Large Parameter Space Research Articles

Selective Up‐ and Down‐Conversion Luminescence for Nonlinear Expansion of Unclonable Parameter Space

AbstractOptical physical unclonable functions (PUFs) have attracted considerable attention as an immediately exploitable cryptographic primitive for high‐level hardware security attributed to their potential for implementing a large parameter space through the incorporation of robust optical phenomena. However, previous optical PUFs primarily relied on linear and single‐channel optical processes, requiring an increase in the number of optical inputs (materials or wavelengths) in a monotonous manner to scale up challenge‐response pairs. Herein, an optical PUF capable of nonlinearly expanding the parameter space to enhance the cryptographic strength through the selective adjustment of up‐ and down‐conversion luminescence is introduced. The nonlinearity in the expansion of the parameter space originates from a random distribution of three types of microspheres, with their shells designed to exhibit various positional arrangements of upconversion nanoparticles and perovskite crystals. Because energy and photon interactions depend on their positional proximity and excitation power, adjusting the two excitation inputs into five power steps enables the single PUF to generate 30 unique cryptographic keys, which is 15 times greater than what a linear system can offer. The PUF also demonstrates high stability, maintaining its cryptographic performance when exposed to heat, moisture, and long‐term laser excitation, underscoring its practical applicability in security protocols.

Influences in Experimental Studies on the Characteristics of Transonic Shock Buffet

This article examines the evolution of transonic shock buffet on the OAT15A supercritial airfoil, aspiring to elucidate some of the causes of disparate buffet-relevant settings reported in the literature. Experimental campaigns on three distinct chord lengths are conducted thus resulting in chord Reynolds numbers of 1.3×106, 2.0×106, and 2.7×106. Continuous variations in Mach and AoA allow mapping the shock wave behavior across a large parameter space and capturing the transition between prebuffet, onset, developed buffet, and offset. High-speed focusing schlieren imaging is employed to extract the temporal and spectral nature of buffet and to deduce key quantities that characterize the unsteadiness. Three-dimensional effects along the span due to interference with the side walls are assessed thoroughly, revealing that phases of upstream shock motion and large separation are accompanied by 3D distortion. Even though this effect gains relevance at reduced aspect ratio, a substantially 2D character of the shock surface around the centerplane is confirmed. Fully developed buffet governed by a 2D buffet mode is proven to exist across all test cases, although the buffet peak condition is gradually shifted toward reduced angle of attack, yet greater Mach number with increased Reynolds number. Conversely, accompanied by severely increased angles of attack, the buffet mechanism appears more volatile for small chord length and is consolidated with enlarged chord, which strongly suggests a dependence on the state of the turbulent boundary layer.

Prediction of wind loading on masked angle members in lattice tower structures

Quantification of the wind loading is critical in lattice tower structure engineering. In this study, we use Computational Fluid Dynamics (CFD) to investigate the aerodynamic loading on two angle members of a lattice tower structure. The presence of two bluff bodies means that one of the angle members may be masked by the other and will thus undergo less wind loading, which is called the mask effect. In the current work, we were specifically interested in investigating this effect with respect to the angles of attack of the two angle members along with the inline and normal separation distance. The four parameters yield a large parameter space which is best tackled using a sophisticated sampling method such as Latin hypercube sampling. First, we validated our RANS simulation results against experiments and Large Eddy Simulation (LES). Then, we performed two-dimensional simulations on a large range of configurations to underline the impact of the input parameters on the output variables, which are the drag and lift coefficients. To produce a tool that can be applied by a structural engineer, the database created using the time-consuming CFD simulations was used to create a correlation between the input parameters and output variables. The functions used in the correlations were designed to respect the symmetries and limiting behavior in the problem. We then investigated the performance of four different cross-validated regression models to predict the drag and lift coefficients. Once created, the regression models produce a method that does not require CFD simulations to be run. The models’ accuracy represents a significant improvement in predicting wind loads on lattice towers. While further refinement is possible, the current results provide a solid basis for engineering design purposes.

A multi-medical image encryption algorithm based on ROI and DNA coding

Abstract With the rapid development of information technology in the field of electronic medicine, the confidentiality of medical images has received increasing attention. The research on the encryption of multiple medical images holds greater practical significance. In this paper, the encryption algorithm is designed specifically for the region of interest (ROI) in medical images. Different techniques and methods are used to encrypt ROI and region of non-interest (RONI) respectively. By combining improved Zigzag scrambling, DNA coding, and the Fisher-Yates shuffle, we place an emphasis on protecting the ROI, and achieve secure encryption for medical images of any number and size. In addition, a new one-dimensional chaotic system S-LCS with larger parameter space and better chaotic properties is proposed. In this encryption scheme, the information about the ROI serves as the secret key, and the initial values and parameters of the chaotic sequences required for encryption are calculated from this key. This strengthens the relationship between the key and the plaintext, enhancing the security of the key. Through testing and comparative analysis, it has been found that the encryption algorithm has high enough security, can resist various attacks, and has high encryption efficiency in the application scenario of multi-image encryption.

Theoretical framework for the response of barotropic shelf current to along-shelf bathymetric perturbations

Abstract The mechanism underlying the response of barotropic shelf current to along-shelf bathymetric perturbations is rationalized by a one-dimensional vorticity equation. Fourier analysis of the equation in the wavenumber domain suggests that bathymetric features with relatively short length scales in the alongshore direction drive steady cross-shelf velocity undulations when the shelf circulation is against the direction of Kelvin wave propagation (upwelling regime). In contrast, much weaker and non-oscillatory cross-shelf velocity is expected when the shelf circulation is in alignment with propagating Kelvin waves (downwelling regime). The velocity undulations in the upwelling regime result from a positive feedback mechanism associated with potential vorticity (PV) advection; however, the presence of bottom friction restricts such undulations within a few wavelengths. The characteristic wavelength of undulations is determined by when the normalized drag coefficient , where u is the along-shelf mean flow speed, Hs is shelf depth, αs is the bottom slope, f is the Coriolis parameter, and r is the linear drag coefficient. Using this formula, a scale of the cross-shelf displacement distance Yt is proposed, aligning with one-layer barotropic simulations over a large parameter space. The study provides a framework for investigating barotropic cross-shelf exchange caused by shelf circulation over irregular bottom bathymetries.

Multi-image encryption scheme using cross-plane coupling permutation and plain-by-plain wave diffusion

Images contain a wealth of visual information, are susceptible to unauthorized access due to their vulnerability and sensitivity. This paper designs a novel multi-image encryption scheme for protecting the privacy of images of different sizes and types. Initially, a 2D memristive hyperchaotic map (2D-MHM) is designed and subjected to various dynamic analyses and randomness evaluations. The results demonstrate that the proposed map possesses an exceptionally large parameter space, high Lyapunov exponent and sample entropy, and has successfully passed the entire suite of NIST test, verifying its feasibility for confidential communication. Then we present a multi-image encryption scheme combining cross-plane coupling permutation and plain-by-plain wave diffusion to realize random exchange and global variation of pixels in different planes. The performance evaluation and numerical analysis demonstrate that the scheme is resilient against multifarious types of attacks, possesses great security while effectively enhancing encryption efficiency. Finally, the proposed scheme is compared with advanced algorithms and its application in healthcare is discussed, exhibiting its superiority in multiple aspects.

Evolution mapping II: describing statistics of the non-linear cosmic velocity field.

Abstract We extend the evolution mapping approach, introduced in the first paper of this series to describe non-linear matter density fluctuations, to statistics of the cosmic velocity field. This framework classifies cosmological parameters into shape parameters, which determine the shape of the linear matter power spectrum, PL(k, z), and evolution parameters, which control its amplitude at any redshift. Evolution mapping leverages the fact that density fluctuations in cosmologies with identical shape parameters but different evolution parameters exhibit similar non-linear evolutions when expressed as a function of clustering amplitude. We analyse a suite of N-body simulations sharing identical shape parameters but spanning a wide range of evolution parameters. Using a method for estimating the volume-weighted velocity field based on the Voronoi tesselation of simulation particles, we study the non-linear evolution of the velocity divergence power spectrum, Pθθ(k), and its cross-power spectrum with the density field, Pδθ(k). We demonstrate that the evolution mapping relation applies accurately to Pθθ(k) and Pδθ(k). While this breaks down in the strongly non-linear regime, deviations can be modelled in terms of differences in the suppression factor, g(a) = D(a)/a, similar to those for the density field. Such modelling describes the differences in Pθθ(k) between models with the same linear clustering amplitude to better than 1percnt accuracy at all scales and redshifts considered. Evolution mapping simplifies the description of the cosmological dependence of non-linear density and velocity statistics, streamlining the sampling of large cosmological parameter spaces for cosmological analysis.

Statistical estimation of full-sky radio maps from 21cm array visibility data using Gaussian Constrained Realisations

Abstract An important application of next-generation wide-field radio interferometers is making high dynamic range maps of radio emission. Traditional deconvolution methods like CLEAN can give poor recovery of diffuse structure, prompting the development of wide-field alternatives like Direct Optimal Mapping and m-mode analysis. In this paper, we propose an alternative Bayesian method to infer the coefficients of a full-sky spherical harmonic basis for a drift-scan telescope with potentially thousands of baselines, that can precisely encode the uncertainties and correlations between the parameters used to build the recovered image. We use Gaussian Constrained Realisations (GCR) to efficiently draw samples of the spherical harmonic coefficients, despite the very large parameter space and extensive sky-regions of missing data. Each GCR solution provides a complete, statistically-consistent gap-free realisation of a full-sky map conditioned on the available data, even when the interferometer’s field of view is small. Many realisations can be generated and used for further analysis and robust propagation of statistical uncertainties. In this paper, we present the mathematical formalism of the spherical harmonic GCR-method for radio interferometers. We focus on the recovery of diffuse emission as a use case, along with validation of the method against simulations with a known diffuse emission component.

Deep learning methods for modeling infrasound transmission loss in the middle atmosphere

Accurate modeling of infrasound transmission losses (TLs) is essential to assess the performance of the global International Monitoring System (IMS) infrasound network. Among existing propagation modeling tools, parabolic equation method (PE) enables TLs to be finely modeled, but its computational cost does not allow exploration of a large parameter space for operational monitoring applications. To reduce computation times, Brissaud et al. (2022) explored the potential of convolutional neural networks (CNNs) trained on a large set of regionally simulated wavefields (>1000 km from the source) to predict TLs with negligible computation times compared to PE simulations. However, this new method shows difficulties in upwind conditions, especially at low frequencies, and causal issues with winds at large distances from the source affecting ground TLs close to the source. In this study, we have developed an optimized CNN network designed to minimize prediction errors while predicting TLs from globally simulated combined temperature and wind fields spanning over propagation ranges of 4000 km. Our approach enhances the previously proposed one by implementing key optimizations that improve the overall architecture performances. The implemented model predicts TLs with an average error of 20 dB in the whole frequency band (0.1-4 Hz) and explored realistic atmospheric scenarios.

FLAIM: A reduced volume ignition model for the compression and thermonuclear burn of spherical fuel capsules

We present the “First Light Advanced Ignition Model” (FLAIM), a reduced model for the implosion, adiabatic compression, volume ignition and thermonuclear burn of a spherical DT fuel capsule utilising a high-Z metal pusher. FLAIM is characterised by a highly modular structure, which makes it an appropriate tool for optimisations, sensitivity analyses and parameter scans. One of the key features of the code is the 1D description of the hydrodynamic operator, which has a minor impact on the computational efficiency, but allows us to gain a major advantage in terms of physical accuracy. We demonstrate that a more accurate treatment of the hydrodynamics plays a primary role in closing most of the gap between a simple model and a general 1D rad-hydro code, and that only a residual part of the discrepancy is attributable to the heat losses. We present a detailed quantitative comparison between FLAIM and 1D rad-hydro simulations, showing good agreement over a large parameter space in terms of temporal profiles of key physical quantities, ignition maps and typical burn metrics.

Applying the Expectation-Maximization Algorithm to Multivariate Signal Extraction

Understanding time series signal extraction is vital for federal agencies, especially as data collection accelerates. One natural extension is moving from univariate to multivariate signal extraction, which offers the promise of reducing extraction error by exploiting cross-sectional relationships. However, such an extension ushers in new computational challenges, viz. larger parameter spaces and more complicated objective functions. The Expectation-Maximization (EM) algorithm provides a methodology to implicitly compute (or approximate) maximum likelihood estimates (MLEs). This paper provides methodology for applying the EM algorithm to a class of latent component multivariate time series models that allow for a nuanced specification of the unobserved signal. We derive an explicit formula for the maximization step, which facilitates computation speed while also improving the stability of the algorithm. Numerical studies demonstrate EM’s ability to efficiently compute MLEs in low-dimensional systems, while also providing feasible estimates in moderate-dimensional systems where MLEs are infeasible to compute. Applications to monthly housing starts and daily immigration are provided.

High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization.

The subcellular organization of proteins carries important information on cellular state and gene function, yet currently there are no technologies that enable accurate measurement of subcellular protein localizations at scale. Here we develop an approach for pooled endogenous protein tagging using prime editing, which coupled with an optical readout and sequencing, provides a snapshot of proteome organization in a manner akin to perturbation-based CRISPR screens. We constructed a pooled library of 17,280 pegRNAs designed to exhaustively tag 60 endogenous proteins spanning diverse localization patterns and explore a large space of genomic and pegRNA design parameters. Pooled measurements of tagging efficiency uncovered both genomic and pegRNA features associated with increased efficiency, including epigenetic states and interactions with transcription. We integrate pegRNA features into a computational model with predictive value for tagging efficiency to constrain the design space of pegRNAs for large-scale peptide knock-in. Lastly, we show that combining in-situ pegRNA sequencing with high-throughput deep learning image analysis, enables exploration of subcellular protein localization patterns for many proteins in parallel following a single pooled lentiviral transduction, setting the stage for scalable studies of proteome dynamics across cell types and environmental perturbations.

Exploring the catastrophic regime: thermodynamics and disintegration in head-on planetary collisions

ABSTRACT Head-on giant impacts (collisions between planet-sized bodies) are frequently used to study the planet formation process as they present an extreme configuration where the two colliding bodies are greatly disturbed. With limited computing resources, focusing on these extreme impacts eases the burden of exploring a large parameter space. Results from head-on impacts are often then extended to study oblique impacts with angle corrections or used as initial conditions for other calculations, for example, the evolution of ejected debris. In this study, we conduct a detailed investigation of the thermodynamic and energy budget evolution of high-energy head-on giant impacts, entering the catastrophic impacts regime, for target masses between 0.001 and 12 M$_{{\oplus }}$. We demonstrate the complex interplay of gravitational forces, shock dynamics, and thermodynamic processing in head-on impacts at high energy. Our study illustrates that frequent interactions of core material with the liquid side of the vapour curve could have cumulative effects on the post-collision remnants, leading to fragmentary disintegration occurring at lower impact energy. This results in the mass of the largest remnant diverging significantly from previously developed scaling laws. These findings suggest two key considerations: (1) head-on planetary collisions for different target masses do not behave similarly, so caution is needed when applying scaling laws across a broad parameter space; and (2) an accurate model of the liquid-vapour phase boundary is essential for modelling giant impacts. Our findings highlight the need for careful consideration of impact configurations in planetary formation studies, as head-on impacts involve a complex interplay between thermodynamic processing, shocks, gravitational forces, and other factors.

Numerical and experimental investigation of the effect of interstitial liquid viscosity on the collapse of wet granular columns

The collapse of wet granular columns with high- and low-viscosity interstitial liquid is investigated through experiments and numerical simulations. By incorporating both capillary and viscous force models of interstitial liquid in the Discrete Element Method (DEM), simulations have been conducted and reproduced consistent collapse processes with the experiment. The influence of water content, contact angle, particle diameter, liquid surface tension, and viscosity is explored over a large parameter space using DEM. For wet granular columns with low-viscosity interstitial liquid of water, the final profile of the collapsed column can be predicted by Bond number (Bo), a ratio of particle gravity to capillary force. For wet granular columns where the inertial effect dominates and both capillary and viscous forces are significant, dimensional analysis is employed to characterize the collapse, indicating that the collapsed profile can be predicted by Bo and Galileo number (Ga). Using water and silicone oils with different viscosities as interstitial liquids, simulations show that the collapsed profile depends on Bo with a low capillary number (Ca ∼ 10−3), Bo and Ga when Ca is intermediate (Ca ∼ 10−1). When Ca is high (Ca > 100), the final profile is solely controlled by Bo and a rolling-collapsed regime is observed.

Sterile neutrino dark matter within the νSMEFT

Sterile neutrinos with masses at the keV scale and mixing to the active neutrinos offer an elegant explanation of the observed dark matter (DM) density. However, the very same mixing inevitably leads to radiative photon emission and the non-observation of such peaked X-ray lines rules out this minimal sterile neutrino DM hypothesis. We show that in the context of the Standard Model effective field theory with sterile neutrinos (νSMEFT), higher dimensional operators can produce sterile neutrino DM in a broad range of parameter space. In particular, νSMEFT interactions can open the large mixing parameter space due to their destructive interference, through operator mixing or matching, in the X-ray emission. We also find that, even in the zero mixing limit, the DM density can always be explained by νSMEFT operators. The testability of the studied νSMEFT operators in searches for electric dipole moments, neutrinoless double beta decay, and pion decay measurements is discussed.

Interstellar Glycolaldehyde, Methyl Formate, and Acetic Acid. II. Chemical Modeling of the Bimodal Abundance Pattern in NGC 6334I

Gas-phase abundance ratios between C2H4O2 isomers methyl formate (MF), glycolaldehyde (GA), and acetic acid (AA) are typically on the order of 100:10:1 in star-forming regions. However, an unexplained divergence from this neat relationship was recently observed toward a collection of sources in the massive protocluster NGC 6334I; some sources exhibited extreme MF:GA ratios, producing a bimodal behavior between different sources, while the MF:AA ratio remained stable. Here, we use a three-phase gas-grain hot-core chemical model to study the effects of a large parameter space on the simulated C2H4O2 abundances. A combination of high gas densities and long timescales during ice-mantle desorption (∼125–160 K) appears to be the physical cause of the high MF:GA ratios. The main chemical mechanism for GA destruction occurring under these conditions is the rapid adsorption and reaction of atomic H with GA on the ice surfaces before it has time to desorb. The different binding energies of MF and GA on water ice are crucial to the selectivity of the surface destruction mechanism; individual MF molecules rapidly escape the surface when exposed by water loss, while GA lingers and is destroyed by H. Moderately elevated cosmic-ray ionization rates can increase absolute levels of “complex organic molecule” (COM) production in the ices and increase the MF:GA ratio, but extreme values are destructive for gas-phase COMs. We speculate that the high densities required for extreme MF:GA ratios could be evidence of COM emission dominated by COMs desorbing within a circumstellar disk.

A stochastic averaging mathematical framework for design and optimization of nonlinear energy harvesters with several electrical DOFs

Energy harvesters for mechanical vibrations are electro-mechanical systems designed to capture ambient dispersed kinetic energy, and to convert it into usable electrical power. The random nature of mechanical vibrations, combined with the intrinsic non-linearity of the harvester, implies that long, time domain Monte-Carlo simulations are required to assess the device performance, making the analysis burdensome when a large parameter space must be explored. Therefore a simplified, albeit approximate, semi-analytical analysis technique is of paramount importance. In this work we present a methodology for the analysis and design of nonlinear piezoelectric energy harvesters for random mechanical vibrations. The methodology is based on the combined application of model order reduction, to project the dynamics onto a lower dimensional space, and of stochastic averaging, to calculate the stationary probability density function of the reduced variables. The probability distribution is used to calculate expectations of the most relevant quantities, like output voltage, harvested power and power efficiency. Based on our previous works, we consider an energy harvester with a matching network, interposed between the harvester and the load, that reduces the impedance mismatch between the two stages. The methodology is applied to the optimization of the matching network, allowing to maximize the global harvested power and the conversion efficiency. We show that the proposed methodology gives accurate predictions of the harvester’s performance, and that it can be used to significantly simplify the analysis, design and optimization of the device.

Aerothermal characterization of the CALLISTO vehicle during descent

AbstractAerothermal loads are a design driving factor during launcher development as the thermal loads directly influence thermal protection system (TPS) design and successively the possible flight trajectory and mission profiles. Recent developments in reusable launch vehicles (RLV) (e.g. SpaceX, Blue Origin) have added the dimension of refurbishment to the challenges the thermal design must consider. With the current European launcher roadmap moving towards a reusable first stage aerothermal loads may significantly change. The CALLISTO vehicle is a flight demonstrator for future reusable launcher stages and their technologies developed by a tri-national consortium and planned to fly in 2025. For this vehicle the highest heat fluxes are mainly due to heating from hot exhaust gases and heated air in the proximity of the aft bay and on the exposed structures like legs and fins. In the presented study we conducted computational fluid dynamics (CFD) studies to determine the aerothermal loads on the vehicle during descent through the landing approach corridor for both phase B and phase C aeroshapes. A defining difference to previous aerothermal databases (ATD) is that the CALLISTO demonstrator is planned to execute a series of test flights with different energy levels, as well as a final demo flight. This leads to an large parameter space the final aerothermal database needs to cover. The database development is described in detail and analysed for integral and local loads, as well as interpolation uncertainties. The final phase C database allows interpolation of interface heatfluxes for the entire flight domain (Mach number, density $$\rho )$$ ρ ) at varying angle of attack (AoA). Further the sensitivity of the plume-vehicle interaction to angle of attack, chemistry, thrust vector control (TVC) and engine throttling are investigated for a critical Mach number indicating further areas of improvement for future databases.

Variational tensor neural networks for deep learning

Deep neural networks (NNs) encounter scalability limitations when confronted with a vast array of neurons, thereby constraining their achievable network depth. To address this challenge, we propose an integration of tensor networks (TN) into NN frameworks, combined with a variational DMRG-inspired training technique. This in turn, results in a scalable tensor neural network (TNN) architecture capable of efficient training over a large parameter space. Our variational algorithm utilizes a local gradient-descent technique, enabling manual or automatic computation of tensor gradients, facilitating design of hybrid TNN models with combined dense and tensor layers. Our training algorithm further provides insight on the entanglement structure of the tensorized trainable weights and correlation among the model parameters. We validate the accuracy and efficiency of our method by designing TNN models and providing benchmark results for linear and non-linear regressions, data classification and image recognition on MNIST handwritten digits.

High Throughput Characterization of Organic Thin Film Transistors.

Automation is vital to accelerating research. In recent years,the application of self-driving labs to materials discovery and device optimization has highlighted many benefits and challenges inherent to these new technologies. Successful automated workflows offer tangible benefits to fundamental science and industrial scale-up by significantly increasing productivity and reproducibility all while enabling entirely new types of experiments. However, it's implemtation is often time-consuming and cost-prohibitive and necessitates establishing multidisciplinary teams that bring together domain-specific knowledge with specific skillsets in computer science and engineering. This perspective article provides a comprehensive overview of how the research group has adopted "hybrid automation" over the last 8 years by using simple automatic electrical testers (autotesters) as a tool to increase productivity and enhance reproducibility in organic thin film transistor (OTFT) research. From wearable and stretchable electronics to next-generation sensors and displays, OTFTs have the potential to be a key technology that will enable new applications from health to aerospace. The combination of materials chemistry, device manufacturing, thin film characterization and electrical engineering makes OTFT research challenging due to the large parameter space created by both diverse material roles and device architectures. Consequently, this research stands to benefit enormously from automation. By leveraging the multidisciplinary team and taking a user-centered design approach in the design and continued improvement of the autotesters, the group has meaningfully increased productivity, explored research avenues impossible with traditional workflows, and developed as scientists and engineers capable of effectively designing and leveraging automation to build the future of their fields to encourage this approach, the files for replicating the infrastructure are included, and questions and potential collaborations are welcomed.