High Spatial Resolution Research Articles

Feasibility of thin-slice, low noise images created using multi-kernel synthesis to replace multiple image series in head CT.

SynthesiZed Improved Resolution and Concurrent nOise reductioN (ZIRCON) is a multi-kernel synthesis method that creates a single series of thin-slice computed tomography (CT) images displaying low noise and high spatial resolution, increasing reader efficiency and minimizing partial volume averaging. To compare the diagnostic performance of a single set of ZIRCON images to two routine clinical image series using conventional CT head and bone reconstruction kernels for diagnosing intracranial findings and fractures in patients with trauma or suspected acute neurologic deficit. In total, 50 patients underwent clinically indicated head CT in the ER (15 normal, 35 abnormal cases). A non-reader neuroradiologist established the reference standard. Three neuroradiologists reviewed two routine clinical series (head and bone kernels) and a single ZIRCON series, detecting intracranial findings or fractures and rating confidence (0-100). Sensitivity, specificity, and jackknife free-response receiver operating characteristic (JAFROC) figure of merit (FOM) were compared (limit of non-inferiority: -0.10). ZIRCON and conventional images demonstrated comparable performance for fractures (sensitivity: 51.5% vs. 54.5%; specificity: 40.2% vs. 34.2%) and intracranial findings (sensitivity: 88.2% vs. 91.4%; specificity: 77.2% vs. 73.7%).The estimated difference of JAFROC FOM demonstrated ZIRCON non-inferiority for acute pathologies overall (0.003 [95% CI=-0.051-0.057]) and fractures (0.048 [95% CI=-0.050-0.145]) but not for intracranial findings alone (-0.024 [95% CI=-0.100-0.052]). Thin-slice, low noise, and high spatial resolution images can be created to display intracranial findings and fractures replacing multiple images series in head CT with similar performance. Future studies in more patients and further algorithmic development are warranted.

Assessing field scale spatiotemporal heterogeneity in salinity dynamics using aerial data assimilation

Soil moisture and salinity shifts within the root zone can significantly alter crop yields. Thus, spatiotemporal dynamics of these parameters are essential for precision crop management. Airborne or spaceborne earth observation methods based on vegetation and soil observations have sometimes been used with limited success to indirectly understand parameters like soil salinity. These datasets lack spatiotemporal resolution to discern field-scale heterogeneities, and estimates’ accuracy is poor. A Metropolis-Hasting Markov Chain-Monte Carlo (MCMC) based method was developed to estimate field-scale soil salinity by assimilating estimated evapotranspiration (ET) data obtained from aerial canopy temperature sensing with ET outputs from a one-dimensional soil-water transport model. By aligning the two estimated ET values, we inferred anticipated soil salinity levels in a mature pecan orchard (28,951 m2). Our results aligned closely with in-situ measurements with a spatial cross-correlation more than 0.86 and highlighted the expected heterogeneities and nonlinearities. This research offers an approach to refine the current state-of-the-art crop models by accounting for field scale heterogeneities using remotely sensed data. This assimilation method will pave the way for a more inclusive agricultural system modeling that can infer critical but hard-to-measure soil properties from easier-to-obtain remotely sensed datasets. Though this paper concentrates on aerial observations, we anticipate similar methods can be used for satellite-based imagery, especially those with high spatial, temporal, and spectral resolutions.

Evaluation of the Representation of Raindrop Self‐Collection and Breakup in Two‐Moment Bulk Models Using a Multifrequency Radar Retrieval

AbstractUsing multifrequency radar observations providing raindrop size distribution evolution with high spatial and temporal resolution, this study aims to assess the ability of different parameterizations of raindrop self‐collection and breakup processes applied in mesoscale models, to reproduce the statistics derived from observations. The stratiform zones of two types of precipitating systems are studied, a frontal situation that occurred over Finland in June 2014 and a squall line system observed over Oklahoma in June 2011. An analysis method for determining raindrop trajectories was used to obtain the temporal variation of the total raindrop concentration from the observations. The resulting raindrop concentration rate as a function of the mean volume diameter reveals significant differences with the parameterizations currently used in two‐moment bulk microphysics schemes. These results show that even if they produce variations in raindrop concentration of the same order of magnitude as the observations, the current parameterizations diverge from the median of the observations, resulting in an overestimation of either the self‐collection or the breakup process. From the median of radar observations, new parameterizations of the self‐collection and breakup processes and of rain self‐collection efficiency are developed and can be implemented in two‐moment bulk microphysics schemes.

A harmonized data set of ruminant livestock presence and grazing data for the European Union and neighbouring countries

Improving the sustainability of the European livestock sector requires recent data at high spatial resolutions. Only then can we analyse potential negative impacts of livestock related to ecosystem degradation, and positive ones such as preserving cultural landscapes through grazing. Data on livestock numbers in Europe are provided by the European statistical office but at a coarse spatial resolution aggregated to statistical regions like NUTS2 or even coarser. While data on a more detailed level, such as local administrative areas, are available from individual national statistical offices, a collection of harmonized data has not been available until now, limiting the use of livestock data in environmental and agricultural studies. We collected data from all European Union Member States and neighbouring countries, resulting in the most up to date and spatially explicit dataset on livestock numbers publicly available to all researchers. We provide data on livestock numbers for more than 70,000 administrative units in 43 countries and territories. In addition, we provide data on the share of cattle that are grazing.

Two-point statistics in non-homogeneous rotating turbulence

Time-resolved two-dimensional two-component particle image velocimetry measurements with high spatial resolution are carried out in a water tank agitated by four blades rotating at constant speed. Different blade geometries and rotation speeds are tested for the purpose of modifying turbulent flow conditions. In all cases where no baffles are used to break the rotation, the Zeman length is an order of magnitude smaller than the Taylor length. Compared with the cases with baffles which break the rotation, in the unbaffled cases turbulence production and/or mean advection are significant and the turbulence nonlinearity is dramatically reduced for the horizontal (i.e. normal to the axis of rotation) two-point turbulence fluctuating velocities. This nonlinearity reduction is manifest not only in the interscale turbulent energy transfer but also in the interspace turbulent energy transfer, which nearly vanishes. However, the nonlinearity is not reduced for the vertical two-point turbulence fluctuating velocities: the corresponding interscale turbulent transfer rate is in fact intensified, and its dependence on the two-point separation distance, as well as that of the corresponding interspace turbulent transfer rate, which does not vanish, is significantly modified. Even though non-homogeneities are very different for different blades and rotation speeds in the unbaffled cases, the horizontal fluctuating velocity's second-order structure function collapses with scalings which resemble predictions for homogeneous turbulence subject to strong rotation. The vertical fluctuating velocity's second-order structure function does not collapse for different blade geometries by neither these nor the Kolmogorov predictions.

Uncertainty-Aware Depth Network for Visual Inertial Odometry of Mobile Robots

Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to their cost efficiency. However, the inherent noise in IMU measurements necessitates the integration of additional sensors to facilitate spatial understanding for mapping. Visual–inertial odometry (VIO) is a prominent approach that combines cameras with IMUs, offering high spatial resolution while maintaining cost-effectiveness. In this paper, we introduce our uncertainty-aware depth network (UD-Net), which is designed to estimate both depth and uncertainty maps. We propose a novel loss function for the training of UD-Net, and unreliable depth values are filtered out to improve VIO performance based on the uncertainty maps. Experiments were conducted on the KITTI dataset and our custom dataset acquired from various driving scenarios. Experimental results demonstrated that the proposed VIO algorithm based on UD-Net outperforms previous methods with a significant margin.

Experimental Investigation of Meter-Level Resolution Radar Measurement at Ka Band in Yellow Sea

The backscatter characteristics of ocean surfaces are of great importance in active marine remote-sensing fields. This paper presents the high spatial and temporal resolution dual co-polarized (VV and HH) and cross-polarized (HV) Ka-band sea-surface backscattering measurements taken from the Yellow Sea research platform at incidence angles ranging from 30° to 50° and in the wind speed range from 5.8 to 8.6 m/s. The experimental results show that the backscattering coefficient in HH polarization is close to (or even surpassing) that in VV polarization within a wind speed range of 7.1 to 8.6 m/s for Ka band under high resolution at medium incidence angles (30°–50°). Further analysis of the 10-ms short-time observation samples found that the sea surface echoes in VV polarization are more sensitive to wave motions, exhibiting more complex scattering characteristics such as multi-peaks and reducing scattering energy, especially at high wind speeds and large incident angles. The Doppler velocity analysis also confirms that rapid ocean wave changes can be detected within a short observation period, especially in VV polarization. The research in this article not only demonstrates the high spatial and temporal resolution capabilities of Ka-band radar for ocean surface observation but also reveals its great potential in interpreting and inversing rapidly evolving marine phenomena.

High sensitivity cameras can lower spatial resolution in high-resolution optical microscopy

High-resolution optical fluorescence microscopies and, in particular, super-resolution fluorescence microscopy, are rapidly adopting highly sensitive cameras as their preferred photodetectors. Camera-based parallel detection facilitates high-speed live cell imaging with the highest spatial resolution. Here, we show that the drive to use ever more sensitive, photon-counting image sensors in cameras can, however, have detrimental effects on the spatial resolution of the resulting images. This is particularly noticeable in applications that demand a high space-bandwidth product, where the image magnification is close to the Nyquist sampling limit of the sensor. Most scientists will often select image sensors based on parameters such as pixel size, quantum efficiency, signal-to-noise performance, dynamic range, and frame rate of the sensor. A parameter that is, however, typically overlooked is the sensor’s modulation transfer function (MTF). We have determined the wavelength-specific MTF of front- and back-illuminated image sensors and evaluated how it affects the spatial resolution that can be achieved in high-resolution fluorescence microscopy modalities. We find significant differences in image sensor performance that cause the resulting spatial resolution to vary by up to 28%. This result shows that the choice of image sensor has a significant impact on the imaging performance of all camera-based optical microscopy modalities.

Tree Species Classification from UAV Canopy Images with Deep Learning Models

Forests play a critical role in the provision of ecosystem services, and understanding their compositions, especially tree species, is essential for effective ecosystem management and conservation. However, identifying tree species is challenging and time-consuming. Recently, unmanned aerial vehicles (UAVs) equipped with various sensors have emerged as a promising technology for species identification due to their relatively low cost and high spatial and temporal resolutions. Moreover, the advancement of various deep learning models makes remote sensing based species identification more a reality. However, three questions remain to be answered: first, which of the state-of-the-art models performs best for this task; second, which is the optimal season for tree species classification in a temperate forest; and third, whether a model trained in one season can be effectively transferred to another season. To address these questions, we focus on tree species classification by using five state-of-the-art deep learning models on UAV-based RGB images, and we explored the model transferability between seasons. Utilizing UAV images taken in the summer and fall, we captured 8799 crown images of eight species. We trained five models using summer and fall images and compared their performance on the same dataset. All models achieved high performances in species classification, with the best performance on summer images, with an average F1-score was 0.96. For the fall images, Vision Transformer (ViT), EfficientNetB0, and YOLOv5 achieved F1-scores greater than 0.9, outperforming both ResNet18 and DenseNet. On average, across the two seasons, ViT achieved the best accuracy. This study demonstrates the capability of deep learning models in forest inventory, particularly for tree species classification. While the choice of certain models may not significantly affect performance when using summer images, the advanced models prove to be a better choice for fall images. Given the limited transferability from one season to another, further research is required to overcome the challenge associated with transferability across seasons.

Upconverting thermal history paint for investigations of short thermal events

The main goal of the work was to develop phosphorescent coating for investigations of surface temperature distribution resulted from short duration and high energy thermal events such as small rocket engine testing. For that purpose, we developed thermal history paint with upconverting Gd2O3: Er3+, Yb3+, Mg2+ nanopowder. The developed paint was heated by Joule effect with precise control over temperature and heating time. Photoluminescence signal of cooled samples was excited with 980 nm laser and collected in automatic manner with use of an optical fibre probe connected to photospectrometer. The results show that irreversible changes of paint emission are temperature and heating time specific for calcination temperatures from 700 °C to 1150 °C. Calibration curves were prepared for heating times of 10, 60 and 180 seconds. Finally, 2D surface temperature samples were determined with use of upconverting thermal history paint and compared to IR camera and pyrometric measurements. The results indicate that uncertainty of temperature measurement below 10 °C was achieved for temperatures above 1000 °C. It has been demonstrated that developed thermal history paint allows for measurements of 2D surface temperature distribution with high spatial resolution and good agreement to standard measurements techniques like thermal camera and pyrometer.

Evaluation of Boundary Layer Characteristics at Mount Si’e Based on UAV and Lidar Data

The atmospheric boundary layer is a crucial transitional region connecting the surface with the free atmosphere, playing a bridging role in land-sea-air interactions and the interactions between different atmospheric layers. This study utilizes rotary-wing UAVs, high-resolution lidar, and WRF simulation data to analyze the vertical distribution characteristics of temperature, humidity, wind speed, and wind direction boundary layer over the Mount Si’e region in 4–6 April 2024. The results indicate that the boundary layer temperature decreases with increasing altitude, reaching up to 18°C, while humidity decreases with height, dropping to as low as 35%. Daytime wind speeds range from 4 to 8 m/s, decreasing to 2 to 4 m/s at night. The boundary layer height can reach up to 900 m during the day and drops to 100–200 m at night, showing distinct diurnal variation characteristics. UAV observations are in good agreement with lidar and WRF simulation results, highlighting the application value of UAVs in high temporal and spatial resolution boundary layer studies. The study also reveals the significant impact of complex terrain on boundary layer characteristics, providing scientific insights into the dynamic and thermal processes of the boundary layer and offering reference value for improving regional weather forecasting and numerical simulations.

Impact of Surface Enhanced Raman Spectroscopy in Catalysis.

Catalysis stands as an indispensable cornerstone of modern society, underpinning the production of over 80% of manufactured goods and driving over 90% of industrial chemical processes. As the demand for more efficient and sustainable processes grows, better catalysts are needed. Understanding the working principles of catalysts is key, and over the last 50 years, surface-enhanced Raman Spectroscopy (SERS) has become essential. Discovered in 1974, SERS has evolved into a mature and powerful analytical tool, transforming the way in which we detect molecules across disciplines. In catalysis, SERS has enabled insights into dynamic surface phenomena, facilitating the monitoring of the catalyst structure, adsorbate interactions, and reaction kinetics at very high spatial and temporal resolutions. This review explores the achievements as well as the future potential of SERS in the field of catalysis and energy conversion, thereby highlighting its role in advancing these critical areas of research.

Scanning Transmission Soft X-Ray Microscopy Probes Topical Drug Delivery of Rapamycin Facilitated by Microneedles.

Scanning Transmission X-ray microscopy (STXM) is a sensitive and selective probe for the penetration of rapamycin which is topically applied to human skin ex vivo and is facilitated by skin treatment with microneedles puncturing the skin. Inner-shell excitation serves as a selective probe for detecting rapamycin by changes in optical density as well as linear combination modeling using reference spectra of the most abundant species. The results indicate that mechanical damage induced by microneedles allows this drug to accumulate in the stratum corneum without reaching the viable skin layers. This is unlike intact skin which shows no drug penetration at all and underscores the mechanical impact of microneedle skin treatment. These results are compared to drug penetration profiles of other drugs highlighting the importance of skin barriers. High spatial resolution studies also indicate that the lipophilic drug rapamycin is observed in corneocytes. Attempts in data evaluation are reported to probe rapamycin also in the lipid layers between the corneocytes, which was not accomplished before. These results are compared to recent results on rapamycin uptake in skin where barrier impairment was induced by pre-treatment with the enzyme trypsin and drug formulations leading to occlusion.

Design of a Capacitive Tactile Sensor Array System for Human-Computer Interaction.

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from "0" to "9". These results show the potential of the capacitive sensor array for advanced human-computer interaction applications.

Unsupervised deep denoising for four-dimensional scanning transmission electron microscopy

By simultaneously achieving high spatial and angular sampling resolution, four dimensional scanning transmission electron microscopy (4D STEM) is enabling analysis techniques that provide great insight into the atomic structure of materials. Applying these techniques to scientifically and technologically significant beam-sensitive materials remains challenging because the low doses needed to minimise beam damage lead to noisy data. We demonstrate an unsupervised deep learning model that leverages the continuity and coupling between the probe position and the electron scattering distribution to denoise 4D STEM data. By restricting the network complexity it can learn the geometric flow present but not the noise. Through experimental and simulated case studies, we demonstrate that denoising as a preprocessing step enables 4D STEM analysis techniques to succeed at lower doses, broadening the range of materials that can be studied using these powerful structure characterization techniques.

Atomic Force Microscopy Measurements of Cartilage in Intact and Regenerating Axolotl Limbs.

Mechanical forces provide important signals for normal cell function and pattern formation in developing tissues, and their role has been widely studied during embryogenesis and pathogenesis. Comparatively, little is known of these signals during animal regeneration. The axolotl is an important model organism for the study of regeneration, given its ability to fully restore many organs and tissues after injury, including missing cartilage and bone. Due to its crucial role as the main supporting tissue in the vertebrate body, regaining skeletal function during regeneration requires both the restoration of the missing structures as well as their mechanical properties. This protocol describes a method for processing axolotl limb samples for atomic force microscopy (AFM), which is the gold standard for probing cell and tissue mechanical properties at high spatial resolution. Taking advantage of the regenerative capabilities of the axolotl, this study measured the stiffness of limb cartilage during homeostasis and two stages of limb regeneration: tissue histolysis and cartilage condensation. We show that AFM is a valuable tool for gaining insights into dynamic tissue restructuring and the mechanical changes that occur during regeneration.

A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network

Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial resolution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983–2100 in a high spatial resolution using the LAI Downscaling Network (LAIDN) model driven by inputs including air temperature, relative humidity, precipitation, and topography data. The dataset spans the historical period (1983–2014) and future scenarios (2015–2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indicating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environmental sciences across present and future periods.

Engineering Boron Vacancy Defects in Boron Nitride Nanotubes.

Spin defects in hexagonal boron nitride (hBN) are emerging as promising platforms for quantum sensing applications. In particular, the negatively charged boron vacancy (VB-) centers have been engineered in bulk hBN and few-layer hBN flakes, and employed for sensing. Here, we investigate the engineering of VB- spin defects in boron nitride nanotubes (BNNTs). The generated spin defects are distributed along and around the BNNTs. Moreover, in contrast to hBN flakes, the spins in BNNTs exhibit a directional response relative to the direction of a surrounding magnetic field, which is consistent with the tubular geometry. The unique geometry of BNNTs allows for a more controlled and predictable placement of spin defects compared to bulk hBN, paving the way for innovative sensing applications with high spatial resolution and optomechanical studies of spin defects in hBN.

A high temporal resolution global gridded dataset of human thermal stress metrics

The human thermal stress indices and datasets are vital for promoting public health and reducing negative environmental impacts as global climate change and extreme meteorological events increase. The current thermal indices generally use an instantaneous or average value to describe thermal stress which cannot reflect the distribution of thermal comfort conditions over time, and there are no global-scale thermal stress datasets with both 0.1° or higher spatial resolution and hourly temporal resolution available yet. A novel human thermal metric, Thermal Stress Duration (TSD), is proposed to represent the accumulative time of different thermal stress levels within a certain period. A high temporal resolution global gridded dataset of human thermal stress metrics (HiGTS) is presented, which consists of hourly gridded maps of Universal Thermal Climate Index (UTCI), Universal Thermal Stress (UTS), and daily TSD at 0.1° × 0.1° spatial resolution over the global land surface, spanning from January 1, 2000, to December 31, 2023.

Detecting neoclassical tearing modes in high-temperature ITER plasma scenarios with the ITER prototype electron cyclotron emission diagnostic

Abstract Electron cyclotron emission (ECE) diagnostics for ITER serve two key purposes. The diagnostics will measure plasma electron temperature with high spatial and temporal resolution. Additionally, they will be used to detect neoclassical tearing modes (NTMs), a deleterious and nonlinearly unstable mode causing the growth of magnetic ‘seed’ islands. Interpreting ECE requires anticipation of physical limits including frequency cut-offs and harmonic overlap. In high temperature plasmas, the relativistic shift and broadening of the emission must also be considered to accurately reconstruct the electron temperature spatial profile. Accounting for these effects allows ECE diagnostics to be used for accurate measurement of the equilibrium electron temperature profile, as well as fluctuations about this equilibrium. One such fluctuation is caused by the fast radial transport of heat across rotating magnetic islands. ECE diagnostics can detect this change as an oscillation at the plasma rotation frequency to determine the existence and location of NTMs. This paper presents work on a synthetic diagnostic for ECE. The synthetic diagnostic tests simulated ECE signals, which are inferred from ITER scenarios perturbed by magnetic islands after accounting for all ECE physics. The synthetic diagnostic tests conventional ECE detection algorithms for NTMs in real-time on ITER-recommended hardware. Combined, these two areas of focus help determine design of the ECE system.