Fine Step Research Articles

Efficient approximate Bayesian inference for quantifying uncertainty in multiscale animal movement models

It is becoming increasingly important for wildlife managers and conservation ecologists to understand which resources are selected or avoided by an animal and how to best predict future spatial distributions of animal populations in the long term. However, inferring the patterns of space use by animals is a challenging multiscale inference problem, and formal uncertainty quantification of parameter estimates is an essential component of models that provide useful predictions across scales. In this study, we develop an approximate Bayesian inference framework for step selection models of animal movement which quantifies the uncertainty in estimates of resource selection and avoidance parameters within the Bayesian paradigm. The framework allows joint inference of movement and resource selection parameters of animals and is multiscale in that parameters inferred from fine scale movement steps scale to produce predictions of long-term patterns of space use. Our analysis focuses on simulated movement data in which we test the performance of our framework by altering movement parameters in the data-generating process. In our simulations, individuals respond to two environmental covariates and we employ all combinations of positive and negative selection coefficients corresponding to attraction to an environmental feature and avoidance of an environmental feature, respectively. In all scenarios, we recover the movement parameters used for the simulation of synthetic movement data using variational inference, an approximate Bayesian method, allowing us to formally quantify the uncertainty associated with each parameter for varying data set sizes. Our framework successfully recovered all combinations of movement parameters of the simulated data and accurately captured their posterior distributions given the available data suggesting that the framework is reliable and suitable for inferring how animals select resources and move on a landscape.Notably, our analysis shows that even for reasonably large data sets (circa 10,000 observations) there can still be considerable uncertainty associated with resource selection parameters which can in turn lead to inaccurate predictions of long term space use if not properly incorporated into the modelling approach. To further illustrate the utility of our approach, we also present a case study of its application to an example data set consisting of GPS locations of a fisher (Martes pennanti) Our approach will be of interest to ecologists looking to address conservation questions such as when and where animals are likely to spend most of their time. Furthermore, the approach could be used to predict new suitable areas for conservation based on how GPS collared animal use or avoid resources while including uncertainty around the predictions, thereby helping to make informed management decisions.

An evaluation of CNN models and data augmentation techniques in hierarchical localization of mobile robots

This work presents an evaluation of CNN models and data augmentation to carry out the hierarchical localization of a mobile robot by using omnidirectional images. In this sense, an ablation study of different state-of-the-art CNN models used as backbone is presented and a variety of data augmentation visual effects are proposed for addressing the visual localization of the robot. The proposed method is based on the adaption and re-training of a CNN with a dual purpose: (1) to perform a rough localization step in which the model is used to predict the room from which an image was captured, and (2) to address the fine localization step, which consists in retrieving the most similar image of the visual map among those contained in the previously predicted room by means of a pairwise comparison between descriptors obtained from an intermediate layer of the CNN. In this sense, we evaluate the impact of different state-of-the-art CNN models such as ConvNeXt for addressing the proposed localization. Finally, a variety of data augmentation visual effects are separately employed for training the model and their impact is assessed. The performance of the resulting CNNs is evaluated under real operation conditions, including changes in the lighting conditions. Our code is publicly available on the project website https://github.com/juanjo-cabrera/IndoorLocalizationSingleCNN.git.

Actinium isotope cross sections for 226Ra(p,xn) reactions measured at low energies

Cross sections for production of the medical isotope 225Ac by the 226Ra(p,2n) reaction have not previously been measured in fine steps over the relevant energy region, and no measurements are presently available in the literature for the actinium contaminant isotopes created by the adjacent 226Ra(p,n)226Ac and 226Ra(p,3n)224Ac reactions. We report thin-target cross-section measurements for production of 224Ac and 225Ac by protons of 15.1–16.8 MeV incident on radium. An upper limit for the 226Ac cross section is also reported.

Optimum Parameters in Ultrasound Coherent Plane Wave Compounding for High LPWV Estimation: Validation on Phantom and Feasibility in 10 Subjects.

The aim of this study is to determine the optimum and fine values of the number and transmission angles of tilted plane waves for coherent plane-wave compounding (CPWC)-based high local pulse wave velocity (LPWV) estimation. A Verasonics system incorporating a linear array probe L14-5/38 with 128 elements and a pulsatile pump, CompuFlow1000, were used to acquire radio frequency data of 3, 5, 7, and 9 tilted plane wave sequences with angle intervals from 0° to 12° with a coarse interval increment step of 1°, and the angle intervals from 0° to 2° with a fine interval increment step of 0.25° from a carotid vessel phantom with the LPWV of 13.42 ± 0.90 m/s. The mean value, standard deviation, and coefficients of variation (CV) of the estimated LPWVs were calculated to quantitatively assess the performance of different configurations for CPWC-based LPWV estimation. Ten healthy human subjects of two age groups were recruited to assess the in vivo feasibility of the optimum parameter values. The CPWC technique with three plane waves (PRF of 12 kHz corresponding to a frame rate of 4000 Hz) with an interval of 0.75° had LPWVs of 13.52 ± 0.08 m/s with the lowest CV of 1.84% on the phantom, and 5.49 ± 1.46 m/s with the lowest CV of 12.35% on 10 subjects. The optimum parameters determined in this study show the best repeatability of the LPWV measurements with a vessel phantom and 10 healthy subjects, which support further studies on larger datasets for potential applications.

A high-order multi-time-step scheme for bond-based peridynamics

A high-order multi-time-step scheme (MTS) for the bond-based peridynamic (PD) model, an extension of classical continuous mechanics widely used for analyzing discontinuous problems like cracks, is proposed. The MTS scheme discretizes the spatial domain with a meshfree method and advances in time with a high-order Runge–Kutta method. To effectively handle discontinuities (cracks) that appear in a local subdomain in the solution, the scheme employs the Taylor expansion and Lagrange interpolation polynomials with a finer time step size, that is, coarse and fine time step sizes for smooth and discontinuous subdomains, respectively, to achieve accurate and efficient simulations. By eliminating unnecessary fine-scale resolution imposed on the entire domain, the MTS scheme outperforms the standard STS scheme for PD by significantly reducing computational costs, particularly for problems with discontinuous solutions, as demonstrated by comprehensive theoretical analysis and numerical experiments.

SkipDiff: Adaptive Skip Diffusion Model for High-Fidelity Perceptual Image Super-resolution

It is well-known that image quality assessment usually meets with the problem of perception-distortion (p-d) tradeoff. The existing deep image super-resolution (SR) methods either focus on high fidelity with pixel-level objectives or high perception with generative models. The emergence of diffusion model paves a fresh way for image restoration, which has the potential to offer a brand-new solution for p-d trade-off. We experimentally observed that the perceptual quality and distortion change in an opposite direction with the increase of sampling steps. In light of this property, we propose an adaptive skip diffusion model (SkipDiff), which aims to achieve high-fidelity perceptual image SR with fewer sampling steps. Specifically, it decouples the sampling procedure into coarse skip approximation and fine skip refinement stages. A coarse-grained skip diffusion is first performed as a high-fidelity prior to obtaining a latent approximation of the full diffusion. Then, a fine-grained skip diffusion is followed to further refine the latent sample for promoting perception, where the fine time steps are adaptively learned by deep reinforcement learning. Meanwhile, this approach also enables faster sampling of diffusion model through skipping the intermediate denoising process to shorten the effective steps of the computation. Extensive experimental results show that our SkipDiff achieves superior perceptual quality with plausible reconstruction accuracy and a faster sampling speed.

Floquet Engineering of Excitons in Two-Dimensional Halide Perovskites via Biexciton States.

Layered two-dimensional halide perovskites (2DHPs) exhibit exciting non-equilibrium properties that allow the manipulation of energy levels through coherent light-matter interactions. Under the Floquet picture, novel quantum states manifest through the optical Stark effect (OSE) following intense subresonant photoexcitation. Nevertheless, a detailed understanding of the influence of strong many-body interactions between excitons on the OSE in 2DHPs remains unclear. Herein, we uncover the crucial role of biexcitons in photon-dressed states and demonstrate precise optical control of the excitonic states via the biexcitonic OSE in 2DHPs. With fine step tuning of the driven energy, we fully parametrize the evolution of exciton resonance modulation. The biexcitonic OSE enables Floquet engineering of the exciton resonance with either a blue-shift or a red-shift of the energy levels. Our findings shed new light on the intricate nature of coherent light-matter interactions in 2DHPs and extend the degree of freedom for ultrafast coherent optical control over excitonic states.

A computational approach to predict and enhance the sensitivity of X-ray resonant magnetic reflectometry to the magnetic behavior of deeply buried interfaces

In the present work a computational approach is applied to model and predict the results of X-ray resonant magnetic reflectometry – a non-destructive synchrotron-based technique to probe chemical composition, crystallographic environment and magnetization in multilayer epitaxial heterostructures with nanoscale depth resolution. The discussed 2D mapping approach is a step forward with respect to conventional resonant X-ray reflectometry and consists of collecting a fine step array of reflected intensity as a function of grazing angle and photon energy across the absorption edge of a particular chemical element. With the use of circularly polarized photons the method can be extended to magnetic systems to produce a map of dichroic reflectance directly related to the magnetization profile of the heterostructure. Studying the magnetic field dependence of dichroic reflectance maps can provide valuable information on the magnetization reversal of individual sublayers of a multilayer heterostructure. In the present paper modeling is performed for a bilayer system mimicking the behavior of a 30 nm ɛ-Fe2O3 thin film that is known to exhibit a pronounced two-component magnetic hysteresis. A technique to find optimal energy/angle combinations in order to sense magnetization of individual sublayers is proposed. Also discussed is the advantage of heavy-element capping, which leads to a substantial increase of the dichroic intensity oscillation contrast in the pre-edge region where the sensitivity to the magnetic behavior of the deeply buried interfaces is most pronounced.

Development of a Critical Edge-Based Adaptive Toolpath Strategy to Improve Geometrical Accuracy of Incrementally Formed Titanium Implants

Incremental sheet forming has become increasingly popular due to its advantages over traditional metal forming processes, including flexibility, low setup cost, and enhanced formability. It holds great potential for manufacturing asymmetrical components, particularly biomedical implants like cranio-facial implants. However, a significant drawback of this technique is a lack of geometrical accuracy in the produced parts. This research aims to enhance the geometric accuracy of a patient-specific frontal cranial implant by introducing a novel tool path strategy called the adaptive tool path strategy (ATP). The ATP dynamically adjusts step sizes by selectively removing slices based on local curvature requirements. The proposed strategy begins with slicing a CAD model at an extremely fine step depth (0.01 mm) to generate the initial dataset. Subsequently, slices are strategically eliminated to conform to desired curvature specifications. The manufacturing process utilizes commercially pure titanium grade 2 alloy, which is incrementally formed into the desired implant shape using both the traditional constant step depth method and the new adaptive tool path strategy. The produced parts are then scanned using high resolution 3D scanners and a detailed comparison is made between the final geometries and the design geometry. The assessment includes evaluating the forming depth, analysing bending due to spring back, and measuring geometric deviations. Furthermore, numerical simulations are conducted to assess the estimation capabilities of finite element analysis in predicting the forming process. The results demonstrate that the ATP tool path strategy significantly reduces spring back, indicated by a nearly 30 % decrease in the angle of bend along the critical edge. Moreover, the root mean square deviation is reduced by 11 %. Importantly, the experimental results aligned with the patterns observed in the numerical simulations, validating the findings obtained through the study.

A Multi-Level Auto-Adaptive Noise-Filtering Algorithm for Land ICESat-2 Photon-Counting Data

Due to atmospheric scattering, solar radiation, and other factors, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) product data suffer from a substantial amount of background noise. This poses a significant challenge when attempting to directly utilize the raw data. Consequently, data denoising becomes an indispensable preprocessing step for its subsequent applications, such as the extraction of forest structure parameters and ground elevation data. While the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is currently the most widely used method, it remains susceptible to complexities arising from terrain, low signal-to-noise ratio (SNR), and input parameter variations. This paper proposes an efficient Multi-Level Auto-Adaptive Noise Filter (MLANF) algorithm based on photon spatial density. Its purpose is to extract signal photons from ICESat-2 terrestrial data of different ground cover types. The algorithm follows a two-step process. Firstly, random noise photons are removed from the upper and lower regions of the signal photons through a coarse denoising process. Secondly, in the fine denoising step, the K-Nearest Neighbor (KNN) algorithm selects the K photons to calculate the slope along the track. The calculated slope is then used to rotate the direction of the searching neighborhood in the DBSCAN algorithm. The proposed algorithm was tested in eight datasets of four surface types: forest, grassland, desert, and urban, and the extraction results were compared with those from the ATL08 datasets and the DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the classification precision, recall, and F-score of our algorithm, as well as two other algorithms, were calculated. The MLANF could achieve a good balance between classification precision (97.48% averaged) and recall (97.96% averaged). Its F-score (97.69% averaged) was higher than that of the other two methods. This demonstrates that the MLANF algorithm successfully obtained a continuous surface profile from ICESat-2 datasets with different surface cover types, significant topographic relief, and low SNR.

Optimal artificial neural network configurations for hourly solar irradiation estimation

<span lang="EN-US">Solar energy is widely used in order to generate clean electric energy. However, due to its intermittent nature, this resource is only inserted in a limited way within the electrical networks. To increase the share of solar energy in the energy balance and allow better management of its production, it is necessary to know precisely the available solar potential at a fine time step to take into account all these stochastic variations. In this paper, a comparison between different artificial neural network (ANN) configurations is elaborated to estimate the hourly solar irradiation. An investigation of the optimal neurons and layers is investigated. To this end, feedforward neural network, cascade forward neural network and fitting neural network have been applied for this purpose. In this context, we have used different meteorological parameters to estimate the hourly global solar irirradiation in the region of Laghouat, Algeria. The validation process shows that choosing the cascade forward neural network two inputs gives an R2 value equal to 97.24% and an normalized root mean square error (NRMSE) equals to 0.1678 compared to the results of three inputs, which gives an R2 value equaled to 95.54% and an NRMSE equals to 0.2252. The comparison between different existing methods in literature show the goodness of the proposed models.</span>

A 540-mA, 0.0487-ps-FOM Digital LDO based on Bi-directional Binary Search with Dual-loop Control

A dual-loop digital low-dropout regulator based on the bi-directional binary search method is implemented in a 0.18-μm CMOS process in this paper. To improve the transient response, a bi-directional successive approximation register without initialization is proposed in coarse loop. To speed up fine adjustment during heavy loads, a two-dimensional MOSFET array is adopted to improve fine regulation step. Simulation results demonstrate that, during load current steps between 540 mA and 40 mA, the undershoot/overshoot is 364 mV/161 mV, and the digital LDO settles within 210 ns. For a supply voltage of 1.4 V with 200 mV dropout, the current efficiency is as high as 99.99% and the figure of merit is as low as 0.0487 ps.

SAR Image Reconstruction via Incremental Imaging With Compressive Sensing

For synthetic aperture radar (SAR) imaging, the irregular loss of received data and the non-uniformly under sampling yield the SAR azimuth ambiguity (SAA) resulting in the degradation in image quality. To address this issue, the incremental SAR imaging approach based on the estimation of sensing dictionary matrix in the pursuit of sparsity is presented in this paper. Several beneficial contributions are included in the proposed method. First, the SAA reduction achievable in the proposed method is considerably improved more than that of the conventional compressive sensing (CS) based approach in terms of the image quality and computational efficiency. Second, we established the signal parameterization scheme which is divided into coarse and fine search steps to estimate the sensing matrix for SAR image restoration via signal model reconstruction. Third, an incremental imaging approach is devised to overcome the drawback of the conventional CS-based approach which is not sufficiently good leading to limited SAA reduction performance under the non-sparse SAR image. These contributions are verified using numerical simulations and experimental results.

Performance Limit of Fiber-Longitudinal Power Profile Estimation Methods

This paper presents analytical results on longitudinal power profile estimation (PPE) methods, which visualize signal power evolution in optical fibers at a coherent receiver. The PPE can be formulated as an inverse problem of the nonlinear Schr\"odinger equation, where the nonlinear coefficient (and thus signal power) is reconstructed from boundary conditions, i.e., transmitted and received signals. Two types of PPE methods are reviewed and analyzed, including correlation-based methods (CMs) and minimum-mean-square-error-based methods (MMSEs). The analytical expressions for their output power profiles and spatial resolution are provided, and thus the theoretical performance limits of the two PPE methods and their differences are clarified. The derived equations indicate that the estimated power profiles of CMs can be understood as the convolution of a true power profile and a smoothing function. Consequently, the spatial resolution and measurement accuracy of CMs are limited, even under noiseless and distortionless conditions. Closed-form formulas for the spatial resolution of CMs are shown to be inversely proportional to the product of a chromatic dispersion coefficient and the square of signal bandwidth. With MMSEs, such a convolution effect is canceled out and the estimated power profiles approach a true power profile under a fine spatial step size.

CryoFIB milling large tissue samples for cryo-electron tomography

Cryo-electron tomography (cryoET) is a powerful tool for exploring the molecular structure of large organisms. However, technical challenges still limit cryoET applications on large samples. In particular, localization and cutting out objects of interest from a large tissue sample are still difficult steps. In this study, we report a sample thinning strategy and workflow for tissue samples based on cryo-focused ion beam (cryoFIB) milling. This workflow provides a full solution for isolating objects of interest by starting from a millimeter-sized tissue sample and ending with hundred-nanometer-thin lamellae. The workflow involves sample fixation, pre-sectioning, a two-step milling strategy, and localization of the object of interest using cellular secondary electron imaging (CSEI). The milling strategy consists of two steps, a coarse milling step to improve the milling efficiency, followed by a fine milling step. The two-step milling creates a furrow–ridge structure with an additional conductive Pt layer to reduce the beam-induced charging issue. CSEI is highlighted in the workflow, which provides on-the-fly localization during cryoFIB milling. Tests of the complete workflow were conducted to demonstrate the high efficiency and high feasibility of the proposed method.

A 23–40-GHz Phased-Array Receiver Using 14-Bit Phase-Gain Manager and Wideband Noise-Canceling LNA

This article presents a 23–40-GHz phased-array receiver (RX) capable of reconfiguring to achieve fine phase step and high gain range. Such phased-array RX consists of a 14-bit phase-gain manager and a wideband noise-canceling low-noise amplifier (NCLNA). The proposed phase-gain manager includes two 7-bit variable gain amplifiers (VGAs) in radio-frequency (RF) domain, two pairs of mixers, and two variable gain sign-map (VGSM) circuits in intermediate-frequency (IF) domain. The proposed phased-array RX can not only provide a maximum 14-bit phase-tuning operation and >35-dB gain variation range but also achieve an improved calibration-free image rejection ratio (IRR) by introducing the dual-mode image rejection operation. Based on the aforementioned structure, a four-element phased-array RX is implemented and fabricated in a 40-nm CMOS technology. The whole circuit size is 2.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 1.3 mm, while consuming 52 mW per element. The measured result shows a state-of-the-art minimal noise figure (NF) of 3.8 dB, which supports 3 Gb/s, 64-QAM and 2.4 Gb/s, 256-QAM modulation signal.

Selecting Burnup Algorithms in OpenMC Using the Calculated Benchmark of LEU Assembly and MOX Fuel

OpenMC is a state-of-the-art Monte Carlo neutron transport simulation code that uses the Python programming language as an API. OpenMC supports eight burnout simulation algorithms. This study presents the results of choosing an integration method for modeling the burnup of fuel assemblies with burnable poisons for WWER-1000 reactors. Burnout simulation results from OpenMC were compared with those reported in the OECD benchmark. 8 different numerical integrators can be used to model burnout in OpenMC code: PI, CE/CM, LE/QI, CE/LI, CF4, EPC-RK4, SI-CE/LI, SI-LE/QI. The test results showed that the SI-CE/LI, SI-LE/QI integrators require significantly more time to calculate one burnup step than the others with the same accuracy, so they were excluded from further consideration. The PI integrator showed low integration accuracy at the same burnup steps with other integrators. However, PI has a high performance compared to other integrators, and as the integration step decreases, it converges to one solution, which can be chosen as a reference for assessing the quality of other integrators. Based on the results obtained using the fine step PI integrator, it was decided to use the CE/LI integrator for further work. The results obtained with CE/LI were compared with those obtained with the VVER-1000 LEU and MOX benchmark for codes: MCU, TVS-M, WIMS8A, HELIOS, MULTICELL and showed good agreement. Thus, we can conclude the applicability of the CE/LI integrator as part of OpenMC for modeling the burnup of fuel assemblies containing burnable poisons. During the work, the resources of the high-performance computer center of the National Research Nuclear University MEPhI were used.

Design of a Compact Planar Magnetic Levitation System with Wrench–Current Decoupling Enhancement

Magnetic levitation technology has promising applications in modern manufacturing, especially for fine-motion stage and long-range omnidirectional planar motors. This paper presents the development of a compact planar maglev prototype with the potential to achieve both applications to increase flexibility for the manufacturing system. The planar stator is designed by using optimized square coils arranged in the zigzag configuration, which provides a better uniform magnetic flux density compared with another configuration. The stator is a compact and portable module with built-in current amplifier units. The single-disc magnet mover is deployed with five controllable degrees of freedom. The cross-coupling effect is decoupled by a precomputed Lorentz force based wrench—current transformation matrix stored in the lookup table. A 2-D linear interpolation is implemented to enhance decoupling effectiveness which is offered via discrete lookup data. Experiments with motion-tracking cameras and a basic controller demonstrate the results of fine step motion of 10 and 20 µm and rotation steps of 0.5 and 1.0 mrad. The potential for multidirectional material handling is represented by a total horizontal translation range of 20 mm by 20 mm with a maximum air gap of 26 mm and a total rotation range of 20 degrees for both roll and pitch.

Convergence analysis of single rate and multirate fixed stress split iterative coupling schemes in heterogeneous poroelastic media

AbstractRecently, the accurate modeling of flow‐structure interactions has gained more attention and importance for both petroleum and environmental engineering applications. Of particular interest is the coupling between subsurface flow and reservoir geomechanics. Different single rate and multirate iterative and explicit coupling schemes have been proposed and analyzed in the past. In addition, Banach fixed point contraction results were obtained for iterative coupling schemes, and conditionally stable results were obtained for explicit coupling schemes. In this work, we will consider the mathematical analysis of the single rate and multirate fixed stress split iterative coupling schemes for spatially heterogeneous poroelastic media. We will re‐establish the contractivity for both schemes in the localized case, and we will show that heterogeneities come at the expense of imposing more restricted conditions on the number of fine flow time steps that can be taken within one coarse mechanics time step in the multirate case. Our mathematical analysis is supplemented by numerical simulations validating our derived upper bounds. To the best of our knowledge, this is the first rigorous mathematical analysis of the multirate fixed‐stress split iterative coupling scheme in heterogeneous poroelastic media.

Photon echoes using atomic frequency combs in Pr:YSO - experiment and semiclassical theory.

Photon echoes in rare-earth-doped crystals are studied to understand the challenges of making broadband quantum memories using the atomic frequency comb (AFC) protocol in systems with hyperfine structure. The hyperfine structure of Pr3+ poses an obstacle to this goal because frequencies associated with the hyperfine transitions change the simple picture of modulation at an externally imposed frequency. The current work focuses on the intermediate case where the hyperfine spacing is comparable to the comb spacing, a challenging regime that has recently been considered. Operating in this regime may facilitate storing quantum information over a larger spectral range in such systems. In this work, we prepare broadband AFCs using optical combs with tooth spacings ranging from 1 MHz to 16 MHz in fine steps, and measure transmission spectra and photon echoes for each. We predict the spectra and echoes theoretically using the optical combs as input to either a rate equation code or a density matrix code, which calculates the redistribution of populations. We then use the redistributed populations as input to a semiclassical theory using the frequency-dependent dielectric function. The two sets of predictions each give a good, but different account of the photon echoes.