Papers

A dual band receiver has been designed for the Black Hole Explorer (BHEX) mission, which is a space Very-Long Baseline Interferometry (VLBI) mission concept, aimed at unveiling the photon ring of black holes. The cryogenic receiver comprises a 228-320 GHz Superconductor-Insulator Superconductor (SIS) receiver, paired with a 76-106.7 GHz HEMT receiver. The details of the design are described in this talk. A novel comb generator, which will be used for delay tracking, has been designed and tested.

The Goos-Hänchen (GH) shift becomes giant in resonant photonic structures, making it promising for refractive index sensors with ultimate sensitivities. We provide here a complete formalism to analytically describe the GH shift and its associated sensitivity around the critical coupling regime in photonic structures. This analytical framework quantitatively connects physical parameters such as the quality factor, the angular dispersion, the beam size and the phase singularity to the GH shift. We numerically confirm this theory in two practical designs: a surface plasmon resonance sensor and a Bloch surface wave (BSW) metasurface sensor. Coupling our theory with numerical simulations, we design a BSW metasurface whose GH sensitivity ($10^{13} \mu m/RIU$) is more than 5 orders of magnitude higher than the current state-of-the art.We also reveal that the main practical limitation to reach ultimate GH sensitivities is the beam size. However, taking into account realistic beam sizes and introducing engineering dispersion for the metasurface, we calculate limits of detection for GH sensors as low as $10^{-13} RIU$ that still surpass current sensors. These results open the way for new sensing application needing high sensitivity and low limit of detection.

We develop a quantitative model of ionization processes in biological tissues under Ultra High Dose Rate (UHDR) radiation. The underlying conjecture is that of electron-hole liquid (EHL) forming in water based substances of biological tissues. Unlike the earlier known EHL in semiconductor crystals, the charge carriers here are low mobile due to strong interactions with the background (solvated electrons, etc.); hence, EHL resembling ionic melts. Similar to all ionic systems, the Coulomb coupling makes that EHL energetically favorable that leads to recombination barriers suppressing subsequent structural transformations. In particular, generation of secondary reactive species in such EHL becomes limited translating into reduction of biological damages and tissue sparing effect. We show how these processes are sensitive to the tissue quality and frequency dispersion of the dielectric permittivity. Equations for dose and dose rate defining the sparing thresholds are derived.

I discuss the constructions of boost-invariant dissipative conformal hydrodynamic flows by elaborating on the geometric procedure by Gubser and Yarom, which starts from a static, maximally symmetric flow on dS$_3\times\mathbb{R}$. Three foliations of dS$_3$ preserve three-dimensional non-Abelian isometry groups, namely, the flat ISO(2)-invariant, the spherical (closed) SO(3)-invariant, and the hyperbolic (open) SO(2,1)-invariant slicings. I show that the fluids that preserve these symmetries, after they have been Weyl transformed to flat spacetime, give rise to three physically distinct and boost-invariant solutions of the relativistic dissipative Navier-Stokes equations: the well-known and widely studied Bjorken and Gubser flows, and a seemingly thus far unexplored solution that arises from the hyperbolic slicing of dS$_3$. The new solution combines the radial expansion characteristic of the Gubser flow with the late-proper-time applicability of Bjorken's solution, and features a finite, radially bounded droplet whose expanding edge resembles a free-streaming shockwave.

Random telegraph signal (RTS) analysis is increasingly important for characterizing meaningful temporal fluctuations in physical, chemical, and biological systems. The simplest RTS arises from discrete stochastic switching events between two binary states, quantified by their transition amplitude and dwell times in each state. Quantitative analysis of RTSs provides valuable insights into microscopic processes such as charge trapping in semiconductors. However, analyzing RTS becomes considerably complex when signals exhibit multi-level structures or are corrupted by background white or pink noise. To address these challenges and support high-throughput RTS characterization, we propose a modular, training-free signal processing pipeline that integrates adaptive dual-tree complex wavelet transform (DTCWT) denoising with a lightweight Bayesian digitization strategy. The adaptive DTCWT denoiser incorporates autonomous parameter selection rules for its decomposition level and thresholds, optimizing white noise suppression without manual tuning. Our Bayesian digitizer formulates RTS level assignment as a probabilistic latent-state inference problem incorporating temporal regularization without iterative optimization, effectively resolving binary trap states even under residual notorious background pink noise. Quantitative benchmarking on large synthetic datasets with known ground truth demonstrates improved RTS reconstruction accuracy, trap-state resolution, and dwell-time estimation across diverse noise regimes and multi-trap scenarios, while achieving up to 83x speedups over classical and neural baselines. Qualitative validation on experimental RTS data when no ground truth is available illustrates practical usability and flexibility for real-time or large-scale analysis in real measurement settings.

Chronic wounds present a major challenge in elderly individuals due to diminished regenerative capacity and impaired tissue repair mechanisms associated with aging. In this study, we formulated a topical gel composed of zinc oxide (ZnO), silver (Ag), and cerium oxide (CeO2) nanoparticles, each chosen for their respective antimicrobial, antioxidant, and tissue-regenerative properties. The nanoparticles were synthesized through precise precipitation or reduction techniques and thoroughly characterized using UV-Vis spectroscopy, dynamic light scattering (DLS), Fourier-transform infrared spectroscopy (FTIR), and electron microscopy to confirm nanoscale structure and purity. An in vivo wound healing model utilizing aged Sprague-Dawley rats was employed, with animals divided into three groups: untreated, placebo-treated, and those receiving the nanoparticle-enriched gel. Wound dimensions were tracked for 14 days, revealing significantly improved healing in the nanoparticle-treated group, with nearly complete closure observed by day 14 (ANOVA, p less than 0.0001). Cytocompatibility was assessed via MTT assay on L929 fibroblasts, confirming greater than 80 percent viability at therapeutically relevant concentrations. These findings underscore the potential of multifunctional nanoparticle-based formulations to enhance wound healing in aged or compromised skin environments, offering a promising therapeutic avenue.

Plants are increasingly becoming a source of inspiration for robotics and engineers to develop bioinspired, adaptive, and multifunctional machines. In this study, we propose a bioinspired aquatic machine that mimics the fruit of the water caltrop (Trapa natans L.). Among various plant species, T. natans produces unique woody fruits that can disperse passively via water currents or by clinging to boats or waterfowls. Inspired by the structures and dispersal mechanisms of T. natans, we designed miniaturized biomimetic machines capable of passive dispersion in aquatic ecosystems. In order to study our selected biological model, we collected natural fresh and dried mature samples of T. natans fruits. We designed biomimetic aquatic machines by extracting the main geometrical details from the natural samples, and by exploiting advanced three-dimensional reconstruction techniques, including x-ray micro-computed topography (Micro-CT). Then, we successfully fabricate the biomimetic machines at high-resolution in two configurations (hollow body and solid body) using light-based bioprinting of photo-responsive hydrogels. We also characterized the mechanical properties of the bioprinted materials through compression tests. Finally, we evaluated the floating behavior of the biomimetic machines in a flow chamber as a proof of concept. This biomimetic approach enhances the adaptability of the machine in aquatic environments, offering new design insights for underwater, soft, and microrobotics.

The spreading and controlling of rumors have great impacts on our society. The transmission of infectious diseases and the spreading of rumors have some common scenarios. Like cross-infection propagation of diseases, two or many kinds of rumors or information may spread at the same time. In this paper, we propose a novel I2E2S2R rumor-spreading model in a homogeneous network. The rumor-free equilibrium, as well as the basic reproduction number, have been calculated from the mean-field equations of the model. Lyapunov function and the LaSalle invariance principle are used to establish the global stability of the rumor-free equilibrium. In numerical simulations, it is perceived that a higher degree of network helps to spread rumors quickly. We have also found that making people aware can help to disappear rumors faster from the network. In addition, making people divert from the rumor to exact information can lessen the spreading of the rumor.

The multipurpose nature of nuclear research reactors has been investigated. This class of reactors has gained momentum within the international community, given the multiple benefits of nuclear technology in medicine, agriculture, industry, and the development of new materials, being characterized by the simultaneous use of several applications. In this context, Brazil has recently joined the group of countries with multipurpose reactor projects. The Brazilian Multipurpose Reactor (RMB) will be Brazil's new neutron source, with a thermal power of $30$ $MW$, with a core flux on the order of $\phi=4\times 10^{14}$ $n/cm^{2}s$ and a power density of $312.5$ $W/cm^3$, ensuring the production of radioisotopes, material and fuel irradiation testing, and neutron beam applications. In this work, the demand for neutron fluxes in different irradiation targets is investigated by Monte Carlo simulations under a perturbation caused by a highly neutron-absorbing material, the Gadolinium-157 (Gd-157) isotope. The results show that Molybdenum-99 (Mo-99) production is not affected by the Gd-157 target, and the minimum proposed thermal neutron flux for the reactor's activity ($\phi_{min}=5.1\times 10^{12}$ $n/cm^{2}s$) is guaranteed even at maximum operational capacity. The potential production of Terbium (Tb) isotopes for nuclear medicine applications in the RMB is briefly discussed.

In this paper, a uniformly high-order discontinuous Galerkin gas kinetic scheme (DG-HGKS) is proposed to solve the Euler equations of compressible flows. The new scheme is an extension of the one-stage compact and efficient high-order GKS (CEHGKS, Li et al. , 2021. J. Comput. Phys. 447, 110661) in the finite volume framework. The main ideas of the new scheme consist of two parts. Firstly, starting from a fully discrete DG formulation, the numerical fluxes and volume integrals are expanded in time. Secondly, the time derivatives are replaced by spatial derivatives using the techniques in CEHGKS. To suppress the non-physical oscillations in the discontinuous regions while minimizing the number of "troubled cells", an effective limiter strategy compatible with the new scheme has been developed by combining the KXRCF indicator and the SHWENO reconstruction technique. The new scheme can achieve arbitrary high-order accuracy in both space and time, thereby breaking the previous limitation of no more than third-order accuracy in existing one-stage DG-HGKS schemes. Numerical tests in 1D and 2D have demonstrated the robustness and effectiveness of the scheme.

Interactive composition simulations in Earth System Models (ESMs) are computationally expensive as they transport numerous gaseous and aerosol tracers at each timestep. This limits higher-resolution transient climate simulations with current computational resources. ESMs like NASA GISS-ModelE3 (ModelE) often use pre-computed monthly-averaged atmospheric composition concentrations (Non-Interactive Tracers or NINT) to reduce computational costs. While NINT significantly cuts computations, it fails to capture real-time feedback between aerosols and other climate processes by relying on pre-calculated fields. We extended the ModelE NINT version using machine learning (ML) to create Smart NINT, which emulates interactive emissions. Smart NINT interactively calculates concentrations using ML with surface emissions and meteorological data as inputs, avoiding full physics parameterizations. Our approach utilizes a spatiotemporal architecture that possesses a well-matched inductive bias to effectively capture the spatial and temporal dependencies in tracer evolution. Input data processed through the first 20 vertical levels (from the surface up to 656 hPa) using the ModelE OMA scheme. This vertical range covers nearly the entire BCB concentration distribution in the troposphere, where significant variation on short time horizons due to surface-level emissions is observed. Our evaluation shows excellent model performance with R-squared values of 0.92 and Pearson-r of 0.96 at the first pressure level. This high performance continues through level 15 (808.5 hPa), then gradually decreases as BCB concentrations drop significantly. The model maintains acceptable performance even when tested on data from entirely different periods outside the training domain, which is a crucial capability for climate modeling applications requiring reliable long-term projections.

This article explores the application of various artificial intelligence techniques to the analysis of near-infrared (NIR) spectra of paracetamol, within the spectral range of 900 nm to 1800 nm. The main objective is to evaluate the performance of several dimensionality reduction algorithms; namely, Principal Component Analysis (PCA), Kernel PCA (KPCA), Sparse Kernel PCA, t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) in modeling and interpreting spectral features. These techniques, derived from data science and machine learning, are evaluated for their ability to simplify analysis and enhance the visualization of NIR spectra in pharmaceutical applications.

We present an investigation of one-photon valence-shell photoelectron spectroscopy and photoelectron circular dichroism (PECD) for the chiral molecule (1R,4R)-3-(heptafluorobutyryl)-(+)-camphor (HFC) and its europium complex Eu(III) tris[3-(heptafluorobutyryl)-(1R,4R)-camphorate] (Eu-HFC$_{3}$), the latter of which constitutes the heaviest organometallic molecule for which PECD has yet been measured. We discuss the role of keto-enol tautomerism in HFC, both as a free molecule and complexed in Eu-HFC$_{3}$. PECD is a uniquely sensitive probe of molecular chirality and structure such as absolute configuration, conformation, isomerisation, and substitution, and as such is in principle well suited to unambiguously resolving tautomers; however modeling remains challenging. For small organic molecules, theory is generally capable of accounting for experimentally measured PECD asymmetries, but significantly poorer agreement is typically achieved for the case of large open-shell systems. Here, we report PECD asymmetries ranging up to $\sim8\%$ for HFC and $\sim7\%$ for Eu-HFC$_{3}$, of similar magnitude to those reported previously for smaller isolated chiral molecules, indicating that PECD remains a practical experimental technique for the study of large, complicated chiral systems.

Efficient and reliable light coupling between optical fibers and photonic integrated circuits has arguably been the most essential issue in integrated photonics for optical interconnects, nonlinear signal conversion, neuromorphic computing, and quantum information processing. A commonly used approach is to use inverse tapers interfacing with lensed fibers, particularly for waveguides of relatively low refractive index, such as silicon nitride (Si3N4), silicon oxynitride, and lithium niobate. This approach simultaneously enables broad operation bandwidth, high coupling efficiency, and simplified fabrication. Although diverse taper designs have been invented and characterized to date, lensed fibers play equally important roles here, yet their optimization has long been underexplored. Here, we fill this gap and introduce a comprehensive co-optimization strategy that synergistically refines the geometries of the taper and the lensed fiber. By incorporating the genuine lensed fiber's shape into the simulation, we accurately capture its non-Gaussian emission profile, thereby nullifying the widely accepted approximation based on a paraxial Gaussian mode. We further characterize many lensed fibers and Si3N4 tapers of varying shapes using different fabrication processes. Our experimental and simulation results show remarkable agreement, both achieving maximum coupling efficiencies exceeding 80% per facet. Finally, we summarize the optimal choices of lensed fibers and Si3N4 tapers that can be directly deployed in modern CMOS foundries for scalable manufacturing of Si3N4 photonic integrated circuits. Our study not only contributes to light-coupling solutions but is also critical for photonic packaging and optoelectronic assemblies that are currently revolutionizing data centers and AI.

Traditional ultrasound simulation methods solve wave equations numerically, achieving high accuracy but at substantial computational cost. Faster alternatives based on convolution with precomputed impulse responses remain relatively slow, often requiring several minutes to generate a full B-mode image. We introduce UltraScatter, a probabilistic ray tracing framework that models ultrasound scattering efficiently and realistically. Tissue is represented as a volumetric field of scattering probability and scattering amplitude, and ray interactions are simulated via free-flight delta tracking. Scattered rays are traced to the transducer, with phase information incorporated through a linear time-of-flight model. Integrated with plane-wave imaging and beamforming, our parallelized ray tracing architecture produces B-mode images within seconds. Validation with phantom data shows realistic speckle and inclusion patterns, positioning UltraScatter as a scalable alternative to wave-based methods.

The spectral and statistical properties are explored for surface plasmon (SP) emission in resonance fluorescence from a driven two level emitter in the proximity of 2D single graphene sheet. We derive an exact closed form analytic expression for the emitted SP field valid in the near and far regions. The SP field profile and spectrum function depend on the graphene conductivity and take into account the dynamic control parameters, namely Fermi energy. We present analysis for the spectrum and second order coherence functions and discuss the possibility of their control using graphene system parameters to manipulate the spectral linewidth and second order coherence function.

Density functional theory (DFT) remains the most widely used electronic structure method. Although exact in principle, in practice, it relies on approximations to the exchange-correlation (XC) functional, which is known to be a unique functional of the electron density. Despite 50 years of active research, existing XC approximations remain far from general purpose chemical accuracy of various thermochemical and materials properties. In that light, the inverse DFT problem, of finding the exact XC potential corresponding to an accurate groundstate density, offers an insightful tool to understand the nature of the XC functional as well as aid in the development of more accurate functionals. However, solving the inverse DFT problem is fraught with several numerical challenges, such as non-uniqueness or spurious oscillations in the solution and non-convergence. We present invDFT as an open-source framework to address the outstanding challenges in inverse DFT and computed XC potentials solely from a target density. We do so by use of a systematically convergent finite-element basis and asymptotic corrections to the target density. We also employ several numerical and high-performance computing (HPC) advances that affords both efficiency and parallel scalability, on CPU-GPU hybrid architectures. We demonstrate the accuracy and scalability of invDFT using accurate full-configuration interaction (FCI) densities as well as model densities, ranging up to 100 electrons and spanning both weakly and strongly correlated molecules.

Solid-state nanopore and nanopipette sensors are powerful devices for the detection, quantification and structural analysis of biopolymers such as DNA and proteins, especially in carrier-enhanced resistive-pulse sensing. However, hundreds of different molecules typically need to be sampled from solution and analysed to obtain statistically robust information. This limits the applicability of such sensors and complicates associated workflows. Here, we present a new strategy to trap DNA structures in the sensing region of a nanopipette through end functionalisation and nanoparticle capping. We develop a robust set of descriptors to characterise the insertion and presence of nanoparticle-DNA constructs in the nanopipette tip and, furthermore, show that they remain mobile and responsive to external electric fields over extended periods of time. This allows for repeated readout of the same DNA structure and could enable new applications for such sensors, for example in flow and in confined environments.

We designed an ingenious all-dielectric metasurface, employing cuboid structures patterned with bow-tie-shaped nanoholes, exhibiting multiple Fano resonances induced by quasi-bound states in the continuum (quasi-BICs) through structural asymmetry. Among them, several resonant modes demonstrated high quality factors in the range of 10^3-10^4, along with near-unity modulation depth and strong spectral contrast. The optical responses were analyzed utilizing the finite-difference time-domain (FDTD) method, with Fano profiles fitted to theoretical models and the BIC governed modes validated via the squared inverse ratio law. Furthermore, multipolar decomposition and electromagnetic spatial field profile revealed the origins of the resonance, while LC circuit modeling provided additional physical insight into the Fano profiles. The proposed metasurface also exhibited strong polarization dependence, indicating its potential for active optical switching. Finally, refractive index sensing performance, including the detection of Vibrio cholerae, reached a sensitivity of 342 nm/RIU and a figure of merit of 217.14 RIU^-1. Advancing the control of high-Q quasi-BIC Fano resonances, this study highlights Fano resonators' potential for refractive index sensing and active switching.

Partial differential equations (PDEs) provide a mathematical foundation for simulating and understanding intricate behaviors in both physical sciences and engineering. With the growing capabilities of deep learning, data$-$driven approaches like Physics$-$Informed Neural Networks (PINNs) have been developed, offering a mesh$-$free, analytic type framework for efficiently solving PDEs across a wide range of applications. However, traditional PINNs often struggle with challenges such as limited precision, slow training dynamics, lack of labeled data availability, and inadequate handling of multi$-$physics interactions. To overcome these challenging issues of PINNs, we proposed a Gradient Enhanced Self$-$Training PINN (gST$-$PINN) method that specifically introduces a gradient based pseudo point self$-$learning algorithm for solving PDEs. We tested the proposed method on three different types of PDE problems from various fields, each representing distinct scenarios. The effectiveness of the proposed method is evident, as the PINN approach for solving the Burgers$'$ equation attains a mean square error (MSE) on the order of $10^{-3}$, while the diffusion$-$sorption equation achieves an MSE on the order of $10^{-4}$ after 12,500 iterations, with no further improvement as the iterations increase. In contrast, the MSE for both PDEs in the gST$-$PINN model continues to decrease, demonstrating better generalization and reaching an MSE on the order of $10^{-5}$ after 18,500 iterations. Furthermore, the results show that the proposed purely semi$-$supervised gST$-$PINN consistently outperforms the standard PINN method in all cases, even when solution of the PDEs are unavailable. It generalizes both PINN and Gradient$-$enhanced PINN (gPINN), and can be effectively applied in scenarios prone to low accuracy and convergence issues, particularly in the absence of labeled data.

Optical clocks have extremely attractive applications in many fields, including time-frequency metrology, validation of fundamental physical principles, and relativistic geodesy. The 467 nm octupole transition in 171Yb+ ion exhibits intrinsic insensitivity to magnetic field and an ultra-long clock state lifetime of 1.6 years. In addition, the entire laser system can be realized by semiconductor technologies, rendering this platform uniquely advantageous for developing high-precision, compact and transportable optical clocks. Here, we report the development of a compact optical clock based on the 467 nm transition of a single 171Yb+ ion. Using a narrow linewidth 467 nm laser to interrogate the clock transition, we obtain a near-Fourier-limited linewidth of 2.3 Hz in an integrated ion trapping system. Self-comparison demonstrated a frequency instability of 2.2E-15/sqrt(tau/s) with an interrogation time of 180 ms, which reaches the high parts in E-18 level with an averaging time of only one day. These work laid the technical foundation for the subsequent clock systematic evaluation and the packaging of each subsystem into an engineering prototype with high-precision at the level of E-18.

Integrated and tunable light sources are critical for advancing quantum nanophotonic chips in quantum computing, communications, and sensing. However, efficient and tunable emission amplification post-fabrication poses major challenges. Hybrid metasurfaces combining niobium pentoxide (Nb2O5), copper indium sulfide (CIS) quantum dots or hexagonal boron nitride (hBN), and antimony trisulfide (Sb2S3) as a low-loss phase-change material offer a compelling solution for dynamic control and amplification of photoluminescence and quantum light emission. In this work, we report an active hybrid metasurface supporting tunable bound states in the continuum (BIC) resonances in the visible regime, achieving experimental Q-factors up to 206 and strong amplification of CIS QDs photoluminescence as well as quantum light emission of hBN single-photon emitters. The metasurface enables BIC resonance shifts of 33.5 nm in the visible spectrum via phase transition of Sb2S3, and 17 nm through dimensional parametric tuning. We experimentally demonstrate highly directional photoluminescence amplification up to 33-fold, alongside broad tunable amplified PL emission upon Sb2S3 phase modulation. Furthermore, we propose amplified, tunable, and on-demand strong coupling of hBN single-photon emitters with the tunable BIC metasurface for next-generation broadband quantum nanophotonic chips. This work sets a new benchmark in reconfigurable nanophotonic platforms for efficient quantum light sources in integrated photonic systems.

The significance of small-scale forcing of particles on the carrier two-dimensional turbulent flow has been shown to influence the spectral scaling properties of the carrier fluid. We investigate possible consequences of such two-way coupling in a turbulent suspension of inertial particles through one- and two-point Eulerian and Lagrangian statistics. In particular, we find signatures of enhanced intermittency in the vorticity distributions. We characterize the changes in the small-scale geometry of the flow via the Okubo-Weiss parameter. Finally, we examine the scaling properties of the second-order vorticity structure functions and find a non-trivial form of scale-invariance at finite mass loading. Motivated by these observations, we propose an effective multiscale forcing framework in which particle feedback is modeled as a spatially localized small-scale forcing. This dual-scale forcing captures the emergence of modified spectral scaling and provides a minimal Eulerian description of particle-laden turbulence that reproduces key statistical signatures of the system.

The 2008 global financial crisis marked the beginning of a decade dominated by fiscal austerity policies in much of the developed world. This paper presents a qualitative narrative review of an extensive collection of academic literature to synthesize evidence on the multifaceted effects of austerity. Following a thematic approach inspired by PRISMA guidelines, the economic, social, and political consequences of these measures are examined. The analysis reveals a majority consensus regarding the recessive effects of austerity, especially when implemented during economic crises, with negative fiscal multipliers that often exacerbate GDP contraction. Socially, austerity is associated with rising inequality, negative impacts on public health, disproportionate gender consequences, and a weakening of social safety nets. Politically, evidence links austerity to the erosion of trust in institutions, a rise in populism, and electoral instability. Despite the political narrative presenting austerity as an inevitable necessity for fiscal sustainability, academic literature underscores its high costs and questionable efficacy, advocating for more contextualized and equitable economic policy approaches.

In this study, we discuss some general critical properties of bound states with one-boson-exchange potential. For simplicity, we first take a system with two identical scalar particles as an example. The interaction between these two scalar particles is described by the exchange of another massive scalar meson under the instantaneous approximation, which results in the Yukawa potential. A highly accurate numerical method is used to determine the critical mass value of the system. The resulting critical mass for the ground state is consistent with those reported in the literature, agreeing to about 35 significant figures. The highly accurate results for the $l=1$ case are also presented, which are significantly more precise than those previously reported in the literature. Furthermore, we extend the discussion to physical hadronic molecular states, where form factors are introduced in the interaction to describe the structure of hadrons. Our numerical results show that although the binding energies of the hadronic molecular states depend on the cutoff in the form factors, the number of hadronic molecular states is almost independent of the cutoffs across a very wide physical region. This indicates a strong and important property: for physically small couplings, the number of hadronic molecular states is almost solely determined by the coupling constants and the masses of the exchange particles. This highly accurate numerical method can also be straightforwardly applied to higher $l$ cases or other systems.

The detection of ultrafast optical and radio-frequency (RF) signals is crucial for applications ranging from high-speed communications to advanced sensing. However, conventional detectors are fundamentally constrained by their intrinsic bandwidth, limiting accurate broadband signal measurement. Here, we show that a neuromorphic photonic processing approach can overcome this limitation, enabling accurate broadband signal detection beyond the detector bandwidth. The key idea lies in the spatiotemporal encoding of input waveforms within a photonic reservoir network, which reconstructs high-frequency components otherwise inaccessible to individual detectors. We experimentally demonstrate the detection of high-speed optical phase signals with more than an eightfold effective bandwidth expansion using an on-chip silicon photonic reservoir. This approach provides a scalable and integrable platform for high-speed optical and RF signal processing, opening new opportunities in ultrafast photonics and next-generation communication systems.

Emergent behaviors are a defining feature of complex systems, yet their quantitative characterization remains an open challenge, as traditional classifications rely mainly on visual inspection of spatio-temporal patterns. In this Letter, we propose using the Mean Information Gain (MIG) as a metric to quantify emergence in Agent-Based Models. The MIG is a conditional entropy-based metric that quantifies the lack of information about other elements in a structure given certain known properties. We apply it to a multi-agent biased random walk that reproduces Wolfram's four behavioral classes and show that MIG differentiates these behaviors. This metric reconnects the analysis of emergent behaviors with the classical notions of order, disorder, and entropy, thereby enabling the quantitative classification of regimes as convergent, periodic, complex, and chaotic. This approach overcomes the ambiguity of qualitative inspection near regime boundaries, particularly in large systems, and provides a compact, extensible framework for identifying and comparing emergent behaviors in complex systems.

The use of several open source scientific packages in the Schrödinger Materials Science Suite will be discussed. A typical workflow for materials discovery will be described, discussing how open source packages have been incorporated at every stage. Some recent implementations of machine learning for materials discovery will be discussed, as well as how open source packages were leveraged to achieve results faster and more efficiently.

Heavy-tailed fluctuations and power law distributions pervade physics, biology, and the social sciences, with numerous mechanisms proposed for their emergence. Kesten processes, which are multiplicative stochastic recursions with additive noise or reinjection, provide a canonical explanation, where power law tails arise from transient supercritical excursions as eigenvalues intermittently cross the stability boundary. Here we uncover a distinct and more general mechanism in multidimensional systems: non-normal eigenvector amplification. In random non-normal matrices, the non-orthogonality of eigenvectors, quantified at each time step by the condition number $\kappa_t$ in Kesten-like processes, induces transient growth that increases the effective Lyapunov exponent $\gamma \to \gamma + \mathbb{E}\left[\ln \kappa_t \right]$ and lowers the tail exponent $\alpha \simeq -2\gamma / \sigma_{\kappa}^2$, where $\mathbb{E}\left[\ln \kappa_t \right]$ and $\sigma_{\kappa}^2$ are respectively the mean and variance of $\ln \kappa_t$. As the system dimension $N$ grows, $\kappa$ typically increases proportionally, making non-normal amplification the dominant source of scale-free behavior. We illustrate this mechanism in polymer stretching in turbulent flows, where intermittent extensions arise from eigenvector amplification of velocity gradients.

This study investigates the molecular-level mechanism of Alternating Current Electrohydrodynamic (AC-EHD) flow in nanopores under high-frequency conditions, using molecular dynamics simulations. A gold-NaCl system with symmetric and asymmetric electrode configurations is used to analyze the flow patterns under high-frequency AC potentials. Our findings reveal localized heat generation near the electrode leading to a steep temperature gradient. An order parameter analysis indicates that the heat generation is due to the periodic change in the alignment of water molecules under AC potentials. At these high frequencies the influence of Na$^+$ and Cl$^-$ ions are negligible. The heat generation and temperature gradient are found to increase with the applied AC frequency. Three different electrode configurations were studied by varying the size and distance between the electrodes. A net directional flow develops in the asymmetric electrode structures. A possible mechanism for this is proposed by analyzing the flow patterns using velocity and temperature profiles, order parameters, streamline plots and mean square displacements. Different effects on the fluid were identified including those associated with temperature gradients, temperature-dependent fluid properties, and non-uniform electric fields. The asymmetric electrode structure created an imbalance in these effects and generated a net directional flow. These findings suggest the existence of a form of nanoscale AC-EHD flow that operates in a frequency regime above that of conventional electroosmotic and electrothermal mechanisms and that, unlike these mechanisms, occurs independently of ionic concentration. Thereby this work provides insights for optimizing AC-EHD flow in nanoscale systems where precise fluid manipulation is critical.