Large Parameter Research Articles

High-Flow and Low-Flow Oxygen Delivery by Nasal Cannula Evaluated in Infant and Adult Airway Replicas.

The nasal cannula is widely regarded as a safe and effective means of administering low- and high-flow oxygen to patients irrespective of their age. However, variability in delivered oxygen concentration (FDO2 FDO2 ) via nasal cannula has the potential to pose health risks. The present study aimed to evaluate predictive equations for FDO2 over a large parameter space, including variation in breathing, oxygen flow, and upper-airway geometry representative of both young children and adults. Realistic nasal airway geometries were previously collected from medical scans of adults, infants, and neonates. Nasal airway replicas based on these geometries were used to measure the FDO2 for low-flow oxygen delivery during simulated spontaneous breathing. The present study extends previously published data sets to include higher oxygen flows. The extended data sets included nasal cannula oxygen flows that ranged from 6 to 65 L/min for the adult replicas, and from 0.5 to 6 L/min for the infant replicas. For both age groups, FDO2 was measured over a range of breathing frequencies, inspiratory to expiratory time ratios, and tidal volumes. Measured FDO2 values were compared with values predicted by using a previously derived flow-weighted equation. For both age groups, FDO2 was observed to increase nonlinearly with the ratio between oxygen flow supplied to the nasal cannula and the average inhalation flow. The previously derived flow-weighted equation over-predicted FDO2 at higher oxygen flows. A new empirical equation, therefore, was proposed to predict FDO2 for either age group as a function of nasal cannula flow, tidal volume, and inspiratory time. Predicted FDO2 values matched measured values, with average relative errors of 2.4% for infants and 4.3% for adults. A new predictive equation for FDO2 was obtained that accurately matched measured data in both adult and infant airway replicas for low- and high-flow regimens.

Fabrication uncertainty guided design optimization of a photonic crystal cavity by using Gaussian processes

We present a fabrication uncertainty aware and robust design optimization approach that can be used to obtain robust design estimates for nonlinear, nonconvex, and expensive model functions. It is founded on Gaussian processes and a Monte Carlo sampling procedure, and assumes knowledge about the uncertainties associated with a manufacturing process. The approach itself is iterative. First, a large parameter domain is sampled in a coarse fashion. This coarse sampling is used primarily to determine smaller candidate regions to investigate in a second, more refined sampling pass. This finer step is used to obtain an estimate of the expected performance of the found design parameter under the assumed manufacturing uncertainties. We apply the presented approach to the robust optimization of the Purcell enhancement of a photonic crystal nanobeam cavity. We obtain a predicted robust Purcell enhancement of F¯P≈3.6. For comparison we also perform an optimization without robustness. We find that an unrobust optimum of FP≈256.5 dwindles to only F¯P≈0.7 when fabrication uncertainties are taken into account. We thus demonstrate that the presented approach is able to find designs of significantly higher performance than those obtained with conventional optimization.

Genetic algorithm-enhanced microcomb state generation

Microcavities enable the generation of highly efficient microcombs, which find applications in various domains, such as high-precision metrology, sensing, and telecommunications. Such applications generally require precise control over the spectral features of the microcombs, such as free spectral range, spectral envelope, and bandwidth. Most existing methods for customizing microcomb still rely on manual exploration of a large parameter space, often lacking practicality and versatility. In this work, we propose a smart approach that employs genetic algorithms to autonomously optimize the parameters for generating and tailoring stable microcombs. Our scheme controls optical parametric oscillation in a microring resonator to achieve broadband microcombs spanning the entire telecommunication C-band. The high flexibility of our approach allows us to obtain complex microcomb spectral envelopes corresponding to various operation regimes, with the potential to be directly adapted to different microcavity geometries and materials. Our work provides a robust and effective solution for targeted soliton crystal and multi-soliton state generation, with future potential for next-generation telecommunication applications and artificial intelligence-assisted data processing.

Domain-wall Skyrmion phase in a rapidly rotating QCD matter

Based on the chiral perturbation theory at the leading order, we show a signal of the presence of a new phase in rapidly rotating QCD matter with two flavors, that is a domain-wall Skyrmion phase. Based on the chiral Lagrangian with a Wess-Zumino-Witten (WZW) term responsible for the chiral anomaly and chiral vortical effect, it was shown that the ground state is a chiral soliton lattice (CSL) consisting of a stack of η-solitons in a high density region under rapid rotation. In a large parameter region, a single η-soliton decays into a pair of non-Abelian solitons, each of which carries SU(2)V/U(1) ≃ ℂP1 ≃ S2 moduli as a consequence of the spontaneously broken vector symmetry SU(2)V. In such a non-Abelian CSL, we construct the effective world-volume theory of a single non-Abelian soliton to obtain a d = 2 + 1 dimensional ℂP1 model with a topological term originated from the WZW term. We show that when the chemical potential is larger than a critical value, a topological lump supported by the second homotopy group π2(S2) ≃ ℤ has negative energy and is spontaneously created, implying the domain-wall Skyrmion phase. This lump corresponds in the bulk to a Skyrmion supported by the third homotopy group π3[SU(2)] ≃ ℤ carrying a baryon number. This composite state is called a domain-wall Skyrmion, and is stable even in the absence of the Skyrme term. An analytic formula for the effective nucleon mass in this medium can be written only in terms of the meson’s constants as 42πfπfη/mπ~1.21\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ 4\\sqrt{2}\\pi {f}_{\\pi }{f}_{\\eta }/{m}_{\\pi}\\sim 1.21 $$\\end{document} GeV with the decay constants fπ and fη of the pions and η meson, respectively, and the pion mass mπ. This is reasonably heavier than the nucleon mass in the QCD vacuum.

Contrastive learning-based knowledge distillation for RGB-thermal urban scene semantic segmentation

RGB thermal semantic segmentation facilitates unmanned platforms to perceive and characterize their surrounding environment, which is critical for autonomous driving tasks. Deep-learning-based algorithms have achieved dominance in terms of accuracy and robustness. However, their large parameter sizes and significant computational demands impede their application in terminal devices. To address this challenge, we propose a novel strategy for achieving a balance between effectiveness and compactness. It includes a robust teacher network, CLNet-T, and a streamlined student network, CLNet-S. Using knowledge distillation (KD), we obtained an optimized model called CLNet-S*. Specifically, CLNet-T and CLNet-S were identical in all aspects except for the feature extraction component. They included a multi-attribute hierarchical feature interaction module (MHFI) and a detail-guided semantic decoder (DGSD). The MHFI initially filters features by considering the characteristics of the low- and high-level features. It gradually combines complementary and common features from various modalities in distinct receptive fields. DGSD uses edge and distribution information to guide semantic decoding, thereby improving the segmentation accuracy at class boundaries. To enhance the performance of the compact student model, our KD strategy includes detail, semantic response distillation (DSRD), and contrastive learning-based feature distillation (CLFD). Practically, DSRD enables the student model to gain knowledge from the teacher model at both the detailed and semantic levels. At the same time, CLFD increases the similarity of features within the same categories and emphasizes the distinctiveness of features between different categories in both the student and teacher models. Extensive experiments conducted on two standard datasets have consistently demonstrated that both CLNet-T and CLNet-S* outperform other state-of-the-art methods. The code and results are available at https://github.com/xiaodonguo/CLNet.

Materials genome engineering accelerates the research and development of organic and perovskite photovoltaics

AbstractThe emerging photovoltaic (PV) technologies, such as organic and perovskite PVs, have the characteristics of complex compositions and processing, resulting in a large multidimensional parameter space for the development and optimization of the technologies. Traditional manual methods are time‐consuming and labor‐intensive in screening and optimizing material properties. Materials genome engineering (MGE) advances an innovative approach that combines efficient experimentation, big database and artificial intelligence (AI) algorithms to accelerate materials research and development. High‐throughput (HT) research platforms perform multidimensional experimental tasks rapidly, providing a large amount of reliable and consistent data for the creation of materials databases. Therefore, the development of novel experimental methods combining HT and AI can accelerate materials design and application, which is beneficial for establishing material‐processing‐property relationships and overcoming bottlenecks in the development of emerging PV technologies. This review introduces the key technologies involved in MGE and overviews the accelerating role of MGE in the field of organic and perovskite PVs.

Multichannel scattering for the Schrödinger equation on a line with different thresholds at both infinities

The multichannel scattering problem for the stationary Schrödinger equation on a line with different thresholds at both infinities is investigated. The analytical structure of the Jost solutions and of the transition matrix relating the Jost solutions as functions of the spectral parameter is described. Unitarity of the scattering matrix is proved in the general case when some of the scattering channels can be closed and the thresholds can be different at left and right infinities on the line. The symmetry relations of the S -matrix are established. The condition determining the bound states is obtained. The asymptotics of the Jost functions and of the transition matrix are derived for a large spectral parameter.

The 230 GHz Variability of Numerical Models of Sagittarius A*. I. Parameter Surveys on Varying the Ion-to-electron Temperature Ratio Under Strongly Magnetized Conditions

The 230 GHz lightcurves of Sagittarius A* (Sgr A*) predicted by general relativistic magnetohydrodynamics and general relativistic ray-tracing (GRRT) models by the Event Horizon Telescope Collaboration have higher variability M ΔT compared to observations. In this series of papers, we explore the origin of such large brightness variability. In this first paper, we performed large GRRT parameter surveys that span from the optically thin to the optically thick regimes, covering the ion-to-electron temperature ratio under strongly magnetized conditions, R Low, from 1 to 60. We find that increasing R Low can lead to either an increase or a reduction in M ΔT depending on the other model parameters, making it consistent with the observed variability of Sgr A* in some cases. Our analysis of GRRT image snapshots finds that the major contribution to the large M ΔT for the R Low = 1 models comes from the photon ring. However, secondary contributions from the accretion flow are also visible depending on the spin parameter. Our work demonstrates the importance of the electron temperature used for modeling radiatively inefficient accretion flows and places new constraints on the ion-to-electron temperature ratio. A more in-depth analysis for understanding the dependencies of M ΔT on R Low will be performed in subsequent papers.

Criterion for ultra-fast bubble walls: the impact of hydrodynamic obstruction

The Bödeker-Moore thermal friction [1] is usually used to determine whether or not a bubble wall can run away. However, the friction on the wall is not necessarily a monotonous function of the wall velocity and could have a maximum before it reaches the Bödeker-Moore limit. In this paper, we compare the maximal hydrodynamic obstruction, a frictional force that exists in local thermal equilibrium, and the Bödeker-Moore thermal friction. We study the former in a fully analytical way, clarifying its physical origin and providing a simple expression for its corresponding critical phase transition strength above which the driving force cannot be balanced out by the maximal hydrodynamic obstruction. We find that for large parameter space, the maximal hydrodynamic obstruction is larger than the Bödeker-Moore thermal friction, indicating that the conventional criterion for the runaway behavior of the bubble wall may have to be modified. We also explain how to apply efficiently the modified criterion to particle physics models and discuss possible limitations of the analysis carried out in this paper.

Geoacoustic inversion using 3D modal rays on the New England Shelf Break

During the Seabed Characterization Experiment in 2021 (SBCEX21), hydrophones deployed on the seabed of the New England Shelf Break recorded acoustic signals from SUS charge explosions. Acoustic data from one of these TOSSIT hydrophones displays evident modal structures which cannot be fully captured with 2D propagation models. In this presentation, 3D acoustic modal ray tracing is shown to provide a stronger match for recorded modal travel times, and is used to invert for geoacoustic parameters of the seabed. Performance of 2D and 3D modal propagation models are presented, and limitations of the methods are shown in the context of large parameter space inversion efforts. [This research is supported by The Office of Naval Research.]

SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis.

Automatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and introduces spatial and channel convolution blocks into the model. Our proposed model not only improves the segmentation performance of breast ultrasound tumors, but also reduces the model parameters and computational resource consumption time. The model was evaluated on the breast ultrasound image dataset and our collected dataset. The experiments show that the SC-Unext model achieved Dice scores of 75.29% and accuracy of 97.09% on the BUSI dataset, and on the collected dataset, it reached Dice scores of 90.62% and accuracy of 98.37%. Meanwhile, we conducted a comparison of the model's inference speed on CPUs to verify its efficiency in resource-constrained environments. The results indicated that the SC-Unext model achieved an inference speed of 92.72ms per instance on devices equipped only with CPUs. The model's number of parameters and computational resource consumption are 1.46M and 2.13 GFlops, respectively, which are lower compared to other network models. Due to its lightweight nature, the model holds significant value for various practical applications in the medical field.

Bayesian Optimization with Experience for Fast Development of Monolithic Tandem Solar Cells: Simulation Case Study

AbstractMachine learning (ML) is gaining more attention in photovoltaic research and will be a vital tool in reaching record‐high power conversion efficiencies (PCE) in the near future. One area, where ML is significantly beneficial is reducing the number of experiments needed to find the optimum combination of parameters in solar cell fabrication. Bayesian optimization (BO) provides routes for quickly identifying optimum parameters in problems with large parameter space. In this work, BO algorithms utilizing previous knowledge are demonstrated to result in faster optimization of tandem solar cells, a technology rapidly gaining more interest due to its potential to deliver record‐high performance. Namely, it is shown that in the space of all possible parameter combinations that take ≈88 years to evaluate, optimum PCE can be obtained in 20 min. Moreover, it is demonstrated that methods utilizing previous knowledge outperform those that do not by yielding an increase of ≈8.9%abs. in the 1st iteration and requiring 5× less time to reach a target PCE of 38.5% across 20 different trials. Results from this work help accelerate the development of tandem solar cells by removing the need for large numbers of experiments in identifying optimum parameters.

Effect of temperature and magnetic field induced hysteresis on reversibility of magnetocaloric effect and its minimization by optimizing the geometrical compatibility condition in Mn–Ni–Fe–Si alloy

The advancement of magnetic materials with coupled magneto-structural phase transition (MST) to fulfill the ultimate objectives of practical solid-state cooling applications requires a better understanding of the hysteresis phenomenon linked across the phase transition region along with the large magnetocaloric parameters. For the present sample Mn0.65Ni0.65Fe0.70Si, the MST is associated with a sharp jump in magnetization along with a small thermal hysteresis of ∼13 K. A giant isothermal magnetic entropy change (|ΔSMmax|) of ∼37.6 J kg−1 K−1 at 299 K and effective refrigerant capacity (RCeffe) of ∼214.3 J kg−1 under ΔH = 30 kOe is obtained with excellent compatibility between the martensite and austenite phases. The geometrical compatibility condition, i.e., very small (∼0.55%) deviation of the middle eigenvalue (λ2) from unity justifies the observation of small hysteresis in the present material. The investigation of hysteresis behavior under different extents of the driving forces (temperature or magnetic field) reveals that both the driving forces trigger equally the phase transition and are responsible equivalently for the hysteresis phenomenon. The present study provides a pathway to understand the complexity of the hysteresis behavior, its impact on the reversibility of magnetocaloric effect, and its minimization by optimizing the geometrical compatibility condition between the austenite and martensite phases.

Minimal configuration point cloud odometry and mapping

Simultaneous Localization and Mapping (SLAM) refers to the common requirement for autonomous platforms to estimate their pose and map their surroundings. There are many robust and real-time methods available for solving the SLAM problem. Most are divided into a front-end, which performs incremental pose estimation, and a back-end, which smooths and corrects the results. A low-drift front-end odometry solution is needed for robust and accurate back-end performance. Front-end methods employ various techniques, such as point cloud-to-point cloud (PC2PC) registration, key feature extraction and matching, and deep learning-based approaches. The front-end algorithms have become increasingly complex in the search for low-drift solutions and many now have large configuration parameter sets. It is desirable that the front-end algorithm should be inherently robust so that it does not need to be tuned by several, perhaps many, configuration parameters to achieve low drift in various environments. To address this issue, we propose Simple Mapping and Localization Estimation (SiMpLE), a front-end LiDAR-only odometry method that requires five low-sensitivity configurable parameters. SiMpLE is a scan-to-map point cloud registration algorithm that is straightforward to understand, configure, and implement. We evaluate SiMpLE using the KITTI, MulRan, UrbanNav, and a dataset created at the University of Queensland. SiMpLE performs among the top-ranked algorithms in the KITTI dataset and outperformed all prominent open-source approaches in the MulRan dataset whilst having the smallest configuration set. The UQ dataset also demonstrated accurate odometry with low-density point clouds using Velodyne VLP-16 and Livox Horizon LiDARs. SiMpLE is a front-end odometry solution that can be integrated with other sensing modalities and pose graph-based back-end methods for increased accuracy and long-term mapping. The lightweight and portable code for SiMpLE is available at: https://github.com/vb44/SiMpLE.

Merger estimates for a disformal Kerr black hole in quadratic degenerate higher-order scalar-tensor theories* *Supported by the National Key Research and Development Program of China (2020YFC2201400), and the National Natural Science Foundation of China (12275079, 12035005, 12275078)

We investigate the main features of a disformal Kerr black hole merger in quadratic degenerate higher-order scalar-tensor theories. In the ringdown stage of the black hole merger, for the prograde orbit, the real part of the quasinormal modes decreases with an increase in the disformal parameter, and the imaginary part also decreases, except in the Kerr case for a large spin parameter. However, for the retrograde orbit, the real part increases with an increase in the disformal parameter, and the imaginary part always decreases with it. For the approximate final spin, regardless of an equal spin, unequal spin, or generic spin configuration merger, the final black hole spin always increases with an increase in the disformal parameter. Our results show that the disformal parameter in the disformal Kerr solution and the MOG parameter in the Kerr-MOG case have obviously different effects on the black hole merger, which suggests the differences between these two spacetime structures.

Nanopatterned Monolayers of Bioinspired, Sequence-Defined Polypeptoid Brushes for Semiconductor/Bio Interfaces.

The ability to control and manipulate semiconductor/bio interfaces is essential to enable biological nanofabrication pathways and bioelectronic devices. Traditional surface functionalization methods, such as self-assembled monolayers (SAMs), provide limited customization for these interfaces. Polymer brushes offer a wider range of chemistries, but choices that maintain compatibility with both lithographic patterning and biological systems are scarce. Here, we developed a class of bioinspired, sequence-defined polymers, i.e., polypeptoids, as tailored polymer brushes for surface modification of semiconductor substrates. Polypeptoids featuring a terminal hydroxyl (-OH) group are designed and synthesized for efficient melt grafting onto the native oxide layer of Si substrates, forming ultrathin (∼1 nm) monolayers. By programming monomer chemistry, our polypeptoid brush platform offers versatile surface modification, including adjustments to surface energy, passivation, preferential biomolecule attachment, and specific biomolecule binding. Importantly, the polypeptoid brush monolayers remain compatible with electron-beam lithographic patterning and retain their chemical characteristics even under harsh lithographic conditions. Electron-beam lithography is used over polypeptoid brushes to generate highly precise, binary nanoscale patterns with localized functionality for the selective immobilization (or passivation) of biomacromolecules, such as DNA origami or streptavidin, onto addressable arrays. This surface modification strategy with bioinspired, sequence-defined polypeptoid brushes enables monomer-level control over surface properties with a large parameter space of monomer chemistry and sequence and therefore is a highly versatile platform to precisely engineer semiconductor/bio interfaces for bioelectronics applications.

Asymptotically‐Flat Black Hole Solutions in Symmergent Gravity

AbstractSymmergent gravity is an emergent gravity model with an curvature sector and an extended particle sector having new particles beyond the known ones. With constant scalar curvature, asymptotically flat black hole solutions are known to have no sensitivity to the quadratic curvature term (coefficient of ). With variable scalar curvature, however, asymptotically‐flat symmergent black hole solutions turn out to explicitly depend on the quadratic curvature term. In the present work, asymptotically‐flat symmergent black holes with variable scalar curvature are constructed and used its evaporation, shadow, and deflection angle to constrain the symmergent gravity parameters. Concerning their evaporation, new particles predicted by symmergent gravity are found, even if they do not interact with the known particles, can enhance the black hole evaporation rate. Concerning their shadow, statistically significant symmergent effects are reached at the level for the observational data of the Event Horizon Telescope (EHT) on the Sagittarius A* supermassive black hole are shown. Concerning their weak deflection angle, discernible features for the boson‐fermion number differences are revealed, particularly at large impact parameters. These findings hold the potential to serve as theoretical predictions for future observations and investigations on black hole properties.

Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data

Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

Constructing new high-order polynomial chaotic maps and application in pseudorandom number generator

The chaotic map have been widely applied in fields such as pseudorandom number generation(PRNG) and image encryption due to its excellent chaotic performance. In this paper, two classes of high-order polynomial chaotic maps of special form are proposed based on the Li-Yorke theorem. The dynamic behavior of the proposed maps is numerically analyzed, including bifurcation and Lyapunov exponent, and the analysis results prove the validity of the proposed conclusions. The proposed polynomial chaotic maps have a larger parameters and chaotic range, as well as a more stable Lyapunov exponent. Furthermore, based on the coupling chaotic systems, we design a pseudorandom number generator(PRNG), and the number of chaos parameters are expanded through the coupling control parameters in a PRNG. Then the performance of the pseudorandom sequence generated by the PRNG is tested and analysed. The test and analysis results show that the pseudorandom sequence has favorabble security, structural complexity, and randomness. Especially, the information entropy of 7.9998 and the key space size of 2208 exceed the recently reported pseudorandom number generators(PRNGs). In comparison with other PRNGs based on chaotic maps in the recent literature, this paper provides comprehensive performance test and analysis of the proposed PRNG and demonstrates its potential for cryptographic applications.

Nonlinear Dynamics of a New Class of Micro-Electromechanical Oscillators—Open Problems

In this paper, we propose a new class of micro-electromechanical oscillators. Some investigations based on Melnikov’s approach are applied for identifying some chaotic possibilities. We demonstrate also some specialized modules for investigating the dynamics of these oscillators. This will be included as an integral part of a planned much more general Web-based application for scientific computing. It turns out that the theoretical apparatus for studying the circuit implementation (design, fabricating, etc.) of the considered differential model for large parameter values is extremely complex and requires a serious investigation. This is the reason to offer this model to the attention of specialists working in this scientific direction. Some open problems related to the use of existing computer algebraic systems for the study of this class of oscillators for large values of n,m and N are also posed. In general, the entire article is subordinated to this frank conversation with the readers with the sole purpose being the professional upgrading of the specialized modules provided for this purpose in subsequent licensed versions of CAS.