This paper reviews the literature on response strategies for restoring infrastructure networks in the aftermath of a disaster. Our motivation for this review is twofold. First, the frequency and magnitude of natural and man-made disasters (e.g., wild fires, tornadoes, global pandemics, terrorist attacks) have been increasing. These events disrupt the operation of infrastructure networks, preventing the delivery of vital goods and services such as power and food. Therefore, it is critical to understand the state-of-the-art in responding to network disruptions in order to develop efficient strategies to mitigate their impacts. Second, it is critical to enable timely decisions in a rapidly changing and unpredictable environment while accounting for numerous interrelated factors. Because the vast majority of response decision problems are computationally challenging, quickly finding solutions that are compatible with real-time decision making is a difficult task. Hence, it is important to understand the nature of response activities and decisions, as well as the available solution methodologies and inherent trade-offs between computation time and solution quality. We review quantitative response methodologies developed for infrastructure network restoration, classifying relevant studies based on the properties of the underlying network. In particular, we focus on resource allocation, scheduling, routing and repair efforts within the domain of power, road, and water, oil and gas network restoration. We also discuss open research questions and future research directions.

We propose a heuristic model of the universe as a growing quasicrystal projected from a higher-dimensional lattice. By extending the Schr\"{o}dinger equation for a particle in a box with time-dependent boundaries, we derive an equation that resembles the Friedmann equation, addressing the Hubble tension. This model incorporates phonons and phasons, providing insights into cosmic-scale dynamics and the universe's expansion. We outline a pre-inflation tiling space phase with quantum error correction, a radiation phase dominated by quasiparticles, and a post-radiation phase with the emergence of matter. Our hypothesis, which posits that the universe is a growing quasicrystal, suggests that the necessity for an inflationary period may be obviated. Furthermore, phonon arising from this quasicrystalline structure could act as dark matter, influencing the dynamics of ordinary matter while remaining mostly undetectable by electromagnetic interactions. This hypothesis draws parallels with other crystalline matter at atomic scales that are fundamentally quantum in nature. We also explore how the notion of tiling space can support continuous symmetry atop a discrete structure, providing a novel framework for understanding the universe's expansion and underlying structure. Consequently, it is logical to start with quantum mechanics as the foundation of our model. Further development could enhance our understanding of cosmic expansion and the underlying structure of the universe.

The use of seismic waves to explore the subsurface underlying the ground is a widely used method in the oil industry, since different kinds of the rocks and mediums have different reflection rate of the seismic waves, so the amplitude of the reflected waves can unraveling the geological structure and lithologic character of a certain area under the ground, but the management and processing of seismic wave data often affects the efficiency of oil exploration and development. Different kinds of the seismic data bulk are always mixed and hard to be classified manually. This paper presents a classification model for four main types of seismic data, and proposed a classification method based on Mel-spectrum. An accuracy of 98.32% was achieved using pre-trained ResNet34 with transfer learning method. The accuracy is further improved compared with the pure fourier transformation method widely used in previous studies. Meanwhile, the transfer learning method and fine-tune strategy to train the neural network by training the first N-1 layers of the network separately and then train the fully connected layers further improves the training efficiency. Our model can also be seen as an efficient data quality control scheme for oil exploration and development. Meanwhile, our method is future-proofed, for further improvement of the seismic data processing quality control system, according to the spectrum characteristics, this model can be further extended into a error data classification model, reduces the workload of the bulk data management.

Formation of the giant Chicxulub crater off Mexico's Yucatan Peninsula coincided with deposition of the global Ir-rich Cretaceous-Tertiary (K-T) stratigraphic boundary layer at ca. 65 Ma. The boundary is marked most sharply by abundant spherules containing unaltered grains of magnesioferrite spinel. Here we predict for the first time the sequential condensation of solids and liquids from the plume of vaporized rock expected from oblique K-T impacts. We predict highly oxidizing plumes that condense silicate liquid droplets bearing spinel grains whose compositions closely match those marking the actual boundary. Systematic global variations in spinel composition are consistent with higher condensation temperatures for spinels found at Atlantic and European sites than for those in the Pacific.

Based on a recently proposed quantum field theory (QFT) for particles with or without structure, called "Structural Algebraic QFT (SAQFT)", we introduce a finite QFT. That is, a QFT for structureless elementary particles that does not require renormalization where loop integrals in the Feynman diagrams are finite. It is an algebraic theory utilizing orthogonal polynomials and based on the structureless sector of SAQFT.

In conventional path integral quantum mechanics, the integral variables are the canonical variables of Hamiltonian mechanics. We show that these integral variables can be transformed into the spacetime metric, leading to a new representation of quantum mechanics. We show that the wave-particle duality can be interpreted as the uncertainty of spacetime for the particle. Summarizing all possible trajectories in conventional path integral quantum mechanics can be transformed into the summation of all possible spacetime metrics. We emphasize that in conventional quantum gravity, it is possible that the classical matter fields correspond to the quantum spacetime. We argue that this is not quite reasonable and propose a new path integral quantum gravity model based on the new interpretation of wave-particle duality. In this model, the aforementioned drawback of conventional quantum gravity naturally disappears.

In this note, we revisit a variational principle introduced by Padmanabhan for describing gravitation using a field action composed of a boundary term. We demonstrate that this procedure can also be applied to derive Maxwell's and Yang-Mills equations. Additionally, we find that in this boundary approach, \(\mathcal{CP}\)-violating dual couplings and spontaneous symmetry breaking through gauge boson masses emerge in a manner analogous to the appearance of the cosmological constant in the original gravitational context.

Introduction: molecular geometry, the three-dimensional arrangement of atoms within a molecule, is fundamental to understanding chemical reactivity, physical properties, and biological activity. The prevailing models used to describe molecular geometry include the Valence Shell Electron Pair Repulsion (VSEPR) theory, hybridization theory, and molecular orbital theory. While these models provide significant insights, they also have inherent limitations. Applying string theory and graph theory with topological and macrotensorial methods could improve the understanding of molecular behavior. Objective: explore the potential applications of string and graph theory to material science, focusing on molecular geometry, electron domains, and phase changes via symmetries. Molecular geometry: each molecule is associated with a simple graph with an orthonormal representation inducing metrics via the usage of macrotensor operators, allowing the calculation of angles between molecules and following the equations of motion. Phase changes: a series of inequalities are proposed depending on the energy-momentum densities of bonds and the edges of the associated graph where electrons or atoms are located, its topology, and isometries, exploring possible new states of matter. Conclusions: application of macrotensors, graphs, and string theory to material science, specifically to molecular geometry and phase changes, allows for a more dynamic and flexible description of natural phenomena involving matter and the prediction of possible new states of matter. This presents a different perspective, opening possibilities for experimental confirmation and applications of the approach presented here.

Einstein's gravity in AdS space coupled to nonlinear electrodynamics (NED) with two parameters is studied. We investigate magnetically charged black holes. The metric and mass functions and their asymptotic are obtained showing that black holes may have one or two horizons. Thermodynamics in extended phase space was studied and it was proven that the first law of black hole thermodynamics and the generalized Smarr relation hold. The magnetic potential and the vacuum polarization conjugated to coupling (NED parameter), are computed and depicted. We calculate the Gibbs free energy and heat capacity.

In the ever-evolving landscape of technology, product innovation thrives on replacing outdated technologies with groundbreaking ones or through the ingenious recombination of existing technologies. Our study embarks on a revolutionary journey by genetically representing products, extracting their chromosomal data, and constructing a comprehensive phylogenetic network of automobiles. We delve deep into the technological features that shape innovation, pinpointing the ancestral roots of products and mapping out intricate product-family triangles. By leveraging the similarities within these triangles, we introduce a pioneering "Product Disruption Index"-inspired by the CD index (Funk and Owen-Smith, 2017)-to quantify a product's disruptiveness. Our approach is rigorously validated against the scientifically recognized trend of decreasing disruptiveness over time (Park et al., 2023) and through compelling case studies. Our statistical analysis reveals a fascinating insight: disruptive product innovations often stem from minor, yet crucial, modifications.

Big mobility datasets (BMD) have shown many advantages in studying human mobility and evaluating the performance of transportation systems. However, the quality of BMD remains poorly understood. This study evaluates biases in BMD and develops mitigation methods. Using Google and Apple mobility data as examples, this study compares them with benchmark data from governmental agencies. Spatio-temporal discrepancies between BMD and benchmark are observed and their impacts on transportation applications are investigated, emphasizing the urgent need to address these biases to prevent misguided policymaking. This study further proposes and tests a bias mitigation method. It is shown that the mitigated BMD could generate valuable insights into large-scale public transit systems across 100+ US counties, revealing regional disparities of the recovery of transit systems from the COVID-19. This study underscores the importance of caution when using BMD in transportation research and presents effective mitigation strategies that would benefit practitioners.

In this paper, we addressed the problem of choosing a nuclear fuel cycle. Ethical problems related to the choice of a nuclear fuel cycle, such as the depletion of natural uranium reserves, the accumulation of nuclear waste, and the connection with the problems of nonidentity and distributive justice are considered. We examined cultural differences in attitudes toward nuclear safety and the associated ambiguities in the choice of a nuclear fuel cycle. We suggested that the reduction in consumption of natural uranium does not seem to be a feasible way of reducing nuclear waste because of the nonidentity problem.

Discovered as an apparent pattern, a universal relation between geometry and information called the holographic principle has yet to be explained. This relation is unfolded in the present paper. As it is demonstrated there, the origin of the holographic principle lies in the fact that a geometry of physical space has only a finite number of points. Furthermore, it is shown that the puzzlement of the holographic principle can be explained by a magnification of grid cells used to discretize geometrical magnitudes such as areas and volumes into sets of points. To wit, when grid cells of the Planck scale are projected from the surface of the observable universe into its interior, they become enlarged. For that reason, the space inside the observable universe is described by the set of points whose cardinality is equal to the number of points that constitute the universe's surface.

In this paper, we introduce a new classical fractional particle model incorporating fractional first derivatives. This model represents a natural extension of the standard classical particle with kinetic energy being quadratic in fractional first derivatives and fractional linear momenta, similarly to classical mechanics. We derive the corresponding equations of motion and explore the symmetries of the model. Also, we present the formulation in terms of fractional potentials. Two important examples are analytically solved: the free particle and the particle subjected to generalized forces characterized by fractional first derivatives.

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM). Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, reducing the data collection time by a factor of about 400 and permitting reconstructions of weaker and three-dimensional current sources.

The flow patterns created by a coherent horizontal liquid jet impinging on a vertical wall atmoderate flow rates (jet flowrates 0.5-4.0 L min-1, jet velocities 2.6-21 m s-1) are studied withwater on glass, polypropylene and polymethylmethacrylate (acrylic, Perspex(R)) using a novelparticle image velicometry (PIV) technique employing nearly opaque fluid doped withartificial pearlescence to track surface velocity. Flow patterns similar to those reported inprevious studies are observed on each substrate: their dimensions differed owing to theinfluence of wall material on contact angle. The dimensions are compared with models for (i)the radial flow zone, reported by Wang et al. (2013b), and the part of the draining film belowthe jet impingement point where it narrows to a node. For (ii), the model presented by Mertenset al. (2005) is revised to include a simpler assumed draining film shape and an alternativeboundary condition accounting for surface tension effects acting at the film edge. This refinedmodel gives equally good or better fits to the experimental data. The effective contact anglewhich gives good agreement with the data is found to lie between the measured quasi-staticadvancing and receding contact angles, at approximately half the advancing value. The PIVmeasurements confirmed the existence of a thin fast moving film with radial flow surroundingthe point of impingement, and a wide draining film bounded by ropes of liquid below theimpingement point. While these measurements generally support the predictions of existingmodels, these models assume that the flow is steady. In contrast, surface waves were evident inboth regions and this partly explains the difference between the measured surface velocity andthe values estimated from the models.

Non-adiabatic coupling matrix elements (NACMEs) are important in quantum chemistry, particularly for molecular dynamics methods such as surface hopping. However, NACMEs are gauge dependent. This presents a difficulty for their calculation in general, where there are no restrictions on the gauge function except that it be differentiable. These cases are relevant for complex-valued electronic wave functions, such as those that arise in the presence of a magnetic field or spin-orbit coupling. Additionally, the Berry curvature and Berry force play an important role in molecular dynamics in a magnetic field, and are also relevant in the context of spin-orbit coupling. For methods such as surface hopping, excited-state Berry curvatures will also be of interest. With this in mind, we have developed a scheme for the calculation of continuous, differentiable NACMEs as a function of the molecular geometry for complex-valued wave functions. We demonstrate the efficacy of the method by using the H$_2$ molecule at the full configuration-interaction (FCI) level of theory. Additionally, ground- and excited- state Berry curvatures are computed for the first time using FCI theory. Finally, Berry phases are computed directly in terms of diagonal NACMEs.

We present an ab initio method for computing vibro-polariton and phonon-polariton spectra of molecules and solids coupled to the photon modes of optical cavities. We demonstrate that if interactions of cavity photon modes with both nuclear and electronic degrees of freedom are treated on the level of the cavity Born-Oppenheimer approximation (CBOA), spectra can be expressed in terms of the matter response to electric fields and nuclear displacements which are readily available in standard density functional perturbation theory (DFPT) implementations. In this framework, results over a range of cavity parameters can be obtained without the need for additional electronic structure calculations, enabling efficient calculations on a wide range of parameters. Furthermore, this approach enables results to be more readily interpreted in terms of the more familiar cavity-independent molecular electric field response properties, such as polarizability and Born effective charges which enter into the vibro-polariton calculation. Using corresponding electric field response properties of bulk insulating systems, we are also able to obtain $\Gamma$ point phonon-polariton spectra of two dimensional (2D) insulators. Results for a selection of cavity-coupled molecular and 2D crystal systems are presented to demonstrate the method.

Afforestation greatly influences several earth system processes, making it essential to understand these effects to accurately assess its potential for climate change mitigation. Although our understanding of forest-climate interactions has improved, significant knowledge gaps remain, preventing definitive assessments of afforestation's net climate benefits. In this review, focusing on the Canadian boreal, we identify these gaps and synthesize existing knowledge. The review highlights regional realities, Earth's climatic history, uncertainties in biogeochemical (BGC) and biogeophysical (BGP) changes following afforestation, and limitations in current assessment methodologies, emphasizing the need to reconcile these uncertainties before drawing firm conclusions about the climate benefits of afforestation. We hope that the identified gaps will drive the development of a more informed decision-making framework for Canadian afforestation policy, one that considers regional and future climatic contexts. Although we use the Canadian boreal as an example, most arguments in this review are applicable across the globe, particularly for the circumpolar nations.

Astronomy is at a turning point in its history and in its relations with the Indigenous peoples who are the generational stewards of land where several of our main observatories are located. The controversy regarding the further development of astronomy facilities on Maunakea is probably the most significant and publicized conflict about the use of such land in the name of science. Thousands have stood in resistance, elders were arrested, and the community is divided. Astronomy's access to one of its most emblematic sites is at risk. This situation challenges our professional practice, the projects we build on Indigenous lands, and our relationships with the people who live within these lands and with society in general. This paper attempts to share the perspective of the authors on the historical events, including the very recent past, through the lens of our understanding and opinions; to provide transparency, with humility, into our process of introspection and transformation; and to share our hopes and ambitions as leaders from Maunakea Observatories for the future of astronomy in Hawai'i, as advocated by the Astro2020 report from the U.S. National Academies of Sciences, Engineering, and Medicine; and to suggest ways for the profession to commit to this long-term vision.

Non-equilibrium electronic quantum transport is crucial for the operation of existing and envisioned electronic, optoelectronic, and spintronic devices. The ultimate goal of encompassing atomistic to mesoscopic length scales in the same nonequilibrium device simulation approach has traditionally been challenging due to the computational cost of high-fidelity coupled multiphysics and multiscale requirements. In this work, we present ELEQTRONeX (ELEctrostatic Quantum TRansport modeling Of Nanomaterials at eXascale), a massively-parallel GPU-accelerated framework for self-consistently solving the nonequilibrium Green's function formalism and electrostatics in complex device geometries. By customizing algorithms for GPU multithreading, we achieve orders of magnitude improvement in computational time, and excellent scaling on up to 512 GPUs and billions of spatial grid cells. We validate our code by computing band structures, current-voltage characteristics, conductance, and drain-induced barrier lowering for various 3D configurations of carbon nanotube field-effect transistors. We also demonstrate that ELEQTRONeX is suitable for complex device/material geometries where periodic approaches are not feasible, such as modeling of arrays of misaligned carbon nanotubes requiring fully 3D simulations.

We present a new boundary condition for simulations of magnetic reconnection based on the topology of a Klein bottle. When applicable, the new condition is computationally cheaper than fully periodic boundary conditions, reconnects more flux than systems with conducting boundaries, and does not require assumptions about regions external to the simulation as is necessary for open boundaries. The new condition reproduces the expected features of reconnection, but cannot be straightforwardly applied in systems with asymmetric upstream plasmas.

Searches for violations of the fundamental symmetries of parity P and time reversal T in atomic and molecular systems provide a powerful tool for precise measurements of the physics of and beyond the standard model. In this work, we investigate how these symmetry violations affect the response of atoms and molecules to applied electric and magnetic fields. We recover well-known observables like the P-odd, T-odd spin-electric field coupling that is used for searches of the electron electric dipole moment (eEDM) or the effect of P-odd, T-even optical rotation in atomic gases. Besides these, we obtain several other possible observables. This includes, in particular, effects that can only be seen when using oscillating or inhomogeneous fields.

A magnitude-least-squares radiofrequency pulse design algorithm is reported which uses interleaved exact and stochastically-generated inexact updates to escape local minima and find low-cost solutions. Inexact updates are performed using a small randomly selected minibatch of the available B1+ measurements to update RF pulse weights, which perturbs the sequence of alternating projections. Applications to RF shimming, parallel transmit spokes RF pulse design, and spectral-spatial RF pulse design are considered. Numerical and simulation studies characterized the optimal minibatch size, which was found to consistently produce lower power and lower RMSE solutions across subjects, coil geometries, B1+ resolutions and orientations. The method was validated in-vivo at 7 Tesla and produced improvements in image quality in a slice-by-slice RF-shimmed imaging sequence. Compared to conventional methods, the pulse design method can more robustly design RF pulses that correct for B1+ inhomogeneities at ultra-high field strengths, and enable pulse designs to be completed with increased computational efficiency

As the focus of quantum science shifts from basic research to development and implementation of applied quantum technology, calls for a robust, diverse quantum workforce have increased. However, little research has been done on the design and impact on participants of workforce preparation efforts outside of R1 contexts. In order to begin to answer the question of how program design can or should attend to the needs and interests of diverse groups of students, we performed interviews with students from two Colorado-based quantum education/workforce development programs, one in an undergraduate R1 setting and one in a distributed community setting and serving students largely from two-year colleges. Through analysis of these interviews, we were able to highlight differences between the student populations in the two programs in terms of participation goals, prior and general awareness of quantum science, and career interest and framing of career trajectories. While both groups of students reported benefits from program participation, we highlight the ways in which students' different needs and contexts have informed divergent development of the two programs, framing contextual design of quantum education and workforce efforts as an issue of equity and representation for the burgeoning quantum workforce.

It has long been a challenging task to improve the light collection efficiency of conventional image sensors built with color filters that inevitably cause the energy loss of out-of-band photons. Although various schemes have been proposed to address the issue, it is still very hard to make a reasonable tradeoff between device performance and practicability. In this work, we demonstrate a pixelated spectral router based on sparse meta-atom array, which can efficiently separate the incident R (600-700 nm), G (500-600 nm), and B (400-500 nm) band light to the corresponding pixels of a Bayer image sensor, providing over 56% signal enhancement above the traditional color filter scheme. The CMOS-compatible spectral router has superior characteristics of polarization insensitivity and high incident angle tolerance (over 30{\deg}), enabled by simple compound Si3N4 nanostructures which are very suitable for massive production. Imaging experiments are conducted to verify its potential for real applications. Our pixelated spectral router scheme is also found to be robust and could be freely adapted to image sensors of various pixel sizes, having great potential in building the new generation of high-performance image sensing components.

We present an analytical description of the Alchemical Transfer Method (ATM) for molecular binding using the Potential Distribution Theory (PDT) formalism. ATM models the binding free energy by mapping the bound and unbound states of the complex by translating the ligand coordinates. PDT relates the free energy and the probability densities of the perturbation energy along the alchemical path to the probability density at the initial state, which is the unbound state of the complex in the case of a binding process. Hence, the ATM probability density of the transfer energy at the unbound state is first related by a convolution operation to the probability densities for coupling the ligand to the solvent and coupling it to the solvated receptor, for which analytical descriptions are available with parameters obtained from maximum likelihood analysis of data from double-decoupling alchemical calculations. PDT is then used to extend this analytical description along the alchemical transfer pathway. We tested the theory on the alchemical binding of five guests to the TEMOA host from the SAMPL8 benchmark set. In each case, the probability densities of the perturbation energy for transfer along the alchemical transfer pathway obtained from numerical calculations match those predicted from the theory and double-decoupling simulations. The work provides a solid theoretical foundation for alchemical transfer, offers physical insights on the form of the probability densities observed in alchemical transfer calculations, and confirms the conceptual and numerical equivalence between the alchemical transfer and double-decoupling processes.

Radium-225 (nuclear spin $I=1/2$) ions possess electronic hyperfine transitions that are first-order insensitive to magnetic field noise, which is advantageous for optical clocks and quantum information science. We report on laser cooling and trapping of radium-225 ions and hyperfine splitting measurements of the ion's $7s$ $^2S_{1/2}$, $7p$ $^2P_{1/2}$, and $6d$ $^2D_{3/2}$ states. We measured the ground state hyperfine constant, $A(^2S_{1/2}) = -27.684511056(9)\ \mathrm{GHz}$, and the quadratic Zeeman coefficient, $C_2 = 142.3(10)\ \mathrm{Hz\ G}^{-2}$, of the $^2S_{1/2} (F=0, m_F = 0) \leftrightarrow~^2S_{1/2} (F=1, m_{F} = 0)$ transition. We also measured the hyperfine constants of the $^2P_{1/2}$ state, $A(^2P_{1/2}) = -5.447(4)\ \mathrm{GHz}$, and the $^2D_{3/2}$ state, $A(^2D_{3/2}) = -619.7(11)\ \mathrm{MHz}$.

Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional methods utilize multiple sensors and polarization-filtering optics, resulting in reduced image resolution due to the loss of signal information at each photodetector. We propose a compressive sensing technique that mitigates information loss by using a low-refractive-index, self-assembled optical encoders. These optical nanostructures scatter light into polarization-dependent lattice modes and respond to input polarization ellipticity. Our study reveals that the wavefront direction and the polarization are encoded in the interference patterns and the linear component of the diffraction. Combining optical encoders with a shallow neural network, the system predicts pointing and polarization when equally spaced sensors are separated by less than 20{\deg}. Comparison of polycrystalline and short-range ordered optical encoders shows that the latter has a higher compression ratio. Our work centers on the unexpected modulation and spatial multiplexing of incident light polarization by self-assembled hollow nanocavity arrays as a new class of materials for optical computing, compressed sensing and imaging.

Existing methods for calculating substructure characteristic modes require treating interconnected metal structures as a single entity to ensure current continuity between different metal bodies. However, when these structures are treated as separate entities, existing methods exhibit inaccuracies, affecting the assessment of structural performance. To address this challenge, we propose an enhanced electromagnetic model that enables accurate characteristic mode analysis for regional structures within interconnected metal bodies. Numerical results validate the accuracy of the proposed method, and an antenna design example demonstrates its practical utility.

In the self-consistent dynamo limit, the magnetic feedback on the velocity field is sufficiently strong to induce a change in the topology of the magnetic field. Consequently, the magnetic energy reaches a state of non-linear saturation. Here, we investigate the role played by helical and non-helical drives in the triggering and the eventual saturation of a self-consistent dynamo. Evidence of small-scale dynamo (SSD) activity is found for both helical and non-helical forcing, driven at the largest possible scale. Based on the spectrum analysis, we find that the evolution of kinetic energy follows Kolmogorov's $k^-{\frac{5}{3}}$ law while that of magnetic energy follows Kazantsev's $k^{\frac{3}{2}}$ scaling. Also, we have verified that the aforementioned scalings remain valid for various magnetic Prandtl numbers (Pm). Statistical analysis is found to support our numerical finds.

Based on an accurate determination of the potential energy surfaces of Rb$_3^+$ correlated to its first asymptotic limit Rb$^+$$+$Rb($5s$)$+$Rb($5s$), we identify the presence of intersections of a pair of singlet and triplet surfaces over all interparticle distances, leading to Jahn-Teller couplings. We elaborate scenarios for charge exchange between ultracold charged atom-dimer complex (Rb$+$Rb$_2^+$ or Rb$^+$$+$Rb$_2$), predicting a strong selectivity on the preparation of the initial state of the dimer. We also demonstrate that the JT couplings must drive the three-body recombination (TBR) of Rb$^+$, Rb, and Rb at ultracold energies. Using the current analysis, we provide a consistent picture of the TBR experiments performed in ion-atom hybrid Rb samples \cite{dieterle2020inelastic,harter2012single}. We also demonstrate the presence of JT coupling as a general phenomenon in the singly-charged homonuclear alkali triatomic systems.

A fundamental challenge in endoscopy is how to fabricate a small fiber-optic probe that can achieve comparable function to probes with large, complicated optics (e.g., high resolution and extended depth of focus). To achieve high resolution over an extended depth of focus (DOF), the application of needle-like beams has been proposed. However, existing methods using miniaturized needle beam designs fail to adequately correct astigmatism and other monochromatic aberrations, limiting the resolution of at least one axis. Here, we describe a novel approach to realize freeform beam-shaping endoscopic probes via two-photon direct laser writing, also known as micro 3D-printing. We present a design achieving approximately 8-micron resolution with a DOF of >0.8 mm at a central wavelength of 1310 nm. The probe has a diameter of 0.25 mm (without the catheter sheaths) and is fabricated using a single printing step directly on the optical fiber. We demonstrate our device in intravascular imaging of living atherosclerotic pigs at multiple time points, as well as human arteries with plaques ex vivo. This is the first step to enable beam-tailoring endoscopic probes which achieve diffraction-limited resolution over a large DOF.

Bound states in the continuum (BICs), which are confined optical modes exhibiting infinite quality factors and carrying topological polarization configurations in momentum space, have recently sparked significant interest across both fundamental and applied physics.} Here we show that breaking time-reversal symmetry by external magnetic field enables a new form of chiral BICs with spin-orbit locking. Applying a magnetic field to a magneto-optical photonic crystal slab lifts doubly degenerate BICs into a pair of chiral BICs carrying opposite pseudo-spins and orbital angular momenta. Multipole analysis verifies the non-zero angular momenta and reveals the spin-orbital-locking behaviors. In momentum space, we observe ultrahigh quality factors and near-circular polarization surrounding chiral BICs, enabling potential applications in spin-selective nanophotonics. Compared to conventional BICs, the magnetically-induced chiral BICs revealed here exhibit distinct properties and origins, significantly advancing the topological photonics of BICs by incorporating broken time-reversal symmetry.

We study the effect of random perturbations in the Human and Nature Dynamics (HANDY) model. HANDY models the interactions between human population, depletion, and consumption of natural resources. HANDY explains how endogenous human--nature interactions could lead to sustainability or collapse in past societies. We introduce a Gaussian random noise perturbation on the population change to represent generic external perturbations. The robustness of the results is investigated with statistical analysis based on probability distributions of specific events. Our study shows that the results of the unperturbed HANDY model are robust under small perturbations of $\lesssim$ 10\% of the Human population. Our results confirm that endogenous dynamics drive the societal cycles. However, exogenous perturbations, such as floods, droughts, earthquakes, volcanic eruptions, infectious disease, epidemics, and wars, can accelerate or delay a collapse cycle.

Rational approximation has proven to be a powerful method for solving two-dimensional (2D) fluid problems. At small Reynolds numbers, 2D Stokes flows can be represented by two analytic functions, known as Goursat functions. Xue, Waters and Trefethen [SIAM J. Sci. Comput., 46 (2024), pp. A1214-A1234] recently introduced the LARS algorithm (Lightning-AAA Rational Stokes) for computing 2D Stokes flows in general domains by approximating the Goursat functions using rational functions. In this paper, we introduce a new algorithm for computing 2D Stokes flows in periodic channels using trigonometric rational functions, with poles placed via the AAA-LS algorithm [Costa and Trefethen, European Congr. Math., 2023] in a conformal map of the domain boundary. We apply the algorithm to Poiseuille and Couette problems between various periodic channel geometries, where solutions are computed to at least 6-digit accuracy in less than 1 second. The applicability of the algorithm is highlighted in the computation of the dynamics of fluid particles in unsteady Couette flows.

Plasmonic double helical antennas are a means to funnel circularly polarized states of light down to the nanoscale. Here, an existing design tool for single helices is extended to the case of double helices and used to design antennas that combine large chiroptical interaction strength with highly directional light emission. Full-field numerical modeling underpins the design and provides additional insight into surface charge distributions and resonance widths. The experimentally realized double helical antennas were studied regarding their polarization-dependent transmission behavior resulting in a large and broadband dissymmetry factor in the visible range. Since the polarization of light is an important tool for implementing logic functionality in photonic and quantum photonic devices, these helices are potential building blocks for future nanophotonic circuits, but also for chiral metamaterials or phase plates.

The COVID-19 pandemic has shown the urgent need for the development of efficient, durable, reusable and recyclable filtration media for the deep-submicron size range. Here we demonstrate a multifunctional filtration platform using porous metallic nanowire foams that are efficient, robust, antimicrobial, and reusable, with the potential to further guard against multiple hazards. We have investigated the foam microstructures, detailing how the growth parameters influence the overall surface area and characteristic feature size, as well as the effects of the microstructures on the filtration performance. Nanogranules deposited on the nanowires during electrodeposition are found to greatly increase the surface area, up to 20 m$^{2}$/g. Surprisingly, in the high surface area regime, the overall surface area gained from the nanogranules has little correlation with the improvement in capture efficiency. However, nanowire density and diameter play a significant role in the capture efficiency of PM$_{0.3}$ particles, as do the surface roughness of the nanowire fibers and their characteristic feature sizes. Antimicrobial tests on the Cu foams show a >99.9995% inactivation efficiency after contacting the foams for 30 seconds. These results demonstrate promising directions to achieve a highly efficient multifunctional filtration platform with optimized microstructures.

A phenomenon of racial segregation in U.S. cities is a multifaceted area of study. A recent advancement in this field is the development of a methodology that transforms census population count-by-race data into a grid of monoracial cells. This format enables assessment of heterogeneity of segregation within a city. This paper leverages such a grid for the quantification of race-constrained population patterns, allowing for the calculation and mapping of binary segregation patterns within arbitrary region. A key innovation is the application of Multifractal Analysis (MFA) to quantify the residency patterns of race-constrained populations. The residency pattern is characterized by a multifractal spectrum function, where the independent variable is a local metric of pattern's "gappiness", and the dependent variable is proportional to the size of the sub-pattern consisting of all locations having the same value of this metric. In the context of binary populations, the gappiness of the race-constrained population's pattern is intrinsically linked to its segregation. This paper provides a comprehensive description of the methodology, illustrated with examples focusing on the residency pattern of Black population in the central region of Washington, DC. Further, the methodology is demonstrated using a sample of residency patterns of Black population in fourteen large U.S. cities. By numerically describing each pattern through a multifractal spectrum, the fourteen patterns are clustered into three distinct categories, each having unique characteristics. Maps of local gappiness and segregation for each city are provided to show the connection between the nature of the multifractal spectrum and the corresponding residency and segregation patterns. This method offers an excellent quantification of race-restricted residency and residential segregation patterns within U.S. cities.

The dynamic model is one of the most successful inventions in subgrid-scale (SGS) modeling as it alleviates many drawbacks of the static coefficient SGS stress models. The model coefficient is often calculated dynamically through the minimization of the Germano-identity error (GIE). However, the driving mechanism behind the dynamic model's success is still not well understood. In wall-bounded flows, we postulate that the principal directions of the resolved rate-of-strain tensor play an important role in the dynamic models. Specifically, we find that minimization of the GIE along only the three principal directions (or less), in lieu of its nine components in its original formulation, produces equally comparable results as the original model when examined in canonical turbulent channel flows, a three-dimensional turbulent boundary layer, and a separating flow over periodic hills. This suggests that not all components of the Germano identity are equally important for the success of the dynamic model, and that there might be dynamically more important directions for modeling the subgrid dynamics.

The restoration of images affected by blur and noise has been widely studied and has broad potential for applications including in medical imaging modalities like computed tomography (CT). Although the blur and noise in CT images can be attributed to a variety of system factors, these image properties can often be modeled and predicted accurately and used in classical restoration approaches for deconvolution and denoising. In classical approaches, simultaneous deconvolution and denoising can be challenging and often represent competing goals. Recently, deep learning approaches have demonstrated the potential to enhance image quality beyond classic limits; however, most deep learning models attempt a blind restoration problem and base their restoration on image inputs alone without direct knowledge of the image noise and blur properties. In this work, we present a method that leverages both degraded image inputs and a characterization of the system blur and noise to combine modeling and deep learning approaches. Different methods to integrate these auxiliary inputs are presented. Namely, an input-variant and a weight-variant approach wherein the auxiliary inputs are incorporated as a parameter vector before and after the convolutional block, respectively, allowing easy integration into any CNN architecture. The proposed model shows superior performance compared to baseline models lacking auxiliary inputs. Evaluations are based on the average Peak Signal-to-Noise Ratio (PSNR), selected examples of good and poor performance for varying approaches, and an input space analysis to assess the effect of different noise and blur on performance. Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics, to enhance the performance of the model in image restoration tasks.

The recently introduced structured input-output analysis is a powerful method for capturing nonlinear phenomena associated with incompressible flows, and this paper extends that method to the compressible regime. The proposed method relies upon a reformulation of the compressible Navier-Stokes equations, which allows for an exact quadratic formulation of the dynamics of perturbations about a steady base flow. To facilitate the structured input-output analysis, a pseudo-linear model for the quadratic nonlinearity is proposed and the structural information of the nonlinearity is embedded into a structured uncertainty comprising unknown `perturbations'. The structured singular value framework is employed to compute the input-output gain, which provides an estimate of the robust stability margin of the flow perturbations, as well as the forcing and response modes that are consistent with the nonlinearity structure. The analysis is then carried out on a plane, laminar compressible Couette flow over a range of Mach numbers. The structured input-output gains identify an instability mechanism, characterized by a spanwise elongated structure in the streamwise-spanwise wavenumber space at a subsonic Mach number, that evolves into an oblique structure at sonic and supersonic Mach numbers. In addition, the structured input-output forcing and response modes provide insight into the thermodynamic and momentum characteristics associated with a source of instability. Comparisons with a resolvent/unstructured analysis reveal discrepancies in the distribution of input-output gains over the wavenumber space as well as in the modal behavior of an instability, thus highlighting the strong correlation between the structural information of the nonlinearity and the underlying flow physics.

Plasma instabilities are a major concern in plasma science, for applications ranging from particle accelerators to nuclear fusion reactors. In this work, we consider the possibility of controlling such instabilities by adding an external electric field to the Vlasov--Poisson equations. Our approach to determining the external electric field is based on conducting a linear analysis of the resulting equations. We show that it is possible to select external electric fields that completely suppress the plasma instabilities present in the system when the equilibrium distribution and the perturbation are known. In fact, the proposed strategy returns the plasma to its equilibrium with a rate that is faster than exponential in time. We further perform numerical simulations of the nonlinear two-stream and bump-on-tail instabilities to verify our theory and to compare the different strategies that we propose in this work.

PyDDC is a particle tracking reservoir simulator capable of solving non-linear density driven convection of single phase carbon-dioxide ($\mathrm{CO_2}$)--brine fluid mixture in saturated porous media at the continuum scale. In contrast to the sate-of-the-art Eulerian models, PyDDC uses a Lagrangian approach to simulate the Fickian transport of single phase solute mixtures. This introduces additional flexibility of incorporating anisotropic dispersion and benefits from having no numerical artifacts in its implementation. It also includes $\mathrm{CO_2}$--brine phase equilibrium models, developed by other researchers, to study the overall dynamics in the presence of electrolyte brine at different pressure and temperatures above the critical point of $\mathrm{CO_2}$. We demonstrate the implementation procedure in depth, outlining the overall structure of the numerical solver and its different attributes that can be used for solving specific tasks.

Traditional mobility management strategies emphasize macro-level mobility oversight from traffic-sensing infrastructures, often overlooking safety risks that directly affect road users. To address this, we propose a Digital Twin-based Driver Risk-Aware Intelligent Mobility Analytics (DT-DIMA) system. The DT-DIMA system integrates real-time traffic information from pan-tilt-cameras (PTCs), synchronizes this data into a digital twin to accurately replicate the physical world, and predicts network-wide mobility and safety risks in real time. The system's innovation lies in its integration of spatial-temporal modeling, simulation, and online control modules. Tested and evaluated under normal traffic conditions and incidental situations (e.g., unexpected accidents, pre-planned work zones) in a simulated testbed in Brooklyn, New York, DT-DIMA demonstrated mean absolute percentage errors (MAPEs) ranging from 8.40% to 15.11% in estimating network-level traffic volume and MAPEs from 0.85% to 12.97% in network-level safety risk prediction. In addition, the highly accurate safety risk prediction enables PTCs to preemptively monitor road segments with high driving risks before incidents take place. Such proactive PTC surveillance creates around a 5-minute lead time in capturing traffic incidents. The DT-DIMA system enables transportation managers to understand mobility not only in terms of traffic patterns but also driver-experienced safety risks, allowing for proactive resource allocation in response to various traffic situations. To the authors' best knowledge, DT-DIMA is the first urban mobility management system that considers both mobility and safety risks based on digital twin architecture.

Steady blood flow, or Poiseuille flow, through compressed or defective blood vessels is a critical issue in hemodynamics, particularly in cardiovascular studies. This research explores a tube with a bipolar cross-section, simulating the geometry of a bicuspid aortic valve (BAV) during an oval systolic opening. The BAV, typically featuring two cusps instead of the usual three found in normal tricuspid configurations, introduces unique hemodynamic challenges. As the most prevalent congenital heart defect, BAV increases the risk of aortic dilation and dissection. A bipolar cross-sectional analysis provides a more accurate geometric approximation for modeling flow through these atypical valve shapes, crucial for understanding the specific fluid dynamics associated with BAV. We derived an exact solution for the governing equations of Poiseuille flow within a bipolar cross-sectional tube, including velocity field, flow rate, and wall shear stress (WSS). The velocity profiles for BAV show remarkable agreement with previous studies using coherent multi-scale simulations, consistently demonstrating a jet-like flow structure absent in tricuspid aortic valve (TAV) scenarios. Analysis reveals that at the center of the entrance, BAV blood flow velocity is significantly higher than TAV but decreases more rapidly towards the vessel wall, creating a steeper vertical velocity gradient and resulting in higher WSS for BAV. Additionally, the WSS, inversely proportional to sin({\xi}*), where {\xi}* represents the bipolar coordinate at the wall boundary, exceeds that found in a circular cylindrical tube with an equivalent diameter. In cases of aortic valve stenosis, where {\xi}* approaches {\pi}, the WSS increases rapidly. This elevated WSS, commonly observed in BAV patients, may detrimentally impact the aortic wall in these structurally abnormal valves, particularly within the ascending aorta.

Lithium-ion batteries are widely used in electric vehicles and grid energy storage systems. Compared to cylindrical batteries, prismatic cells are the primary choice because of their advantage for dense packing. However, thermal runaway and temperature inhomogeneities are the main thermal regulation problems that affect their reliability, safety, and useful life. Here, we propose and assess a multifaceted cooling system composed of water channels (active cooling) and metallic foam embedded with two types of phase-change materials or PCMs (passive cooling) with different melting points. We show that a multifaceted thermal regulation strategy can improve both cooling effectiveness and temperature homogeneity through a representative prismatic battery module. Our numerical results indicate that for a battery pack cooled with a water channel (3C discharge rate), a dual-PCM arrangement can reduce the maximum temperature by 1.3 $^\circ$C and 2.7 $^\circ$C compared to a mono-PCM arrangement and a battery pack without PCM. The maximum temperature difference within the cell is also 1.2 $^\circ$C. Therefore, multi-PCM thermal management systems show better performance than their mono-PCM predecessors in terms of lowering the maximum battery temperature and improving thermal homogeneity. This work motivates the development of multifaceted thermal management systems with active and passive cooling to improve the long-term performance of electrochemical battery cells.

Turbulent convection in the interiors of the Sun and the Earth occurs at high Rayleigh numbers $Ra$, low Prandtl numbers $Pr$, and different levels of rotation rates. To understand the combined effects better, we study rotating turbulent convection for $Pr = 0.021$ (for which some laboratory data corresponding to liquid metals are available), and varying Rossby numbers $Ro$, using direct numerical simulations (DNS) in a slender cylinder of aspect ratio 0.1; this confinement allows us to attain high enough Rayleigh numbers. We are motivated by the earlier finding in the absence of rotation that heat transport at high enough $Ra$ is similar between confined and extended domains. We make comparisons with higher aspect ratio data where possible. We study the effects of rotation on the global transport of heat and momentum as well as flow structures (a) for increasing rotation at a few fixed values of $Ra$ and (b) for increasing $Ra$ (up to $10^{10}$) at the fixed, low Ekman number of $1.45 \times 10^{-6}$. We compare the results with those from unity $Pr$ simulations for the same range of $Ra$ and $Ro$, and with the non-rotating case over the same range of $Ra$ and low $Pr$. We find that the effects of rotation diminish with increasing $Ra$. These results and comparison studies suggest that, for high enough $Ra$, rotation alters convective flows in a similar manner for small and large aspect ratios, and so useful insights on the effects of high thermal forcing on convection can be obtained by considering slender domains.

Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer from sparse sensor distribution and geographically localized earthquake locations, while physics-based methods are computationally intensive and require accurate representations of Earth structures and earthquake sources. We propose a novel artificial intelligence (AI) simulator, Conditional Generative Modeling for Ground Motion (CGM-GM), to synthesize high-frequency and spatially continuous earthquake ground motion waveforms. CGM-GM leverages earthquake magnitudes and geographic coordinates of earthquakes and sensors as inputs, learning complex wave physics and Earth heterogeneities, without explicit physics constraints. This is achieved through a probabilistic autoencoder that captures latent distributions in the time-frequency domain and variational sequential models for prior and posterior distributions. We evaluate the performance of CGM-GM using small-magnitude earthquake records from the San Francisco Bay Area, a region with high seismic risks. CGM-GM demonstrates a strong potential for outperforming a state-of-the-art non-ergodic empirical ground motion model and shows great promise in seismology and beyond.

Materials with electromagnetic interference (EMI) shielding in the terahertz (THz) regime, while minimizing the quantity used, are highly demanded for future information communication, healthcare and mineral resource exploration applications. Currently, there is often a trade-off between the amount of material used and the absolute EMI shielding effectiveness (EESt) for the EMI shielding materials. Here, we address this trade-off by harnessing the unique properties of two-dimensional (2D) beta12-borophene (beta12-Br) nanosheets. Leveraging beta12-Br's light weight and exceptional electron mobility characteristics, which represent among the highest reported values to date, we simultaneously achieve a THz EMI shield effectiveness (SE) of 70 dB and an EESt of 4.8E5 dB cm^2/g (@0.87 THz) using a beta12-Br polymer composite. This surpasses the values of previously reported THz shielding materials with an EESt less than 3E5 dB cm^2/g and a SE smaller than 60 dB, while only needs 0.1 wt.% of these materials to realize the same SE value. Furthermore, by capitalizing on the composite's superior mechanical properties, with 158% tensile strain at a Young's modulus of 33 MPa, we demonstrate the high-efficiency shielding performances of conformably coated surfaces based on beta12-Br nanosheets, suggesting their great potential in EMI shielding area.

Effective frequency control in power grids has become increasingly important with the increasing demand for renewable energy sources. Here, we propose a novel strategy for resolving this challenge using graph convolutional proximal policy optimization (GC-PPO). The GC-PPO method can optimally determine how much power individual buses dispatch to reduce frequency fluctuations across a power grid. We demonstrate its efficacy in controlling disturbances by applying the GC-PPO to the power grid of the UK. The performance of GC-PPO is outstanding compared to the classical methods. This result highlights the promising role of GC-PPO in enhancing the stability and reliability of power systems by switching lines or decentralizing grid topology.

With the establishment of machine learning (ML) techniques in the scientific community, the construction of ML potential energy surfaces (ML-PES) has become a standard process in physics and chemistry. So far, improvements in the construction of ML-PES models have been conducted independently, creating an initial hurdle for new users to overcome and complicating the reproducibility of results. Aiming to reduce the bar for the extensive use of ML-PES, we introduce ${\it Asparagus}$, a software package encompassing the different parts into one coherent implementation that allows an autonomous, user-guided construction of ML-PES models. ${\it Asparagus}$ combines capabilities of initial data sampling with interfaces to ${\it ab initio}$ calculation programs, ML model training, as well as model evaluation and its application within other codes such as ASE or CHARMM. The functionalities of the code are illustrated in different examples, including the dynamics of small molecules, the representation of reactive potentials in organometallic compounds, and atom diffusion on periodic surface structures. The modular framework of ${\it Asparagus}$ is designed to allow simple implementations of further ML-related methods and models to provide constant user-friendly access to state-of-the-art ML techniques.

Biological cells exhibit a hierarchical spatial organization, where various compartments harbor condensates that form by phase separation. Cells can control the emergence of these condensates by affecting compartment size, the amount of the involved molecules, and their physical interactions. While physical interactions directly affect compartment binding and phase separation, they can also cause oligomerization, which has been suggested as a control mechanism. Analyzing an equilibrium model, we illustrate that oligomerization amplifies compartment binding and phase separation, which reinforce each other. This nonlinear interplay can also induce multistability, which provides additional potential for control. Our work forms the basis for deriving thermodynamically consistent kinetic models to understand how biological cells can regulate phase separation in their compartments.

Nanophotonics, which deals with the study of light-matter interaction at scales smaller than the wavelength of radiation, has widespread applications from plasmonic waveguiding, topological photonic crystals, super-lensing, solar absorbers, and infrared imaging. The physical phenomena governing these effects can be captured by using a macroscopic homogenized quantity called the refractive index. However, the lattice-level description of optical waves in a crystalline material using a quantum theory has long been unresolved. Inspired by the dynamics of electron waves and their corresponding band structure, here, we put forth a pico-optical band theory of solids which reveals waves hidden deep within a crystal lattice. We show that these hidden waves arise from optical pico-indices, a family of quantum functions obeying crystal symmetries, and cannot be described by the conventional concept of refractive index. We present for the first time - the hidden waves and pico-optical band structure of 14 distinct materials. We choose Si, Ge, InAs, GaAs, CdTe, and others from Group IV, III-V, and II-VI due to their technological relevance but our framework is readily applicable to a wide range of emerging 2D and 3D materials. In stark contrast to the macroscopic refractive index of these materials used widely today, this picophotonic framework shows that hidden waves exist throughout the crystal lattice and have unique pico-polarization texture and crowding. We also present an open-source software package, Purdue-PicoMax, for the research community to discover hidden waves in new materials like hBN, graphene, and Moire materials. Our work establishes a foundational crystallographic feature to discover novel pico-optical waves in light-matter interaction.

The Circular Electron-Positron Collider (CEPC) can also work as a powerful and excellent synchrotron light source, which can generate high-quality synchrotron radiation. This synchrotron radiation has potential advantages in the medical field, with a broad spectrum, with energies ranging from visible light to x-rays used in conventional radiotherapy, up to several MeV. FLASH radiotherapy is one of the most advanced radiotherapy modalities. It is a radiotherapy method that uses ultra-high dose rate irradiation to achieve the treatment dose in an instant; the ultra-high dose rate used is generally greater than 40 Gy/s, and this type of radiotherapy can protect normal tissues well. In this paper, the treatment effect of CEPC synchrotron radiation for FLASH radiotherapy was evaluated by simulation. First, Geant4 simulation was used to build a synchrotron radiation radiotherapy beamline station, and then the dose rate that CEPC can produce was calculated. Then, a physicochemical model of radiotherapy response kinetics was established, and a large number of radiotherapy experimental data were comprehensively used to fit and determine the functional relationship between the treatment effect, dose rate and dose. Finally, the macroscopic treatment effect of FLASH radiotherapy was predicted using CEPC synchrotron radiation light through the dose rate and the above-mentioned functional relationship. The results show that CEPC synchrotron radiation beam is one of the best beams for FLASH radiotherapy.

We design and fabricate an 8-channel thin film lithium niobate (TFLN) arrayed-waveguide grating (AWG) and demonstrate the electro-optical tunability of the device. The monolithically integrated microelectrodes are designed for waveguides phase modulation and wavelength tunning. Experiments show that the fabricated electro-optically controlled TFLN AWG has a channel spacing of 200 GHz and a wavelength tuning efficiency of 10 pm/V.

In radiation oncology, inter-fractional dosimetry is often performed with luminescent dosimeters to verify the accurate delivery of a plan and ensure patient safety. Optically stimulated luminescent detectors (OSLDs) are the most commonly used detector type which offers good dose linearity and accuracy in the megavoltage energy range. Freiberg Instruments offer a dosimetry system under the brand name myOLSchip which consists of a BeO OSL dosimeter, reader, and eraser. A Varian Truebeam was used to characterize the detectors and calibrate their response in order to perform in-situ dosimetry during treatment. The OSLDs were tested with both photon and electron beams from 6-15 MV and 6-20 MV respectively. The dose signal to dose conversion in this investigation follows the recommendations of TG-191 in developing a dose response curve and creating a batch calibration factor for each dosimeter. The repeatability of this system is also investigated following successive erasing and re-irradiation cycles. The results of this data have been compared to the stated accuracy and precision of the BeO detectors by the manufacturer and shown to have good dose linearity and repeatability across multiple exposures and erasure cycles.

Large language modules (LLMs) have great potential for auto-grading student written responses to physics problems due to their capacity to process and generate natural language. In this explorative study, we use a prompt engineering technique, which we name "scaffolded chain of thought (COT)", to instruct GPT-3.5 to grade student written responses to a physics conceptual question. Compared to common COT prompting, scaffolded COT prompts GPT-3.5 to explicitly compare student responses to a detailed, well-explained rubric before generating the grading outcome. We show that when compared to human raters, the grading accuracy of GPT-3.5 using scaffolded COT is 20% - 30% higher than conventional COT. The level of agreement between AI and human raters can reach 70% - 80%, comparable to the level between two human raters. This shows promise that an LLM-based AI grader can achieve human-level grading accuracy on a physics conceptual problem using prompt engineering techniques alone.

Accurate assessment of myocardial tissue stiffness is pivotal for the diagnosis and prognosis of heart diseases. Left ventricular diastolic stiffness ($\beta$) obtained from the end-diastolic pressure-volume relationship (EDPVR) has conventionally been utilized as a representative metric of myocardial stiffness. The EDPVR can be employed to estimate the intrinsic stiffness of myocardial tissues through image-based in-silico inverse optimization. However, whether $\beta$, as an organ-level metric, accurately represents the tissue-level myocardial tissue stiffness in healthy and diseased myocardium remains elusive. We developed a modeling-based approach utilizing a two-parameter material model for the myocardium (denoted by $a_f$ and $b_f$) in image-based in-silico biventricular heart models to generate EDPVRs for different material parameters. Our results indicated a variable relationship between $\beta$ and the material parameters depending on the range of the parameters. Interestingly, $\beta$ showed a very low sensitivity to $a_f$, once averaged across several LV geometries, and even a negative correlation with $a_f$ for small values of $a_f$. These findings call for a critical assessment of the reliability and confoundedness of EDPVR-derived metrics to represent tissue-level myocardial stiffness. Our results also underscore the necessity to explore image-based in-silico frameworks, promising to provide a high-fidelity and potentially non-invasive assessment of myocardial stiffness.

The current paper examines the possibility of replacing conventional synchronous single-attempt exam with more flexible and accessible multi-attempt asynchronous assessments in introductory-level physics by using large isomorphic problem banks. We compared student's performance on both numeric and conceptual problems administered on a multi-attempt, asynchronous quiz to their performance on isomorphic problems administered on a subsequent single-attempt, synchronous exam. We computed the phi coefficient and the McNemar's test statistic for the correlation matrix between paired problems on both assessments as a function of the number of attempts considered on the quiz. We found that for the conceptual problems, a multi-attempt quiz with five allowed attempts could potentially replace similar problems on a single-attempt exam, while there was a much weaker association for the numerical questions beyond two quiz attempts.

We present a framework for computing the shock Hugoniot using on-the-fly machine learned force field (MLFF) molecular dynamics simulations. In particular, we employ an MLFF model based on the kernel method and Bayesian linear regression to compute the electronic free energy, atomic forces, and pressure; in conjunction with a linear regression model between the electronic internal and free energies to compute the internal energy, with all training data generated from Kohn-Sham density functional theory (DFT). We verify the accuracy of the formalism by comparing the Hugoniot for carbon with recent Kohn-Sham DFT results in the literature. In so doing, we demonstrate that Kohn-Sham calculations for the Hugoniot can be accelerated by up to two orders of magnitude, while retaining ab initio accuracy. We apply this framework to calculate the Hugoniots of 14 materials in the FPEOS database, comprising 9 single elements and 5 compounds, between temperatures of 10 kK and 2 MK. We find good agreement with first principles results in the literature while providing tighter error bars. In addition, we confirm that the inter-element interaction in compounds decreases with temperature.

The neutron electric dipole moment (EDM) is a sensitive probe for currently undiscovered sources of charge-parity symmetry violation. As part of the \uline{T}RIUMF \uline{U}ltra\uline{c}old \uline{A}dvanced \uline{N}eutron (TUCAN) collaboration, we are developing spin analyzers for ultracold neutrons (UCNs) to be used for a next-generation experiment to measure the neutron EDM with unprecedented precision. Spin-state analysis of UCNs constitutes an essential part of the neutron EDM measurement sequence. Magnetized iron films used as spin filters of UCNs are crucial experimental components, whose performance directly influences the statistical sensitivity of the measurement. To test such iron film spin filters, we propose the use of polarized cold-neutron reflectometry, in addition to conventional UCN transmission experiments. The new method provides information on iron film samples complementary to the UCN tests and accelerates the development cycles. We developed a collaborative effort to produce iron film spin filters and test them with cold and ultracold neutrons available at JRR-3/MINE2 and J-PARC/MLF BL05. In this article, we review the methods of neutron EDM measurements, discuss the complementarity of this new approach to test UCN spin filters, provide an overview of our related activities, and present the first results of polarized cold-neutron reflectometry recently conducted at the MINE2 beamline.

Recovering pressure fields from image velocimetry measurements has two general strategies: i) directly integrating the pressure gradients from the momentum equation and ii) solving or enforcing the pressure Poisson equation (divergence of the pressure gradients). In this work, we analyze the error propagation of the former strategy and provide some practical insights. For example, we establish the error scaling laws for the Pressure Gradient Integration (PGI) and the Pressure Poisson Equation (PPE). We explain why applying the Helmholtz-Hodge Decomposition (HHD) could significantly reduce the error propagation for the PGI. We also propose to use a novel HHD-based pressure field reconstruction strategy that offers the following advantages: i) effective processing of noisy scattered or structured image velocimetry data on a complex domain and ii) using Radial Basis Functions (RBFs) with curl/divergence-free kernels to provide divergence-free correction to the velocity fields for incompressible flows and curl-free correction for pressure gradients. Complete elimination of divergence-free bias in measured pressure gradient and curl-free bias in the measured velocity field results in superior accuracy. Synthetic velocimetry data based on exact solutions and high-fidelity simulations are used to validate the analysis as well as demonstrate the flexibility and effectiveness of the RBF-HHD solver.

Laser synchronization is a technique that locks the wavelength of a free-running laser to that of the reference laser, thereby enabling synchronous changes in the wavelengths of the two lasers. This technique is of crucial importance in both scientific and industrial applications. Conventional synchronization systems, whether digital or analog, have intrinsic limitations in terms of accuracy or bandwidth. The hybrid "digital + analog" system can address this shortcoming. However, all above systems face the challenge of achieving an both high locking accuracy and low structural complexity simultaneously. This paper presents a hybrid "digital + analog" laser synchronization system with low-complexity and high-performance. In the digital part, we proposed a electric intensity locking method based on a band-pass filter, which realizes the fluctuation of frequency offset between a single frequency laser (SFL) and a mode-locked laser (MLL) less than 350 kHz in 24 hours. Following the incorporation of the analog control component, frequency fluctuation is less than 2.5 Hz in 24 hours. By synchronizing two SFLs to a repetition-frequency locked MLL, we achieve indirect synchronization between SFLs with a frequency offset of 10.6 GHz and fluctuation less than 5 Hz in 24 hours, demonstrating robust long- and short-term stability. Since the MLL is employed as a reference, the system can be utilized for cross-band indirect synchronization of multiple lasers. Based on the synchronization system, we propose a photonic-assisted microwave frequency identification scheme, which has detection error of less than 0.6 MHz. The high performance of the synchronization system enables the proposed frequency identification scheme to achieve high measurement accuracy and a theoretically large frequency range.

This paper investigates the relationship between extensional and shear viscosity of low-viscosity power-law fluids. We showed the first experimental evidence of the conditions satisfying the same power exponents for extensional and shear viscosity, as indicated by the Carreau model. The extensional and shear viscosity are respectively measured by capillary breakup extensional rheometry dripping-onto-substrate (CaBER-DoS) and by a shear rheometer for various Ohnesorge number Oh. The viscosity ranges measured were about O(10^0) to O(10^4) mPas for shear viscosity and O(10^1) to O(10^3) mPas for extensional viscosity. Our experimental results show that, at least for the range of Oh > 1, the power-law expression for the liquid filament radius, extensional viscosity, and shear viscosity holds, even for low-viscosity fluids.

The quest to understand the nature of dark matter and dark energy motivates a deep exploration into axion physics, particularly within the framework of string theory. Axions, originally proposed to solve the strong CP problem, emerge as compelling candidates for both dark matter and dark energy components of the universe. String theory, offering a unified perspective on fundamental forces, predicts a rich spectrum of axion-like particles (ALPs) arising from its compactification schemes. This paper provides a comprehensive review of axion physics within string theory, detailing their theoretical foundations, emergence from compactification processes, and roles in cosmological models. Key aspects covered include the Peccei-Quinn mechanism, the structure of ALPs, their moduli stabilization, and implications for observational signatures in dark matter, dark energy, and cosmological inflation scenarios. Insights from ongoing experimental efforts and future directions in axion cosmology are also discussed

Brillouin optical correlation-domain reflectometry (BOCDR) is unique in its ability to measure distributed strain and temperature changes along a fiber under test (FUT) from a single end, offering random access and relatively high spatial resolution, making it promising for infrastructure monitoring. BOCDR achieves spatial resolution through frequency modulation of the laser output, and this modulation frequency determines the measurement position, necessitating accurate association of modulation frequencies with positions on the FUT. However, a practical method to precisely correlate modulation frequency values with FUT positions has not yet been proposed. This study introduces a method leveraging the change in Rayleigh noise spectrum with modulation frequency to accurately associate these frequencies with positions on the FUT. The effectiveness of this method is proved through distributed strain measurement.

In the last decade, remarkable advances in integrated photonic technologies have enabled table-top experiments and instrumentation to be scaled down to compact chips with significant reduction in size, weight, power consumption, and cost. Here, we demonstrate an integrated continuously tunable laser in a heterogeneous gallium arsenide-on-silicon nitride (GaAs-on-SiN) platform that emits in the far-red radiation spectrum near 780 nm, with 20 nm tuning range, <6 kHz intrinsic linewidth, and a >40 dB side-mode suppression ratio. The GaAs optical gain regions are heterogeneously integrated with low-loss SiN waveguides. The narrow linewidth lasing is achieved with an extended cavity consisting of a resonator-based Vernier mirror and a phase shifter. Utilizing synchronous tuning of the integrated heaters, we show mode-hop-free wavelength tuning over a range larger than 100 GHz (200 pm). To demonstrate the potential of the device, we investigate two illustrative applications: (i) the linear characterization of a silicon nitride microresonator designed for entangled-photon pair generation, and (ii) the absorption spectroscopy and locking to the D1 and D2 transition lines of 87-Rb. The performance of the proposed integrated laser holds promise for a broader spectrum of both classical and quantum applications in the visible range, encompassing communication, control, sensing, and computing.

A low energy particle confined by a horizontal reflective surface and gravity settles in gravitationally bound quantum states. These gravitational quantum states (GQS) were so far only observed with neutrons. However, the existence of GQS is predicted also for atoms. The GRASIAN collaboration pursues the first observation of GQS of atoms, using a cryogenic hydrogen beam. This endeavor is motivated by the higher densities, which can be expected from hydrogen compared to neutrons, the easier access, the fact, that GQS were never observed with atoms and the accessibility to hypothetical short range interactions. In addition to enabling gravitational quantum spectroscopy, such a cryogenic hydrogen beam with very low vertical velocity components - a few cm s$^{-1}$, can be used for precision optical and microwave spectroscopy. In this article, we report on our methods developed to reduce background and to detect atoms with a low horizontal velocity, which are needed for such an experiment. Our recent measurement results on the collimation of the hydrogen beam to 2 mm, the reduction of background and improvement of signal-to-noise and finally our first detection of atoms with velocities < 72 m s$^{-1}$ are presented. Furthermore, we show calculations, estimating the feasibility of the planned experiment and simulations which confirm that we can select vertical velocity components in the order of cm s$^{-1}$.

The attachment-line boundary layer is critical in hypersonic flows because of its significant impact on heat transfer and aerodynamic performance. In this study, high-fidelity numerical simulations are conducted to analyze the subcritical roughness-induced laminar-turbulent transition at the leading-edge attachment-line boundary layer of a blunt swept body under hypersonic conditions. This simulation represents a significant advancement by successfully reproducing the complete leading-edge contamination process induced by surface roughness elements in a realistic configuration, thereby providing previously unattainable insights. Two roughness elements of different heights are examined. For the lower-height roughness element, additional unsteady perturbations are required to trigger a transition in the wake, suggesting that the flow field around the roughness element acts as a disturbance amplifier for upstream perturbations. Conversely, a higher roughness element can independently induce the transition. A low-frequency absolute instability is detected behind the roughness, leading to the formation of streaks. The secondary instabilities of these streaks are identified as the direct cause of the final transition.

In this study,we investigate the characteristics of three-dimensional turbulent boundary layers influenced by transverse flow and pressure gradients. Our findings reveal that even without assuming an infinite sweep, a fully developed turbulent boundary layer over the present swept blunt body maintains spanwise homogeneity, consistent with infinite sweep assumptions.We critically examine the law-of-the and temperature-velocity relationships, typically applied two-dimensional turbulent boundary layers, in three-dimensional contexts. Results show that with transverse velocity and pressure gradient, streamwise velocity adheres to classical velocity transformation relationships and the predictive accuracy of classical temperaturevelocity relationships diminishes because of pressure gradient. We show that near-wall streak structures persist and correspond with energetic structures in the outer region, though three-dimensional effects redistribute energy to align more with the external flow direction. Analysis of shear Reynolds stress and mean flow shear directions reveals in near-wall regions with low transverse flow velocity, but significant deviations at higher transverse velocities. Introduction of transverse pressure gradients together with the transverse velocities alter the velocity profile and mean flow shear directions, with shear Reynolds stress experiencing similar changes but with a lag increasing with transverse. Consistent directional alignment in outer regions suggests a partitioned relationship between shear Reynolds stress and mean flow shear: nonlinear in the inner region and approximately linear in the outer region.

Optically read out gaseous detectors are used in track reconstruction and imaging applications requiring high granularity images. Among resolution-determining factors, the amplification stage plays a crucial role and optimisations of detector geometry are pursued to maximise spatial resolution. To compare MicroPattern Gaseous Detector (MPGD) technologies, focused low-energy X-ray beams at the SOLEIL synchrotron facility were used to record and extract point spread function widths with Micromegas and GEM detectors. Point spread function width of $\approx$108\,\microns for Micromegas and $\approx$127\,\microns for GEM foils were extracted. The scanning of the beam with different intensities, energies and across the detector active region can be used to quantify resolution-limiting factors and improve imaging detectors using MPGD amplification stages.

Spatial variation in the intensity of magnetospheric and ionospheric fluctuation during solar storms creates ground-induced currents, of importance in both infrastructure engineering and geophysical science. This activity is currently measured using a network of ground-based magnetometers, typically consisting of extensive installations at established observatory sites. We show that this network can be enhanced by the addition of remote quantum magnetometers which combine high sensitivity with intrinsic calibration. These nodes utilize scalable hardware and run independently of wired communication and power networks. We demonstrate that optically pumped magnetometers, utilizing mass-produced and miniaturized components, offer a single scalable sensor with the sensitivity and stability required for space weather observation. We describe the development and deployment of an off-grid magnetic sensing node, powered by a solar panel, present observed data from periods of low and high geomagnetic activity, and compare it to existing geomagnetic observatories.

Nonequilibrium flows have been frequently encountered in various aerospace engineering applications. To understand nonequilibrium physics, multiscale effects, and the dynamics in these applications, an effective and reliable multiscale scheme for all flow regimes is required. Following the direct modeling methodology, the adaptive unified gas-kinetic scheme employs discrete velocity space (DVS) to accurately capture the non-equilibrium physics, recovering the original unified gas-kinetic scheme (UGKS), and adaptively employs continuous distribution functions based on the Chapman-Enskog expansion to achieve better efficiency. Different regions are dynamically coupled at the cell interface through the fluxes from the discrete and continuous gas distribution functions, thereby avoiding any buffer zone between them. In the current study, an implicit adaptive unified gas-kinetic scheme (IAUGKS) is constructed to further enhance the efficiency of steady-state solutions. The current scheme employs implicit macroscopic governing equations and couples them with implicit microscopic governing equations within the non-equilibrium region, resulting in high convergence efficiency in all flow regimes. A series of numerical tests were conducted for high Mach number flows around diverse geometries such as a cylinder, a sphere, an X-38-like vehicle, and a space station. The current scheme can capture the non-equilibrium physics and provide accurate predictions of surface quantities. In comparison with the original UGKS, the velocity space adaptation, unstructured DVS, and implicit iteration significantly improve the efficiency by one or two orders of magnitude. Given its exceptional efficiency and accuracy, the IAUGKS serves as an effective tool for nonequilibrium flow simulations.

Plasma-based accelerators are a promising approach for reducing the size and cost of future particle accelerators, making them a viable technology for constructing and upgrading X-ray free-electron lasers (FELs). Adding an energy booster stage to the linear accelerator of an operational X-ray FEL is recognised as a realistic near-term application of plasma accelerators, with a significant impact on the scientific reach of these facilities. Here, we discuss potential use cases of such a plasma-based energy booster and apply particle-in-cell simulations to estimate its ability to enhance the performance of existing X-ray FEL facilities.

Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather such as the ECMWF ERA5. These datasets have been made freely available to the wider research community, including the commercial sector, which has been a major factor in the rapid rise of ML forecast systems and the levels of accuracy they have achieved. However, historical reanalyses used for training and real-time analyses used for initial conditions are produced by data assimilation, an optimal blending of observations with a physics-based forecast model. As such, many ML forecast systems have an implicit and unquantified dependence on the physics-based models they seek to challenge. Here we propose a new approach, training a neural network to predict future weather purely from historical observations with no dependence on reanalyses. We use raw observations to initialise a model of the atmosphere (in observation space) learned directly from the observations themselves. Forecasts of crucial weather parameters (such as surface temperature and wind) are obtained by predicting weather parameter observations (e.g. SYNOP surface data) at future times and arbitrary locations. We present preliminary results on forecasting observations 12-hours into the future. These already demonstrate successful learning of time evolutions of the physical processes captured in real observations. We argue that this new approach, by staying purely in observation space, avoids many of the challenges of traditional data assimilation, can exploit a wider range of observations and is readily expanded to simultaneous forecasting of the full Earth system (atmosphere, land, ocean and composition).

Quasi-phase-matching (QPM) is a widely adopted technique for mitigating stringent momentum conservation in nonlinear optical processes such as second-harmonic generation (SHG). It effectively compensates for the phase velocity mismatch between optical harmonics by introducing a periodic spatial modulation to the nonlinear optical medium. Such a mechanism has been further generalized to the spatiotemporal domain, where a non-stationary spatial QPM can induce a frequency shift of the generated light. Here we demonstrate how a spatiotemporal QPM grating, consisting in a concurrent spatial and temporal modulation of the nonlinear response, naturally emerges through all-optical poling in silicon nitride microresonators. Mediated by the coherent photogalvanic effect, a traveling space-charge grating is self-organized, affecting momentum and energy conservation, resulting in a quasi-phase-matched and Doppler-shifted second harmonic. Our observation of the photoinduced spatiotemporal QPM expands the scope of phase matching conditions in nonlinear photonics.

Spacetime metamaterials (ST-MMs) are opening new regimes of light-matter interactions based on the breaking of temporal and spatial symmetries, as well as intriguing concepts associated with synthetic motion. In this work, we investigate the continuous spatiotemporal translation symmetry of ST-MMs with uniform modulation velocity. Using Noether theorem, we demonstrate that such symmetry entails the conservation of the energy-momentum. We highlight how energy-momentum conservation imposes constraints on the range of allowed light-matter interactions within ST-MMs, as illustrated with examples of the collision of electromagnetic and modulation pulses. Furthermore, we discuss the similarities and differences between the conservation of energy-momentum and relativistic effects. We believe that our work provides a step forward in clarifying the fundamental theory underlying ST-MMs.

This study proposes a novel super-resolution (or SR) framework for generating high-resolution turbulent boundary layer (TBL) flow from low-resolution inputs. The framework combines a super-resolution generative adversarial neural network (SRGAN) with down-sampling modules (DMs), integrating the residual of the continuity equation into the loss function. DMs selectively filter out components with excessive energy dissipation in low-resolution fields prior to the super-resolution process. The framework iteratively applies the SRGAN and DM procedure to fully capture the energy cascade of multi-scale flow structures, collectively termed the SRGAN-based energy cascade framework (EC-SRGAN). Despite being trained solely on turbulent channel flow data (via "zero-shot transfer"), EC-SRGAN exhibits remarkable generalization in predicting TBL small-scale velocity fields, accurately reproducing wavenumber spectra compared to DNS results. Furthermore, a super-resolution core is trained at a specific super-resolution ratio. By leveraging this pre-trained super-resolution core, EC-SRGAN efficiently reconstructs TBL fields at multiple super-resolution ratios from various levels of low-resolution inputs, showcasing strong flexibility. By learning turbulent scale invariance, EC-SRGAN demonstrates robustness across different TBL datasets. These results underscore EC-SRGAN potential for generating and predicting wall turbulence with high flexibility, offering promising applications in addressing diverse TBL-related challenges.

Akin to the traditional quasi-classical trajectory method for investigating the dynamics on a single adiabatic potential energy surface for an elementary chemical reaction, we carry out the dynamics on a 2-state ab initio potential energy surface including nonadiabatic coupling terms as friction terms for D+ + H2 collisions. It is shown that the resulting dynamics correctly accounts for nonreactive charge transfer, reactive non charge transfer and reactive charge transfer processes. In addition, it leads to the formation of triatomic DH2+ species as well.

We propose the Artificial Intelligence Velocimetry-Thermometry (AIVT) method to infer hidden temperature fields from experimental turbulent velocity data. This physics-informed machine learning method enables us to infer continuous temperature fields using only sparse velocity data, hence eliminating the need for direct temperature measurements. Specifically, AIVT is based on physics-informed Kolmogorov-Arnold Networks (not neural networks) and is trained by optimizing a combined loss function that minimizes the residuals of the velocity data, boundary conditions, and the governing equations. We apply AIVT to a unique set of experimental volumetric and simultaneous temperature and velocity data of Rayleigh-B\'enard convection (RBC) that we acquired by combining Particle Image Thermometry and Lagrangian Particle Tracking. This allows us to compare AIVT predictions and measurements directly. We demonstrate that we can reconstruct and infer continuous and instantaneous velocity and temperature fields from sparse experimental data at a fidelity comparable to direct numerical simulations (DNS) of turbulence. This, in turn, enables us to compute important quantities for quantifying turbulence, such as fluctuations, viscous and thermal dissipation, and QR distribution. This paradigm shift in processing experimental data using AIVT to infer turbulent fields at DNS-level fidelity is a promising avenue in breaking the current deadlock of quantitative understanding of turbulence at high Reynolds numbers, where DNS is computationally infeasible.

For the first time, we estimate the in-medium mass shift of the two-flavored heavy mesons $B_c, B_c^*, B_s, B_s^*, D_s$ and $D_s^*$ in symmetric nuclear matter. The estimates are made by evaluating the lowest order one-loop self-energies. The enhanced excitations of intermediate state heavy-light quark mesons in symmetric nuclear matter are the origin of their negative mass shift. Our results show that the magnitude of the mass shift for the $B_c$ meson ($\bar{b} c$ or $b \bar{c}$) is larger than those of the $\eta_c (\bar{c} c)$ and $\eta_b (\bar{b} b)$, different from a naive expectation that it would be in-between of them. While, that of the $B_c^*$ shows the in-between of the $J/\psi$ and $\Upsilon$. We observe that the lighter vector meson excitation in each meson self-energy gives a dominant contribution for the corresponding meson mass shift, $B_c, B_s,$ and $D_s$.

This study presents a theoretical model for a self-replicating mechanical system inspired by biological processes within living cells and supported by computer simulations. The model decomposes self-replication into core components, each of which is executed by a single machine constructed from a set of basic block types. Key functionalities such as sorting, copying, and building, are demonstrated. The model provides valuable insights into the constraints of self-replicating systems. The discussion also addresses the spatial and timing behavior of the system, as well as its efficiency and complexity. This work provides a foundational framework for future studies on self-replicating mechanisms and their information-processing applications.

Magnetic fields are prevalent on almost all astronomical scales, but their importance in different systems and over cosmic time is yet to be understood. Our current knowledge on the evolution of magnetic fields is limited by scarce observations in the distant Universe, where galaxies have recently been found to be more evolved than most of our model predictions. In this study, we conduct rest-frame 131 $\mu$m full-polarisation ALMA observations of dust emission in a strongly lensed dusty star-forming galaxy, SPT0346-52, at z=5.6, when the Universe was only 1 Gyr old. Dust grains can become aligned with local magnetic fields, resulting in the emission of linearly polarised thermal infrared radiation. Our observations have revealed a median polarisation level of 0.9$\pm$0.2 per cent with a variation of $\pm$0.4 per cent across the regions with polarisation detection, similar to that of local starburst galaxies. The polarised dust emission is patchy. It mostly overlaps with the [C II] emission at a velocity of about -150 km/s, and extends over 3 kiloparsecs with a bimodal distribution in position angles. Our analysis indicates that the kpc-scale polarised dust is most likely aligned by the large-scale magnetic fields associated with a galaxy merger. If the ordered fields are confirmed to be coherent, such early detection of large-scale magnetic fields favours an efficient formation of magnetic fields in primordial galaxies, which highlights the importance of magnetic fields in mediating galaxy evolution over long cosmic timescales. Future surveys towards a wider galaxy population are necessary to test the ubiquitousness of large-scale magnetic fields in early galaxies.

Metasurface Energy Harvesters (MEHs) have emerged as a prominent enabler of highly efficient Radio Frequency (RF) energy harvesters. This survey delves into the fundamentals of the MEH technology, providing a comprehensive overview of their working principle, unit cell designs and prototypes over various frequency bands, as well as state-of-the art modes of operation. Inspired by the recent academic and industrial interest on Reconfigurable Intelligent Surfaces (RISs)for the upcoming sixth-Generation (6G) of wireless networks, we study the interplay between this technology and MEHs aiming for energy sustainable RISs power by metasurface-based RF energy harvesting. We present a novel hybrid unit cell design capable of simultaneous energy harvesting and 1-bit tunable reflection whose dual-functional response is validated via full-wave simulations. Then, we conduct a comparative collection of real-world measurements for ambient RF power levels and power consumption budgets of reflective RISs to unveil the potential for a self-sustainable RIS via ambient RF energy harvesting. The paper is concluded with an elaborative discussion on open design challenges and future research directions for MEHs and energy sustainable hybrid RISs.

Null results for WIMP dark matter have led to increased interest in exploring other dark matter candidates, such as Axions and Axion-Like Particles (ALPs), which also helps in answering the strong CP problem. This experiment achieved a sub-100 DRU (differential-rate-unit, expressed in counts/keV/kg/day) background in the MeV region of interest by employing a combination of active and passive veto techniques. Such a low background facilitates the search for ALPs with axion-photon coupling $g_{a\gamma \gamma} > 10^{-6}$ and axion-electron coupling $10^{-8}< g_{aee} < 10^{-4}$ in the 1 keV to 10 MeV mass range. This indicates that the experiment has the capability to constrain the unexplored cosmological triangle in the ALP-photon parameter space for ALPs in the MeV mass range.

The idea of post-measurement coincidence pairing simplifies substantially long-distance, repeater-like quantum key distribution (QKD) by eliminating the need for tracking the differential phase of the users' lasers. However, optical frequency tracking remains necessary and can become a severe burden in future deployment of multi-node quantum networks. Here, we resolve this problem by referencing each user's laser to an absolute frequency standard and demonstrate a practical post-measurement pairing QKD with excellent long-term stability. We confirm the setup's repeater-like behavior and achieve a finite-size secure key rate (SKR) of 15.94 bit/s over 504 km fiber, which overcomes the absolute repeaterless bound by 1.28 times. Over a fiber length 100 km, the setup delivers an impressive SKR of 285.68 kbit/s. Our work paves the way towards an efficient muti-user quantum network with the local frequency standard.

The diverse isotopic anomalies of meteorites demonstrate that the protoplanetary disk was composed of components from different stellar sources, which mixed in the disk and formed the planetary bodies. However, the origin of the accretion materials of different planetary bodies and the cosmochemical relationship between these bodies remain ambiguous. The noncarbonaceous (NC) planetary bodies originate from the inner solar system and have isotopic compositions distinct from those of the carbonaceous (CC) bodies. We combined Ca, Ti, Cr, Fe, Ni, Mo, and Ru isotopic anomalies to develop a quantitative two-endmember mixing model of the NC bodies. Correlations of the isotopic anomalies of different elements with different cosmochemical behaviors originate from the mixing of two common endmembers. Using this mixing model, we calculated the isotopic anomalies of NC bodies for all the considered isotopes, including the isotopic anomalies that are difficult to measure or have been altered by spallation processes. The mixing proportion between the two endmembers in each NC body has been calculated as a cosmochemical parameter, which represents the compositional relationship of the accretion materials between the NC bodies. Using the calculated mixing proportions, the feeding zones of the NC bodies could be estimated. The estimated feeding zones of NC bodies indicate a large population of interlopers in the main asteroid belt and an indigenous origin of Vesta. The feeding zones estimated in different planet formation scenarios indicate that the orbits of Jupiter and Saturn during formation of terrestrial planets were likely to be more circular than their current ones.

In this study, we present a comprehensive analysis of the motion of a tagged monomer within a Gaussian semiflexible polymer model. We carefully derived the generalized Langevin Equation (GLE) that governs the motion of a tagged central monomer. This derivation involves integrating out all the other degrees of freedom within the polymer chain, thereby yielding an effective description of the viscoelastic motion of the tagged monomer. A critical component of our analysis is the memory kernel that appears in the GLE. By examining this kernel, we characterized the impact of bending rigidity on the non-Markovian diffusion dynamics of the tagged monomer. Furthermore, we calculated the mean-squared displacement of the tagged monomer using the derived GLE. Our results not only show remarkable agreement with previously known results in certain limiting cases but also provide dynamic features over the entire timescale.

This study presents a theoretical investigation of the physical mechanisms governing small signal capacitance in ferroelectrics, focusing on Hafnium Zirconium Oxide. Utilizing a time-dependent Ginzburg Landau formalism-based 2D multi-grain phase-field simulation framework, we simulate the capacitance of metal-ferroelectric-insulator-metal (MFIM) capacitors. Our simulation methodology closely mirrors the experimental procedures for measuring ferroelectric small signal capacitance, and the outcomes replicate the characteristic butterfly capacitance-voltage behavior. We delve into the components of the ferroelectric capacitance associated with the dielectric response and polarization switching, discussing the primary physical mechanisms - domain bulk response and domain wall response - contributing to the butterfly characteristics. We explore their interplay and relative contributions to the capacitance and correlate them to the polarization domain characteristics. Additionally, we investigate the impact of increasing domain density with ferroelectric thickness scaling, demonstrating an enhancement in the polarization capacitance component (in addition to the dielectric component). We further analyze the relative contributions of the domain bulk and domain wall responses across different ferroelectric thicknesses. Lastly, we establish the relation of polarization capacitance components to the capacitive memory window (for memory applications) and reveal a non-monotonic dependence of the maximum memory window on HZO thickness.

Electrical and thermal transport across material interfaces is key for 2D electronics in semiconductor technology, yet their relationship remains largely unknown. We report a theoretical proposal to separate electronic and phononic contributions to thermal conductance at 2D interfaces, which is validated by non-equilibrium Green's function calculations and molecular dynamics simulations for graphene-gold contacts. Our results reveal that while metal-graphene interfaces are transparent for both electrons and phonons, non-covalent graphene interfaces block electronic tunneling beyond two layers but not phonon transport. This suggests that the Wiedemann-Franz law can be experimentally tested by measuring transport across interfaces with varying graphene layers.

Identical systems, or entities, are indistinguishable in quantum mechanics (QM), and the symmetrization postulate rules the possible statistical distributions of a large number of identical quantum entities. However, a thorough analysis on the historical development of QM attributes the origin of quantum statistics, in particular, Bose-Einstein statistics, to a lack of statistical independence of the micro-states of identical quantum entities. We have recently identified Bose-Einstein statistics in the combination of words in large texts, as a consequence of the entanglement created by the meaning carried by words when they combine in human language. Relying on this investigation, we put forward the hypothesis that entanglement, hence the lack of statistical independence, is due to a mechanism of contextual updating, which provides deeper reasons for the appearance of Bose-Einstein statistics in human language. However, this investigation also contributes to a better understanding of the origin of quantum mechanical statistics in physics. Finally, we provide new insights into the intrinsically random behaviour of microscopic entities that is generally assumed within classical statistical mechanics.

Reservoir computing has been shown to be a useful framework for predicting critical transitions of a dynamical system if the bifurcation parameter is also provided as an input. Its utility is significant because in real-world scenarios, the exact model equations are unknown. This Letter shows how the theory of dynamical system provides the underlying mechanism behind the prediction. Using numerical methods, by considering dynamical systems which show Hopf bifurcation, we demonstrate that the map produced by the reservoir after a successful training undergoes a Neimark-Sacker bifurcation such that the critical point of the map is in immediate proximity to that of the original dynamical system. In addition, we have compared and analyzed different structures in the phase space. Our findings provide insight into the functioning of machine learning algorithms for predicting critical transitions.

We present results from a pilot study, using a laser-produced plasma, to identify new lines in the 350 to 1000 nm spectral region for the r-process element gold (Au), of relevance to studies of neutron star mergers. This was achieved via optical-IR spectroscopy of a laser-produced Au plasma, with an Au target of high purity (99.95 %) and a low vacuum pressure to remove any air contamination from the experimental spectra. Our data were recorded with a spectrometer of 750 mm focal length and 1200 lines mm-1 grating, yielding a resolution of 0.04 nm. We find 54 lines not previously identified and which are not due to the impurities (principally copper (Cu) and silver (Ag)) in our Au sample. Of these 54 lines, we provisionally match 21 strong transitions to theoretical results from collisional-radiative models that include energy levels derived from atomic structure calculations up to the 6s level. Some of the remaining 33 unidentified lines in our spectra are also strong and may be due to transitions involving energy levels which are higher-lying than those in our plasma models. Nevertheless, our experiments demonstrate that laser-produced plasmas are well suited to the identification of transitions in r-process elements, with the method applicable to spectra ranging from UV to IR wavelengths.

Recent studies highlight the scientific importance and broad application prospects of two-dimensional (2D) sliding ferroelectrics, which prevalently exhibit vertical polarization with suitable stackings. It is crucial to understand the mechanisms of sliding ferroelectricity and to deterministically and efficiently switch the polarization with optimized electric fields. Here, applying our newly developed DREAM-Allegro multi-task equivariant neural network, which simultaneously predicts interatomic potentials and Born effective charges, we construct a comprehensive potential for boron nitride ($\mathrm{BN}$) bilayer. The molecular dynamics simulations reveal a remarkably high Curie temperature of up to 1500K, facilitated by robust intralayer chemical bonds and delicate interlayer van der Waals(vdW) interactions. More importantly, it is found that, compared to the out-of-plane electric field, the inclined field not only leads to deterministic switching of electric polarization, but also largely lower the critical strength of field, due to the presence of the in-plane polarization in the transition state. This strategy of an inclined field is demonstrated to be universal for other sliding ferroelectric systems with monolayer structures belonging to the symmetry group $p \bar{6} m 2$, such as transition metal dichalcogenides (TMDs).

High-throughput reaction condition (RC) screening is fundamental to chemical synthesis. However, current RC screening suffers from laborious and costly trial-and-error workflows. Traditional computer-aided synthesis planning (CASP) tools fail to find suitable RCs due to data sparsity and inadequate reaction representations. Nowadays, large language models (LLMs) are capable of tackling chemistry-related problems, such as molecule design, and chemical logic Q\&A tasks. However, LLMs have not yet achieved accurate predictions of chemical reaction conditions. Here, we present MM-RCR, a text-augmented multimodal LLM that learns a unified reaction representation from SMILES, reaction graphs, and textual corpus for chemical reaction recommendation (RCR). To train MM-RCR, we construct 1.2 million pair-wised Q\&A instruction datasets. Our experimental results demonstrate that MM-RCR achieves state-of-the-art performance on two open benchmark datasets and exhibits strong generalization capabilities on out-of-domain (OOD) and High-Throughput Experimentation (HTE) datasets. MM-RCR has the potential to accelerate high-throughput condition screening in chemical synthesis.

It is under debate whether the magnetic field in the solar atmosphere carries neutralized electric currents; particularly, whether a magnetic flux rope (MFR), which is considered the core structure of coronal mass ejections, carries neutralized electric currents. Recently Wang et al. (2023, ApJ, 943, 80) studied magnetic flux and electric current measured at the footpoints of 28 eruptive MFRs from 2010 to 2015. Because of the small sample size, no rigorous statistics has been done. Here, we include 9 more events from 2016 to 2023 and perform a series of nonparametric statistical tests at a significance level of 5\%. The tests confirm that there exist no significant differences in magnetic properties between conjugated footpoints of the same MFR, which justifies the method of identifying the MFR footpoints through coronal dimming. The tests demonstrate that there exist no significant differences between MFRs with pre-eruption dimming and those with only post-eruption dimming. However, there is a medium level of association between MFRs carrying substantial net current and those produce pre-eruption dimming, which can be understood by the Lorentz-self force of the current channel. The tests also suggest that in estimating the magnetic twist of MFRs, it is necessary to take into account the spatially inhomogeneous distribution of electric current density and magnetic field.

Mn3TeO6 (MTO) has been experimentally found to adopt a P21/n structure under high pressure, which exhibits a significantly smaller band gap compared to the atmospheric R-3 phase. In this study, we systematically investigate the magnetism, structural phase transition and electronic properties of MTO under high pressure through first-principles calculations. Both R-3 and P21/n phases of MTO are antiferromagnetic at zero temperature. The R-3 phase transforms to the P21/n phase at 7.58 GPa, accompanied by a considerable volume collapse of about 6.47%. Employing the accurate method that combines DFT+U and G0W0, the calculated band gap of R-3 phase at zero pressure is very close to the experimental values, while that of the P21/n phase is significantly overestimated. The main reason for this difference is that the experimental study incorrectly used the Kubelka-Munk plot for the indirect band gap to obtain the band gap of the P21/n phase instead of the Kubelka-Munk plot for the direct band gap. Furthermore, our study reveals that the transition from the R-3 phase to the P21/n phase is accompanied by a slight reduction in the band gap.

Uranium is considered as a very important nuclear energy material because of the huge amount of energy released. As the main products of spontaneous decay of uranium, helium is difficult to react with uranium for its chemical inertness. Therefore, bubbles will be formed inside uranium, which could greatly reduce the performance of uranium or cause the safety problems. Additionally, nuclear materials are usually operated in an environment of high-temperature and high-pressure, so it is necessary to figure out the exact state of helium inside uranium at extreme conditions. Here, we explored the structural stability of U-He system under high-pressure and high-temperature by using density functional theory calculations. Two metastable phases are found between 50 and 400 GPa: U4He with space group Fmmm and U6He with space group P-1. Both are metallic and adopt layered structures. Electron localization function calculation combined with charge density difference analysis indicate that there are covalent bonds between U and U atoms in both Fmmm-U4He and P-1-U6He. Compared with the elastic modulus of ${\alpha}$-U, the addition of helium has certain influence on the mechanical properties of uranium. Besides, first-principles molecular dynamics simulations were carried out to study the dynamical behavior of Fmmm-U4He and P-1-U6He at high-temperature. It is found that Fmmm-U4He and P-1-U6He undergo one-dimensional superionic phase transitions at 150 GPa. Our study revealed exotic structure of U-He compounds beyond the form of bubble under high-pressure and high-temperature, that might be relevant to the performance and safety issue of nuclear materials at extreme conditions.

Low-energy excitations may manifest intricate behaviors of correlated electron systems and provide essential insights into the dynamics of quantum states and phase transitions. We study a two-orbital Hubbard model featuring the so-called holon-doublon low-energy excitations in the Mott insulating narrow band in the orbital-selective Mott phase (OSMP). We employ an improved dynamical mean-field theory (DMFT) technique to calculate the spectral functions at zero temperature. We show that the holon-doublon bound state is not the sole component of the low-energy excitations. Instead, it should be a bound state composed of a Kondo-like state in the wide band and a doublon in the narrow band, named inter-band Kondo-like (IBK) bound states. Notably, as the bandwidths of the two bands approach each other, we find anomalous IBK bound-state excitations in the metallic {\em wide} band.

Hurricane Ian is the deadliest and costliest hurricane in Florida's history, with 2.5 million people ordered to evacuate. As we witness increasingly severe hurricanes in the context of climate change, mobile device location data offers an unprecedented opportunity to study hurricane evacuation behaviors. With a terabyte-level GPS dataset, we introduce a holistic hurricane evacuation behavior algorithm with a case study of Ian: we infer evacuees' departure time and categorize them into different behavioral groups, including self, voluntary, mandatory, shadow and in-zone evacuees. Results show the landfall area (Fort Myers, Lee County) had lower out-of-zone but higher overall evacuation rate, while the predicted landfall area (Tampa, Hillsborough County) had the opposite, suggesting the effects of delayed evacuation order. Out-of-zone evacuation rates would increase from shore to inland. Spatiotemporal analysis identified three evacuation waves: during formation, before landfall, and after landfall. These insights are valuable for enhancing future disaster planning and management.

Li-containing argyrodites represent a promising family of Li-ion conductors with several derived compounds exhibiting room-temperature ionic conductivity > 1 mS/cm and making them attractive as potential candidates as electrolytes in solid-state Li-ion batteries. Starting from the parent phase Li7PS6, several cation and anion substitution strategies have been attempted to increase the conductivity of Li ions. Nonetheless, a detailed understanding of the thermodynamics of native defects and doping of Li argyrodite and their effect on the ionic conductivity of Li is missing. Here, we report a comprehensive computational study of defect chemistry of the parent phase Li7PS6 in both intrinsic and extrinsic regimes, using a newly developed workflow to automate the computations of several defect formation energies in a thermodynamically consistent framework. Our findings agree with known experimental findings, rule out several unfavorable aliovalent dopants, narrowing down the potential promising candidates that can be tested experimentally. We also find that cation-anion co-doping can provide a powerful strategy to further optimize the composition of argyrodite. In particular, Si-F co-doping is predicted to be thermodynamically favorable; this could lead to the synthesis of the first F-doped Li-containing argyrodite. Finally, using DeePMD neural networks, we have mapped the ionic conductivity landscape as function of the concentration of the most promising cation and anion dopants identified from the defect calculations, and identified the most promising region in the compositional space with high Li conductivity that can be explored experimentally.

Among Universe's most consequential events are large impacts generating rapidly-evolving extreme pressures and temperatures. Crystalline and amorphous forms of (Mg, Fe)2SiO4 are abundant and widespread, within planets and in space. The behavior of these minerals is expected to deviate form thermodynamic equilibrium in many of the processes that are critical to the formation and evolution of planets, particularly shock events. To further the understanding of the behavior of the silicate under extreme conditions, we statically compressed a crystal of forsterite up to 160.5 GPa, far beyond the compound's stability field, and probed its long-range ordering with synchrotron microdiffraction. We found that forsterite retains long-range ordering up to the highest pressure reached. Forsterite III, emerging at about 58 GPa, persists in compression to 160.5 GPa and in decompression down to about 13 GPa, for a rare combined occurrence of a metastable phase of nearly 150 GPa. These observations dispute earlier reports of pressure-induced amorphization and are a unique testimony of the resilience of the crystalline state in quasi hydrostatic compression. We confirm that highly disordered forsterite can be obtained from the decompression of forsterite III as suggested from the substantial loss of long-range ordering observed at 7 GPa after further decompression. Such kinetic pathway may explain how synthetic olivine glass have been obtained in shock experiments and could be a mechanism of generation of amorphous forsterite in cosmic dust. The 120 GPa Hugoniot discontinuity finds no correspondence in our data, marking a departure from the parallelism between static "cold compression" and dynamic compression.

In this work we propose a new efficient basis for the electronic structure problem. The basis is based on the Muffin Tin Orbital (MTO) idea that the eigenstates of the Khon Sham (KS) Hamiltonian may we be expanded in terms of eigenstates of the spherically averaged KS Hamiltonian inside the so called Muffin Tin (MT) spheres and Bessel functions in the interstitial multiplied by appropriate spherical Harmonics. Here we use the fact that the solution to problem of finding the ground state electron density is most often done through an iterative process, where generically on the order of over 20 iterations are taken till the ground state electron density and energy converges to the lowest values allowed by the correlation and exchange functional for the fixed form of the external potential. We use eigenstate information from the previous convergence iteration to choose the energies of the eigenstates of the spherically averaged KS Hamiltonian. Furthermore within the Atomic Sphere Approximation (ASA) the energies of the Bessel functions do not matter as they are cancelled out. This is an efficient method aimed at studying the electronic structure of materials with large unit cells especially if they are of close packed form where ASA is particularly accurate.

Solid-water interfaces are crucial to many physical and chemical processes and are extensively studied using surface-specific sum-frequency generation (SFG) spectroscopy. To establish clear correlations between specific spectral signatures and distinct interfacial water structures, theoretical calculations using molecular dynamics (MD) simulations are required. These MD simulations typically need relatively long trajectories (a few nanoseconds) to achieve reliable SFG response function calculations via the dipole-polarizability time correlation function. However, the requirement for long trajectories limits the use of computationally expensive techniques such as ab initio MD (AIMD) simulations, particularly for complex solid-water interfaces. In this work, we present a pathway for calculating vibrational spectra (IR, Raman, SFG) of solid-water interfaces using machine learning (ML)-accelerated methods. We employ both the dipole moment-polarizability correlation function and the surface-specific velocity-velocity correlation function approaches to calculate SFG spectra. Our results demonstrate the successful acceleration of AIMD simulations and the calculation of SFG spectra using ML methods. This advancement provides an opportunity to calculate SFG spectra for the complicated solid-water systems more rapidly and at a lower computational cost with the aid of ML.

Involvement of the environment is indispensable for establishing the statistical distribution of system. We analyze the statistical distribution of a quantum system coupled strongly with a heat bath. This distribution is determined by tracing over the bath's degrees of freedom for the equilibrium system-plus-bath composite. The stability of system distribution is largely affected by the system--bath interaction strength. We propose that the quantum system exhibits a stable distribution only when its system response function in the frequency domain satisfies $\tilde\chi(\omega = 0+)>0$. We show our results by investigating the non-interacting bosonic impurity system from both the thermodynamic and dynamic perspectives. Our study refines the theoretical framework of canonical statistics, offering insights into thermodynamic phenomena in small-scale systems.

Biomembranes wrapping cells and organelles are not only the partitions that separate the insides but also dynamic fields for biological functions accompanied by membrane shape changes. In this review, we discuss the spatiotemporal patterns and fluctuations of membranes under nonequilibrium conditions. In particular, we focus on theoretical analyses and simulations. Protein active forces enhance or suppress the membrane fluctuations; the membrane height spectra are deviated from the thermal spectra. Protein binding or unbinding to the membrane is activated or inhibited by other proteins and chemical reactions, such as ATP hydrolysis. Such active binding processes can induce traveling waves, Turing patterns, and membrane morphological changes. They can be represented by the continuum reaction-diffusion equations and discrete lattice/particle models with state flips. The effects of structural changes in amphiphilic molecules on the molecular-assembly structures are also discussed.

Aspera is a NASA Astrophysics Pioneers SmallSat mission designed to study diffuse OVI emission from the warm-hot phase gas in the halos of nearby galaxies. Its payload consists of two identical Rowland Circle-type long-slit spectrographs, sharing a single MicroChannel plate detector. Each spectrograph channel consists of an off-axis parabola primary mirror and a toroidal diffraction grating optimized for the 1013-1057 Angstroms bandpass. Despite the simple configuration, the optical alignment/integration process for Aspera is challenging due to tight optical alignment tolerances, driven by the compact form factor, and the contamination sensitivity of the Far-Ultraviolet optics and detectors. In this paper, we discuss implementing a novel multi-phase approach to meet these requirements using state-of-the-art optical metrology tools. For coarsely positioning the optics we use a blue-laser 3D scanner while the fine alignment is done with a Zygo interferometer and a custom computer-generated hologram. The detector focus requires iterative in-vacuum alignment using a Vacuum UV collimator. The alignment is done in a controlled cleanroom facility at the University of Arizona.

Understanding the noise characteristics of high quantum efficiency silicon-based ultraviolet detectors, developed by the Microdevices Lab at the Jet Propulsion Laboratory, is critical for current and proposed UV missions using these devices. In this paper, we provide an overview of our detector noise characterization test bench that uses delta-doped, photon counting, Electron-multiplying CCDs (EMCCDs) to understand the fundamental noise properties relevant to all silicon CCDs and CMOS arrays. This work attempts to identify the source of the dark current plateau that has been previously measured with photon-counting EMCCDs and is known to be prevalent in other silicon-based arrays. It is suspected that the plateau could be due to a combination of detectable photons in the tail of blackbody radiation of the ambient instrument, low-level light leaks, and a non-temperature-dependent component that varies with substrate voltage. Our innovative test setup delineates the effect of the ambient environment during dark measurements by independently controlling the temperature of the detector and surrounding environment. We present the design of the test setup and preliminary results.

F. Pastawski and J. Preskill discussed error correction of quantum annealing (QA) based on a parity-encoded spin system, known as the Sourlas-Lechner-Hauke-Zoller (SLHZ) system. They pointed out that the SLHZ system is closely related to a classical low-density parity-check (LDPC) code and demonstrated its error-correcting capability through a belief propagation (BP) algorithm assuming independent random spin-flip errors. In contrast, Ablash et al. suggested that the SLHZ system does not receive the benefits of post-readout decoding. The reason is that independent random spin-flips are not the most relevant error arising from sampling excited states during the annealing process, whether in closed or open system cases. In this work, we revisit this issue: we propose a very simple decoding algorithm to eliminate errors in the readout of SLHZ systems and show experimental evidence suggesting that SLHZ system exhibits error-correcting capability in decoding annealing readouts. Our new algorithm can be thought of as a bit-flipping algorithm for LDPC codes. Assuming an independent and identical noise model, we found that the performance of our algorithm is comparable to that of the BP algorithm. The error correcting-capability for the sampled readouts was investigated using Monte Carlo calculations that simulate the final time distribution of QA. The results show that the algorithm successfully eliminates errors in the sampled readouts under conditions where error-free state or even code state is not sampled at all. Our simulation suggests that decoding of annealing readouts will be successful if the correctable states can be sampled by annealing, and annealing can be considered to play a role as a pre-process of the classical decoding process. This knowledge will be useful for designing and developing practical QA based on the SLHZ system in the near future.

Since its invention by Arthur Ashkin and colleagues at Bell Labs in the 1970s, optical micromanipulation, also known as optical tweezers or laser tweezers, has evolved remarkably to become one of the most convenient and versatile tools for studying soft materials, including biological systems. Arthur Ashkin received the Nobel Prize in Physics in 2018 for enabling these extraordinary scientific advancements. Essentially, a focused laser beam is used to apply and measure minuscule forces from a few piconewtons to femtonewtons by utilizing light-matter interaction at mesoscopic length scales. Combined with advanced microscopy and position-sensing techniques, optical micromanipulations enable us to investigate diverse aspects of functional soft materials. These include studying mechanical responses through force-elongation measurements, examining the structural properties of complex fluids employing microrheology, analyzing chemical compositions using spectroscopy, and sorting cells through single-cell analysis. Furthermore, it is utilized in various soft-matter-based devices, such as laser scissors and optical motors in microfluidic channels. This chapter presents an overview of optical micromanipulation techniques by describing fundamental theories and explaining the design considerations of conventional single-trap and dual-trap setups as well as recent improvisations. We further discuss their capabilities and applications in probing exotic soft-matter systems and in developing widely utilized devices and technologies based on functional soft materials.

We report an experimental study of a one-dimensional quintuple-quantum-dot array integrated with two quantum dot charge sensors in an InAs nanowire. The device is studied by measuring double quantum dots formed consecutively in the array and corresponding charge stability diagrams are revealed with both direct current measurements and charge sensor signals. The one-dimensional quintuple-quantum-dot array are then tuned up and its charge configurations are fully mapped out with the two charge sensors. The energy level of each dot in the array can be controlled individually by using a compensated gate architecture (i.e., "virtual gate"). After that, four dots in the array are selected to form two double quantum dots and ultra strong inter-double-dot interaction is obtained. A theoretical simulation based on a 4-dimensional Hamiltonian confirms the strong coupling strength between the two double quantum dots. The highly controllable one-dimensional quantum dot array achieved in this work is expected to be valuable for employing InAs nanowires to construct advanced quantum hardware in the future.

Supramolecular crystal gels, a subset of molecular gels, form through self-assembly of low molecular weight gelators into interconnecting crystalline fibers, creating a three-dimensional soft solid network. This study focuses on the formation and properties of viologen-based supramolecular crystalline gels. It aims to answer key questions about the tunability of network properties and the origin of these properties through in-depth analyses of the gelation kinetics triggered by thermal quenching. Experimental investigations, including UV-Vis absorption spectroscopy, rheology, microscopy and scattering measurements, contribute to a comprehensive and self-consistent understanding of the system kinetics. We confirm that the viologen-based gelators crystallize into nanometer radius hollow tubes that assemble into micro to millimetric spherulites. We then show that the crystallization follows the Avrami theory and is based on pre-existing nuclei. We also establish that the growth is interface controlled leading to the hollow tubes to branch into spherulites with fractal structures. Finally, we demonstrate that the gel properties can be tuned depending on the quenching temperature. Lowering the temperature results in the formation of denser and smaller spherulites. In contrast, the gels elasticity is not significantly affected by the quench temperature, leading us to hypothesize that the spherulites densification occurs at the expense of the connectivity between spherulite.

Biological vision systems seamlessly integrate feature-sensitive spatial processing and strong nonlinearity. The retina performs feature detection via ganglion cells, which nonlinearly enhance image features such as edges through lateral inhibition processes between neighbouring cells. As demand for specialised machine learning hardware increases, physical systems are sought which combine feature detection with the nonlinear processing required for tasks such as image classification. Currently, physical machine vision schemes detect features linearly, then employ separate digital systems for subsequent nonlinear activation. Here, we introduce a bio-inspired 'retinomorphic' machine vision platform using a semiconductor network laser. The system hosts many spatially-overlapping lasing modes which detect multiple image features in parallel via their lasing amplitude. Integrated nonlinearity is provided by antagonistic competition for gain between modes - a photonic analogue of the lateral inhibition in retinal cells. Detected feature maps are fed back through the system, providing multi-layer image classification with intrinsic nonlinear processing. Accuracies of 98.05% and 87.85% are achieved for MNIST digits and Fashion-MNIST tasks respectively.

This work introduces an on-the-fly (i.e., online) linear unmixing method which is able to sequentially analyze spectral data acquired on a spectrum-by-spectrum basis. After deriving a sequential counterpart of the conventional linear mixing model, the proposed approach recasts the linear unmixing problem into a linear state-space estimation framework. Under Gaussian noise and state models, the estimation of the pure spectra can be efficiently conducted by resorting to Kalman filtering. Interestingly, it is shown that this Kalman filter can operate in a lower-dimensional subspace while ensuring the nonnegativity constraint inherent to pure spectra. This dimensionality reduction allows significantly lightening the computational burden, while leveraging recent advances related to the representation of essential spectral information. The proposed method is evaluated through extensive numerical experiments conducted on synthetic and real Raman data sets. The results show that this Kalman filter-based method offers a convenient trade-off between unmixing accuracy and computational efficiency, which is crucial for operating in an on-the-fly setting. To the best of the authors' knowledge, this is the first operational method which is able to solve the spectral unmixing problem efficiently in a dynamic fashion. It also constitutes a valuable building block for benefiting from acquisition and processing frameworks recently proposed in the microscopy literature, which are motivated by practical issues such as reducing acquisition time and avoiding potential damages being inflicted to photosensitive samples.

The analysis of empirical data through model-free inequalities leads to the conclusion that violations of Bell-type inequalities by empirical data cannot have any significance unless one believes that the universe operates according to the rules of a mathematical model.

The applicability of first-order Fermi acceleration in explaining the cosmic ray spectrum has been reexamined using recent results on shock acceleration mechanisms from the Multiscale Magnetospheric mission in Earth's bow shock. It is demonstrated that the Fermi mechanism is a crude approximation of the ballistic surfing acceleration (BSA) mechanism. While both mechanisms yield similar expressions for the energy gain of a particle after encountering a shock once, leading to similar power-law distributions of the cosmic ray energy spectrum, the Fermi mechanism is found to be inconsistent with fundamental equations of electrodynamics. It is shown that the spectral index of cosmic rays is determined by the average magnetic field compression rather than the density compression, as in the Fermi model. It is shown that the knee observed in the spectrum at an energy of 5x10^{15} eV could correspond to ions with a gyroradius comparable to the size of shocks in supernova remnants. The BSA mechanism can accurately reproduce the observed spectral index s = -2.5 below the knee energy, as well as a steeper spectrum, s = -3, above the knee. The acceleration time up to the knee, as implied by BSA, is on the order of 300 years. First-order Fermi acceleration does not represent a physically valid mechanism and should be replaced by ballistic surfing acceleration in applications or models related to quasi-perpendicular shocks in space. It is noted that BSA, which operates outside of shocks, was previously misattributed to shock drift acceleration (SDA), which operates within shocks.

In flat-band systems with non-orthogonal compact localized states (CLSs), onsite perturbations couple neighboring CLSs and generate exponentially-decaying impurity states, whose degree of localization depends on lattice parameters. In this work, a diamond chain with constant magnetic flux per plaquette is decorated with several controlled onsite impurities in a patterned arrangement, generating an effective system that emerges from the flat band. The coupling distribution of the effective system is determined by the relative distance between impurities and the value of the flux, which can be chosen to engineer a wide variety of models. We employ a staggered distribution of impurities that effectively produces the well-known Su-Schrieffer-Heeger model, and show that the topological edge states display an enhanced robustness to non-chiral disorder due to an averaging effect over their extension. Finally, we provide a route to implement the system experimentally using optical waveguides that guide orbital angular momentum (OAM) modes. This work opens the way for the design of topologically protected impurity states in other flat-band systems or physical platforms with non-orthogonal bases.

Ferroelectric tunnel junctions (FTJs) are a class of memristor which promise low-power, scalable, field-driven analog operation. In order to harness their full potential, operation with identical pulses is targeted. In this paper, several weight update schemes for FTJs are investigated, using either non-identical or identical pulses, and with time delays between the pulses ranging from 1 us to 10 s. Experimentally, a method for achieving non-linear weight update with identical pulses at long programming delays is demonstrated by limiting the switching current via a series resistor. Simulations show that this concept can be expanded to achieve weight update in a 1T1C cell by limiting the switching current through a transistor operating in sub-threshold or saturation mode. This leads to a maximum linearity in the weight update of 87%. The scaling behaviour of this scheme as devices are reduced to the sub-micron range is investigated, and the linearity slightly reduces in scaled devices to a maximum of 81%. The origin of the remaining non-linearity is discussed, as well as how this can be overcome.

Consider the scenario where an infinite number of players (i.e., the \textit{thermodynamic} limit) find themselves in a Prisoner's dilemma type situation, in a \textit{repeated} setting. Is it reasonable to anticipate that, in these circumstances, cooperation will emerge? This paper addresses this question by examining the emergence of cooperative behaviour, in the presence of \textit{noise} (or, under \textit{selection pressure}), in repeated Prisoner's Dilemma games, involving strategies such as \textit{Tit-for-Tat}, \textit{Always Defect}, \textit{GRIM}, \textit{Win-Stay, Lose-Shift}, and others. To analyze these games, we employ a numerical Agent-Based Model (ABM) and compare it with the analytical Nash Equilibrium Mapping (NEM) technique, both based on the \textit{1D}-Ising chain. We use \textit{game magnetization} as an indicator of cooperative behaviour. A significant finding is that for some repeated games, a discontinuity in the game magnetization indicates a \textit{first}-order \textit{selection pressure/noise}-driven phase transition. The phase transition is particular to strategies where players do not severely punish a single defection. We also observe that in these particular cases, the phase transition critically depends on the number of \textit{rounds} the game is played in the thermodynamic limit. For all five games, we find that both ABM and NEM, in conjunction with game magnetization, provide crucial inputs on how cooperative behaviour can emerge in an infinite-player repeated Prisoner's dilemma game.

Recent investigations have demonstrated that multi-phonon scattering processes substantially influence the thermal conductivity of materials, posing significant computational challenges for classical simulations as the complexity of phonon modes escalates. This study examines the potential of quantum simulations to address these challenges, utilizing Noisy Intermediate Scale Quantum era (NISQ) quantum computational capabilities and quantum error mitigation techniques to optimize thermal conductivity calculations. Employing the Variational Quantum Eigensolver (VQE) algorithm, we simulate phonon-phonon contributions based on the Boltzmann Transport Equation (BTE). Our methodology involves mapping multi-phonon scattering systems to fermionic spin operators, necessitating the creation of a customized ansatz to balance circuit accuracy and depth. We construct the system within Fock space using bosonic operators and transform the Hamiltonian into the sum of Pauli operators suitable for quantum computation. By addressing the impact of non-unitary noise effects, we benchmark the noise influence and implement error mitigation strategies to develop a more efficient model for quantum simulations in the NISQ era.