen evolution reaction (HER). Furthermore, the determined higher HER overpotential of Fe2N3B@G catalyst can effectively inhibit the HER and enhance the selectivity toward NRR. In addition, Fe2N3B@G catalyst also showed good thermal stability by MD simulations up to 500 K, offering its feasibility in practical applications. This study demonstrates the superior performance of Fe2N3B@G in nitrogen reduction catalysis, and provides theoretical guidance for atomic catalyst design by the co-doping strategy and in-deep electronic environment modulation.

The antipolar $Pbcn$ phase of HfO$_2$ has been suggested to play an important role in the phase transition and polarization switching mechanisms in ferroelectric hafnia. In this study, we perform a comprehensive benchmark of density functional theory (DFT) calculations and deep potential molecular dynamics (DPMD) simulations to investigate the thermodynamic stability and phase transition behavior of hafnia, with a particular focus on the relationship between the $Pbcn$ and ferroelectric $Pca2_1$ phases. Our results reveal significant discrepancies in the predicted stability of the $Pbcn$ phase relative to the $Pca2_1$ phase across different exchange-correlation functionals. Notably, the PBE and hybrid HSE06 functionals exhibit consistent trends, which diverge from the predictions of the PBEsol and SCAN functionals. For a given density functional, temperature-driven phase transitions predicted by DFT-based quasi-harmonic free energy calculations aligns with finite-temperature MD simulations using a deep potential trained on the same density functional. Specifically, the PBE functional predicts a transition from $Pca2_1$ to $Pbcn$ with increasing temperature, while PBEsol predicts a transition from $Pca2_1$ to $P4_2/nmc$. A particularly striking and reassuring finding is that under fixed mechanical boundary conditions defined by the ground-state structure of $Pca2_1$, all functionals predict consistent relative phase stabilities and comparable switching barriers as well as domain wall energies. These findings underscore the unique characteristics of the $Pbcn$ phase in influencing phase transitions and switching mechanisms in ferroelectric hafnia.

In the context of quantum thermodynamics, quantum batteries have emerged as promising devices for energy storage and manipulation. Over the past decade, substantial progress has been made in understanding the fundamental properties of quantum batteries, with several experimental implementations showing great promise. This Perspective provides an overview of the solid-state materials platforms that could lead to fully operational quantum batteries. After briefly introducing the basic features of quantum batteries, we discuss organic microcavities, where superextensive charging has already been demonstrated experimentally. We then explore other materials, including inorganic nanostructures (such as quantum wells and dots), perovskite systems, and (normal and high-temperature) superconductors. Key achievements in these areas, relevant to the experimental realization of quantum batteries, are highlighted. We also address challenges and future research directions. Despite their enormous potential for energy storage devices, research into advanced materials for quantum batteries is still in its infancy. This paper aims to stimulate interdisciplinarity and convergence among different materials science research communities to accelerate the development of new materials and device architectures for quantum batteries.

Standard detection of entanglement relies on local measurements of the individual particles, evaluating their correlations in post-processing. For time-energy entangled photons, either times $(t_{1},t_{2})$ or energies $(E_{1},E_{2})$ are measured, but not both due to the mutual quantum uncertainty, providing only partial information of the entanglement. In principle, a global detector could recover the complete information of entanglement in a single shot if it could measure the combined correlated variables $(t_{1}-t_{2})$ and $(E_{1}+E_{2})$ without measuring the individual energies or times. Such a global measurement is possible using the reverse disentangling interaction, like sum-frequency generation (SFG), but nonlinear interactions at the single-photon level are ridiculously inefficient. We overcome this barrier by stimulating the nonlinear SFG interaction with a strong pump, thereby measuring both the energy-sum (SFG spectrum) and the time-difference (response to group delay/dispersion) simultaneously and efficiently. We generate bi-photons with extreme time-energy entanglement (octave-spanning spectrum of 113THz) and measure a relative uncertainty between time-difference and energy-sum of $\Delta(t_1-t_2)\Delta(E_1+E_2)\approx\!2\!\cdot\!10^{-13}h$, violating the classical bound by >12 orders of magnitude. The presented coherent SFG dramatically enhances the detection SNR compared to standard methods since it ideally rejects erroneous coincidences in both time and energy, paving the way for sensing applications, such as quantum illumination (radar) and more.

We describe an unexpected anthropic fine-tuning of gravity: human cognition arose on Earth only because the laws of gravity included gravitational waves. Their link is the heat from decays of the radioactive isotopes U-238 and Th-232, which were synthesized mainly in rare explosive mergers of binary neutron stars, brought about by the loss of orbital energy to gravitational radiation. This heat, released in Earth's interior, has (1) maintained plate tectonics and (2) likely helped keep Earth's iron core molten. The core's magnetic field has protected all life from annihilation by the solar wind. More surprisingly, relative brain size, a proxy for cognition, has seen two sharp increases, first for mammals and then for humans, both attributed by evolutionary biologists to adaptations to major climatic changes caused by specific tectonic events. After the second event, the joining of North and South America, human brain size grew from chimpanzee levels to modern ones. If the laws of gravity had not included gravitational waves, humans would not be capable of studying the laws of gravity.

Phosphorus plays an essential role in the chemistry of living organisms, being present in several fundamental biomolecules. The investigation of chemical reactions taking place in different astronomical environments involving phosphorus-containing molecules is essential for understanding how these species are produced and destroyed. Phosphorus monoxide (PO) and phosphorus nitride (PN) are key reservoirs of phosphorus in the Interstellar Medium (ISM). This work presents a computational study of the CPN system to identify viable reaction pathways involving atom-diatom collisions and to explore a potential destruction route for PN in the ISM. We explore the potential energy landscape of the C($\mathrm{^3P}$) + PN($^1\Sigma^+$), N($\mathrm{^4S}$) + CP($^2\Sigma^+$) and P($\mathrm{^4S}$) + CN($^2\Sigma^+$) reactions by performing high-accuracy ab initio calculations and provide their rate coefficients over a wide range of temperatures. The temperature-dependent rate coefficients were fitted to the modified Arrhenius equation: $k(T)=\alpha(T/300)^{\beta}\mathrm{exp}(-\gamma/T)$. An updated chemical network for P-bearing species was used to model the time-dependent abundances and reaction contributions of P, PO, PN, and PH during the chemical evolution of diffuse/translucent and dense clouds. The only neutral-neutral reaction capable of destroying PN without an activation energy seems to be the PN+C one. We have also shown that reactions between CP and N can yield CN and PN barrierless. Chemical models indicate that PO is a crucial species driving the gas-phase formation of PN. Typically, PO/PN ratios exceed 1, though their chemistry is influenced by photon- and cosmic-ray-induced processes. Over time in simulated dense clouds, neutral-neutral reactions such as PO + N, PH + N, P + OH, and PH + O play a significant role in determining the relative abundances of PO and PN.

The collective motion of bacteria, commonly referred to as bacterial turbulence, is well understood in Newtonian fluids. However, studies on complex fluids have predominantly focused on viscoelastic effects. In our experiments, we employed Ficoll and Methocel polymers to compare the impacts of Newtonian and shear-thinning fluids on bacterial turbulence. We reported various physical properties, including energy and enstrophy, and observed that the shear-thinning effect is significantly suppressed in high-concentration bacterial suspensions. This suppression is largely attributed to the disruption of chain-like polymer structures around bacterial flagella due to strong interbacterial interactions in dense suspensions. To validate this hypothesis, we conducted experiments across bacterial concentrations (within the range where bacterial turbulence forms) and verified the findings using theoretical calculations based on the modified Resistive Force Theory (RFT).

We present results of a search for spin-independent dark matter-nucleon interactions in a 1 cm$^2$ by 1 mm thick (0.233 gram) high-resolution silicon athermal phonon detector operated above ground. This sensor achieves an energy resolution of $\sigma_P =$ \SI{361.5(4)}{\milli\electronvolt}, the best for any athermal phonon detector to date. With an exposure of \SI{0.233}{\gram} $\times$ 12 hours, we place the most stringent constraints on dark matter masses between 44 and \SI{87}{\mega\electronvolt\per\square c}, with the lowest unexplored cross section of \SI{4e-32}{\square\centi\meter} at \SI{87}{\mega\electronvolt\per\square c}. We employ a conservative salting technique to reach the lowest dark matter mass ever probed via direct detection experiment. This constraint is enabled by two-channel rejection of low-energy backgrounds that are coupled to individual sensors.