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Previous work suggests that synergistic activity among motor elements implicated in force production tasks underlies enhanced performance stability associated with visual feedback. A hallmark of synergistic activity is reciprocal compensation, that is, covariation in the states of motor elements that stabilizes critical performance variables. The present study examined if characteristics of reciprocal compensation are indicators of individuals' capacity to respond adaptively to variations in the resolution of visual feedback about criterion performance. Twenty healthy adults (19.25 ± 1.25 years; 15 females and five males) pressed two sensors with their index fingers to produce a total target force equivalent to 20% of their maximal voluntary contraction under nine conditions that differed in the spatial resolution of real-time feedback about their performance. By combining within-trial uncontrolled manifold and sample entropy analyses, we quantified the amount and degree of irregularity (i.e., non-repetitiveness) of reciprocal compensations over time. We found a U-shaped relationship between performance stability and gain. Importantly, this relationship was moderated by the degree of irregularity of reciprocal compensation. Lower irregularity in reciprocal compensation patterns was related to individuals' capacity to maintain (or minimize losses in) performance under changes in feedback resolution. Results invite future investigation into how interindividual variations in reciprocal compensation patterns relate to differences in control strategies supporting adaptive responses in complex, visually guided motor tasks. [ABSTRACT FROM AUTHOR]

A recent ground-breaking experimental study [Lyons et al., Phys. Rev. X 14(1), 011017 (2024)] reports on measuring the temporal duration and the spatial extent of failed attempts to cross an activation barrier (i.e., "loops") for a folding transition in a single molecule and for a Brownian particle trapped within a bistable potential. Within the model of diffusive dynamics, however, both of these quantities are, on average, exactly zero because of the recrossings of the barrier region boundary. That is, an observer endowed with infinite spatial and temporal resolution would find that finite loops do not exist (or, more precisely, form a set of measure zero). Here we develop a description of the experiment that takes the "fuzziness" of the boundaries caused by finite experimental resolution into account and show how the experimental uncertainty of localizing the point, in time and space, where the barrier is crossed leads to observable distributions of loop times and sizes. Although these distributions generally depend on the experimental resolution, this dependence, in certain cases, may amount to a simple resolution-dependent factor and, therefore, the experiments do probe inherent properties of barrier crossing dynamics. [ABSTRACT FROM AUTHOR]

Surface functionalization of low-dimensional nanomaterials offers a means to tailor their optoelectronic and chemical characteristics. However, functionalization reactions are sensitive to the inherent surface features of nanomaterials, such as defects, grain boundaries, and edges. Conventional optical characterization methods, such as Raman spectroscopy, have limited sensitivity and spatial resolution and, therefore, struggle to visualize reaction sites and chemical species. Here, we demonstrate the capability of spatially and chemically sensitive tip-enhanced Raman spectroscopy imaging to map the distribution of molecules in covalently functionalized graphene. Hyperspectral vertex component analysis and density functional theory are necessary to interpret the nature of binding sites and extract information from the spatially and spectrally heterogeneous datasets. Our results clarify the origin of heterogeneous surface functionalization, resolving preferential binding at edges and defects. This work demonstrates the potential of nanospectroscopic tools combined with unsupervised learning to characterize complex, partially ordered optoelectronic nanomaterials. [ABSTRACT FROM AUTHOR]

Bragg interferometry (BI) is an imaging technique based on four-dimensional scanning transmission electron microscopy (4D-STEM) wherein the intensities of select overlapping Bragg disks are fit or more qualitatively analyzed in the context of simple trigonometric equations to determine local stacking order. In 4D-STEM based approaches, the collection of full diffraction patterns at each real-space position of the scanning probe allows the use of precise virtual apertures much smaller and more variable in shape than those used in conventional dark field imaging such that even buried interfaces marginally twisted from other layers can be targeted. With a coarse-grained form of dark field ptychography, BI uses simple physically derived fitting functions to extract the average structure within the illumination region and is, therefore, viable over large fields of view. BI has shown a particular advantage for selectively investigating the interlayer stacking and associated moiré reconstruction of bilayer interfaces within complex multi-layered structures. This has enabled investigation of reconstruction and substrate effects in bilayers through encapsulating hexagonal boron nitride and of select bilayer interfaces within trilayer stacks. However, the technique can be improved to provide a greater spatial resolution and probe a wider range of twisted structures, for which current limitations on acquisition parameters can lead to large illumination regions and the computationally involved post-processing can fail. Here, we analyze these limitations and the computational processing in greater depth, presenting a few methods for improvement over previous works, discussing potential areas for further expansion, and illustrating the current capabilities of this approach for extracting moiré-scale strain. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for the three-dimensional characterization of flyers based on microscopic photonic Doppler velocimetry (PDV) velocity field measurements. By strategically positioning 56 PDV velocity measurement spots within a circle of 0.50 mm diameter at an interval of approximately 58 μm, we achieve spatially resolved measurements of the velocity history of the flyer during its flight within the barrel and subsequent impact on a transparent window. Crucial data, such as displacement history, impact time, and impact pulse duration, can be extracted at each measurement spot. Furthermore, a correlation analysis of these datasets enables the reconstruction of the flyer's three-dimensional morphology, both during its flight inside the barrel and at the critical moment of impact with the window. This method offers a remarkable temporal resolution in the order of nanoseconds and a spatial resolution in the order of micrometers. With the characteristics of high spatial resolution and high experimental efficiency, the microscope PDV technology is poised to become an essential research tool in the study of action mechanisms, design optimization, and determination of initiation criteria for exploding foil initiators. [ABSTRACT FROM AUTHOR]

The surface plasmon resonance (SPR) technique has been widely applied to biosensing technologies for the rapid quantification of biomolecules without enzyme and fluorescent labeling. However, the conventional prism-coupling SPR method generally has a detection area of a few mm2, and the large contribution of the background signal forms a barrier to highly sensitive detection. Based on a highly spatially resolved SPR method, the present study constructed a scanning GC-SPR imaging instrument using an objective lens with a high numerical aperture and a plasmonic chip that could be used for grating-coupled SPR. Focusing light on the diffraction limit can suppress background signals and improve detection sensitivity. SPR imaging can also be performed by scanning a focal spot. Using this method, the refractive index of a mixture of water and dimethyl sulfoxide was measured with a detection accuracy of 2.43 × 10−3 RIU. Polydopamine films prepared with a thickness of <5 nm were also measured, and each film thickness was evaluated with high sensitivity from the effective refractive index detected in a small area of <1 µm2. [ABSTRACT FROM AUTHOR]

We report an experimental study of surface acoustic wave (SAW) localization and propagation in random metasurfaces composed of Al scatters using pump–probe spectroscopy. Thanks to this technique, wideband high frequency acoustic modes are generated, and their dynamical propagation directly from inside of the media with a high (micrometric) spatial resolution is enabled. During SAW propagation, part of the acoustic wavefront energy is trapped within free areas between the scatterers, acting as cavities. The spectral content of the localized modes of a few GHz is found to depend on the shape and size of the cavities but also on the landscape seen by the wave during its propagation before arriving inside them. The experimental results are supported by numerical simulations using the finite element method. This study is the phononic part of a more global research on the co-localization of elastic and optical waves on random metasurfaces, with the main objective of enhancing the photon–phonon interaction. Applications could range from the design of acousto-optic modulators to ultrasensitive sensors. [ABSTRACT FROM AUTHOR]

Multiple scattering poses a fundamental limitation in deep imaging, especially for high-resolution optical imaging methods. The amalgamation of reflection matrix measurements and optical coherence tomography (OCT) has afforded significant advantages for deep imaging through highly scattering media. To empirically exhibit the superior performance of reflection matrix OCT (RMOCT), this study proposes a unique method to ascertain the actual resolutions at each imaging point. In contrast to conventional theoretical lateral resolutions, these resolutions are derived by applying time-reversal decomposition to the time-gated reflection matrix. Moreover, the concept of contribution rate, which quantifies the imaging contributions for each point, is introduced by considering the local imaging point itself and its neighboring points. The contribution rate provides a quantitative evaluation of the imaging quality afforded by a system. To the best of our knowledge, this study represents the comprehensive assessment of the practical performance of RMOCT in terms of actual resolving power and imaging quality. [ABSTRACT FROM AUTHOR]

Whether single-molecule trajectories, observed experimentally or in molecular simulations, can be described using simple models such as biased diffusion is a subject of considerable debate. Memory effects and anomalous diffusion have been reported in a number of studies, but directly inferring such effects from trajectories, especially given limited temporal and/or spatial resolution, has been a challenge. Recently, we proposed that this can be achieved with information-theoretical analysis of trajectories, which is based on the general observation that non-Markov effects make trajectories more predictable and, thus, more "compressible" by lossless compression algorithms. Toy models where discrete molecular states evolve in time were shown to be amenable to such analysis, but its application to continuous trajectories presents a challenge: the trajectories need to be digitized first, and digitization itself introduces non-Markov effects that depend on the specifics of how trajectories are sampled. Here we develop a milestoning-based method for information-theoretical analysis of continuous trajectories and show its utility in application to Markov and non-Markov models and to trajectories obtained from molecular simulations. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Long‐axis positron emission tomography (PET) has emerged as one of the recent research directions in PET due to its ability to significantly enhance sensitivity and counting performance for low‐dose imaging, rapid imaging, and whole‐body dynamic imaging.The PET system presented in this study is a long‐axis animal PET based on lutetium–yttrium orthosilicate and silicon photomultiplier, designed for whole‐body imaging in rats. It features a diameter of 143 mm and an axial length of 213.3 mm. This study evaluated the performance of this PET system in accordance with the National Electrical Manufacturers Association (NEMA) NU 4‐2008 standards.The performance evaluation was conducted according to the NEMA NU 4‐2008 standards in terms of spatial resolution, sensitivity, counting rate performance, scatter fraction (SF) and image quality. In addition, a rat imaging study was conducted to assess the imaging capability of this PET system.The average energy resolution of the PET system was 12.87%, the average coincidence timing resolution was 751 ps. The FWHM of spatial resolution reconstructed by filtered back projection and 3D‐OSEM‐PSF algorithm at 5 mm radial offset from the axial center were 1.65 and 0.88 mm. The peak absolute sensitivity measured by a point source at the center of the field of view was evaluated as 6.71% (361–661 keV) and 10.31% (250–750 keV). For the mouse‐like phantom, the SF was 11.0% and the peak noise equivalent counting rate (NECR) was 1193 kcps at 94.2 MBq (2.54 mCi). For the rat‐like phantom, the SF was 26.8% and the NECR was 682.5 kcps at 78.6 MBq (2.12 mCi).The performance measurement results demonstrate that this PET system exhibits high sensitivity and count rate performance, making it potential for high‐quality whole‐body dynamic imaging of rats. [ABSTRACT FROM AUTHOR]

Purpose Methods Results Conclusion The performance of detectors is key for a PET scanner to achieve high spatial resolution and high sensitivity. This work aims to develop flood histogram generating algorithms to reduce the edge effect and improve the crystal identification of a PET detector consisting of two optically coupled pixelated scintillator detectors.The PET detector consists of two optically coupled detectors, each consisting of a 23×23 LYSO crystal array with a crystal size of 1.0×1.0×20 mm3 read out by an 8×8 SiPM array with a pixel size of 3.0×3.0 mm2. The SiPM array is read out with a resistor network circuit to obtain four position encoding energy signals. A novel center of gravity (COG) positioning algorithm using six signals from the two detectors was proposed and compared to the traditional COG algorithms using either four or eight signals from the detectors. The raised‐to‐the‐power (RTP) method was applied to the three COG algorithms for the PET detector. Different powers of the RTP from 1.0 to 2.5 were evaluated.The proposed COG algorithm significantly improves the crystal identification at the junction of the two detectors as compared to the COG algorithm using four signals of each detector, and improves the crystal identification at the center of the two detectors as compared to the COG algorithm using eight signals from both detectors. The RTP method significantly improves the overall flood histogram qualities of the two COG algorithms using either eight or six signals from the two detectors, and the two COG algorithm provide similar flood histogram quality when a power of 1.5 is used.The novel positioning algorithms reduce the edge effect and improve the flood histogram quality for a PET detector consisting of two optically coupled detectors, each consisting of a pixelated scintillator crystal array and a SiPM array with highly multiplexed four signal readout. The positioning algorithms can be used in a PET scanner to improve the spatial resolution and sensitivity. [ABSTRACT FROM AUTHOR]

Materials and their interfaces are the core for the development of a large variety of fields, including catalysis, energy storage and conversion. In this case, tip-enhanced Raman spectroscopy (TERS), which combines scanning probe microscopy with plasmon-enhanced Raman spectroscopy, is a powerful technique that can simultaneously obtain the morphological information and chemical fingerprint of target samples at nanometer spatial resolution. It is an ideal tool for the nanoscale chemical characterization of materials and interfaces, correlating their structures with chemical performances. In this review, we begin with a brief introduction to the nanoscale characterization of materials and interfaces, followed by a detailed discussion on the recent theoretical understanding and technical improvements of TERS, including the origin of enhancement, TERS instruments, TERS tips and the application of algorithms in TERS. Subsequently, we list the key experimental issues that need to be addressed to conduct successful TERS measurements. Next, we focus on the recent progress of TERS in the study of various materials, especially the novel low-dimensional materials, and the progresses of TERS in studying different interfaces, including both solid–gas and solid–liquid interfaces. Finally, we provide an outlook on the future developments of TERS in the study of materials and interfaces. [ABSTRACT FROM AUTHOR]

Light field (LF) image super-resolution (SR) is a technique designed to enhance the details and clarity of low-resolution (LR) light field images by leveraging the additional information and structure present within the LF data. With the rise of deep learning, the performance of LF image super-resolution has been significantly improved, but it has led to an increase in model parameters and computational complexity, resulting in a phenomenon of excessive reliance on computational resources. To address this problem, this paper proposes a lightweight but effective model. In our approach, we employ Swin Attention to extract features from LF images. Window and Shifted Window are the main components of Swin Attention. Therefore, the local features of the LF images are captured using Window, and feature correlations are established through Shifted Window. Furthermore, we introduce Extensive Attention (EA) blocks to capture the global features of the LF image. In addition to the aforementioned configurations, we have also engineered an iteration of the Low Computation Convolution (LCC) that is capable of eliminating redundant information prior to the feature extraction process in LF images. This design aims to mitigate superfluous computations, thereby enhancing computational efficiency. Experimental results show that our approach achieves suboptimal performance compared to other state-of-the-art models, while having fewer parameters, lower computational complexity, and faster inference speed. [ABSTRACT FROM AUTHOR]

With the development and popularization of unmanned aerial vehicle (UAV) equipment, UAV-based remote sensing technology has developed rapidly. With UAV aerial photography platforms, the obtained low altitude remote sensing images of surfaces have high temporal and spatial resolution and can be used for monitoring and change analyses of surface geographic information. As it is impractical to carry complex and large load equipment on a UAV, it is desirable to develop embedded systems for relevant real-time processing with UAV remote sensing technology. This study combines the advantages of UAV remote sensing image processing and embedded development technology to develop a near real-time UAV remote sensing image change detection system, which realizes the fast, accurate, and automatic detection of variations in the target area. Finally, the feasibility, correctness, and practicability of the prototype system are demonstrated with experiments. [ABSTRACT FROM AUTHOR]

Universal access to childhood vaccination is important to child health and sustainable development. Here we identify, at a fine spatial scale, under-immunized children and zero-dose children. Using Chad, as an example, the most recent nationally representative household survey that included recommended vaccine antigens was assembled. Age-disaggregated population (12–23 months) and vaccination coverage were modelled at a fine spatial resolution scale (1km × 1 km) using a Bayesian geostatistical framework adjusting for a set of parsimonious covariates. There was a variation at fine spatial scale in the population 12–23 months a national mean of 18.6% (CrI 15.8%–22.6%) with the highest proportion in the South-East district of Laremanaye 20.0% (14.8–25.0). Modelled coverage at birth was 49.0% (31.2%–75.3%) for BCG, 44.8% (27.1–74.3) for DTP1, 24.7% (12.5–46.3) for DTP3 and 47.0% (30.6–71.0) for measles (MCV1). Combining coverage estimates with the modelled population at a fine spatial scale yielded 312,723 (Lower estimate 156055–409266) zero-dose children based on DTP1. Improving routine immunization will require investment in the health system as part of enhancing primary health care. The uncertainties in our estimates highlight areas that require further investigation and higher quality data to gain a better understanding of vaccination coverage. [ABSTRACT FROM AUTHOR]

The manipulation and detection of mobile domain walls in nanoscopic magnetic wires underlies the development of multibit memories. The studies of such domain walls have focused on macroscopic wires that allow for optical detection by using magneto-optic effects. In this study, we demonstrated the electrical tracking with a spatial resolution of better than 40 nm of multiple mobile domain walls in nanoscopic racetracks, using a set of anomalous Hall detectors integrated into the racetracks. Electrical time-series signals from the Hall detectors allow for the static and dynamic phase space visualization of the dynamics of a domain wall or multiple domain walls that can be described by a multicore memristor model. The domain wall dynamics and stochasticity can be controlled in racetracks even to deep submicron dimensions. [ABSTRACT FROM AUTHOR]

Mid-crustal magma domains are the source of many basaltic eruptions. Lavas from individual eruptions are often chemically homogeneous, suggesting that they derive from single, well-mixed magma reservoirs. The 2023 to 2024 eruptions at Sundhnúksgígar in the Svartsengi volcanic system in Iceland provide an opportunity to observe the behavior of a mid-crustal magma domain at high spatial and temporal resolution by detailed sampling and geochemical characterization. We observed substantial mantle-derived geochemical variability in the products erupted in the first hours of the December 2023 and January, February, and March to May 2024 eruptions, indicating that the eruptions derived from multiple magma reservoirs, which mineral-melt equilibration pressures place in the mid crust. The unusual presence of geochemical heterogeneity in the mid-crustal magma domain provides insights into how dynamic and complex mid-crustal magma domains can be. [ABSTRACT FROM AUTHOR]

In single-site validations, site-pixel, synthetic pixel and footprint-integrated methods are commonly used to validate evapotranspiration (ET) retrieval at the pixel scale. To reveal the reliability of these methods, this study analysed the performance of the three methods for ET retrieval validation based on eddy-covariance observations at the Daman site which is numerously used for validation in Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The analysis at different spatial scales in different seasons showed that the validation results of the site-pixel method were more similar that of the footprint-integrated method than the synthetic pixel method. However, the validation results of the site-pixel method showed significant discrepancies from those of the footprint-integrated method, especially in summer at the 480 M scale which is near to the spatial resolution of MODIS with a large difference of more than 125.65 W M−2. This error is due to aggregation and high spatial heterogeneity. The use of synthetic pixel can avoid this error. Normally, the error probably appears when the wind direction is 270°–360°, usually in summer, autumn and winter. In addition, the different aggregation methods had a slight effect on the ET estimation results. This study is helpful in determining the reliability of different single-point validation methods and provides a reliable way to select a validation method for single-site validation of remote sensing retrieval of ET. [ABSTRACT FROM AUTHOR]

This study presents the spatial interactions downscaling (SPID) method and introduces the climate data for adaptation and vulnerability assessments (ClimAVA) dataset. SPID employs random forest models to capture the relationship between spatial patterns at global circulation model (GCM) resolution and fine-resolution pixel values. In summary, a random forest model is trained for each fine spatial resolution pixel of the reference data as the predictand, and nine pixels from the spatially resampled (coarser) version of the reference data at the GCM's resolutions as predictors. Models are then utilized to downscale the bias-corrected GCM data. The ClimAVA-SW dataset offers a high-resolution (4 km), bias-corrected, downscaled future climate projection derived from seventeen CMIP6 GCMs. It includes three variables (daily precipitation, minimum and maximum temperature) for three shared socioeconomic pathways (SSP245, SSP370, SSP585) across the U.S. Southwest region. The ClimAVA dataset sets itself apart with the SPID method's capacity to provide remarkable climate realism, high physical plausibility of change, and excellent representation of extreme events while maintaining user-friendliness and requiring relatively low computational resources. [ABSTRACT FROM AUTHOR]

The importance of microvascular imaging in diagnosis and therapeutic targeting of various diseases is increasingly recognized. The new approach emphasizes the need for holistic studies to understand the inter-organ vascular cross-talk. Here, we report on the development of a novel perfusion protocol which consistently delivers a micro-computed tomography contrast agent to micro-vessels of multiple organs in a single experimental animal. We describe the achieved repeatability of the perfusions, as well as the image analysis steps developed individually for each organ type. We also optimize image acquisition by investigating the compromise between shortening of the scanning time and preservation of the highest possible spatial resolution. Taking together, with the multi-organ perfusion, optimized image acquisition, and the conceived image analysis steps, we provide a comprehensive and reliable experimental protocol for studying vascular morphology and pathology in multi-organ diseases. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusion Digital tomosynthesis has shown potential for increasing specificity and sensitivity compared to radiography for low‐dose chest imaging. Prior investigation of the s‐DCT system indicated potential, but additional iteration with improved scan speed, power, and angular span was necessary for translation.The study aims to demonstrate and characterize a second‐generation stationary digital chest tomosynthesis (s‐DCT) scanner with increased x‐ray energy, tube current, and larger angular span.The second‐generation s‐DCT system employed a meter‐long linear carbon nanotube (CNT) source array integrated with a digital detector and patient imaging table. Tube output, focal spot size, modulation transfer function (MTF), artifact spread function (ASF), and imaging performance were evaluated. A lung phantom with simulated nodules was imaged for clinical task‐based demonstration.The scanner achieved a 6 s scan time, significantly improved from the prior generation's 16 s. The x‐ray tube exhibited good current stability, with 20.4 ± 0.6 mA tube current and focal spot size aligned with specifications (IEC 0.8). The MTF confirmed enhanced spatial resolution of 2.4 lp/mm, comparable to commercial chest tomosynthesis systems. The ASF indicated improved depth resolution (5.2 mm, previously 9.5 mm). Phantom imaging showcased visualization of both high and low‐attenuation lung nodules.The second‐generation s‐DCT system exhibited improved performance in terms of tube power, scan time, and image quality. Enhanced in‐plane and depth resolution, along with faster imaging, suggest potential clinical benefits for improved diagnoses. Further clinical validation is warranted to ascertain the system's clinical utility. [ABSTRACT FROM AUTHOR]

Anticipating the effects of global change on biodiversity has become a global challenge requiring new methods. Approaches like species distribution models have limitations which have fueled the development of joint species distribution models (JSDMs). However, JSDMs rely on systematic surveys community data, and no assessment has been made of their suitability with unstructured opportunistic databases data. We used hierarchical modeling of species communities (HMSC) to test JSDMs performance when using opportunistic databases. Using artificial data that mimic the limitations of such databases by subsampling complete co‐occurrence matrices (i.e. original data), we analysed how the completeness of opportunistic databases affects JSDMs regarding 1) the role of independent variables on species occurrence, 2) residual species co‐occurrence (as a proxy of biotic interactions) and 3) species distributions. Moreover, we illustrate how to evaluate completeness at the pixel level of real data with a study case of forest tree species in Europe, and evaluate the role of data completeness in model estimation. Our results with artificial data demonstrate that decreasing the completion percentage (the rate of original data presences represented in the subsampled matrices) increases false negatives and negative co‐occurrence probabilities, resulting in a loss of ecological information. However, HMSC tolerates different levels of degradation depending on the model aspect being considered. Models with 50% of missing data are valid for estimating species niches and distribution, but interaction matrices require databases with at least 75% of completion data. Furthermore, HMSC's predictions often resemble the original community data (without false negatives) even more than the subsampled data (with false negatives) in the training dataset. These findings were confirmed with the real study case. We conclude that opportunistic databases are a valuable resource for JSDMs, but require an analysis of data completeness for the target taxa in the study area at the spatial resolution of interest. [ABSTRACT FROM AUTHOR]

Generative models, such as diffusion models, have made significant advancements in recent years, enabling the synthesis of high‐quality realistic data across various domains. Here, the adaptation and training of a diffusion model on super‐resolution microscopy images are explored. It is shown that the generated images resemble experimental images, and that the generation process does not exhibit a large degree of memorization from existing images in the training set. To demonstrate the usefulness of the generative model for data augmentation, the performance of a deep learning‐based single‐image super‐resolution (SISR) method trained using generated high‐resolution data is compared against training using experimental images alone, or images generated by mathematical modeling. Using a few experimental images, the reconstruction quality and the spatial resolution of the reconstructed images are improved, showcasing the potential of diffusion model image generation for overcoming the limitations accompanying the collection and annotation of microscopy images. Finally, the pipeline is made publicly available, runnable online, and user‐friendly to enable researchers to generate their own synthetic microscopy data. This work demonstrates the potential contribution of generative diffusion models for microscopy tasks and paves the way for their future application in this field. [ABSTRACT FROM AUTHOR]

Monocular depth estimation is an important but challenging task. Although the performance has been improved by adopting various encoder-decoder architectures, the estimated depth maps lack structure details and clear edges due to simple repeated upsampling. To solve this problem, this paper presents the novel LapUNet (Laplacian U-shape networks), in which the encoder adopts ResNeXt101, and the decoder is constructed with the novel DLRU (dynamic Laplacian residual U-shape) module. The DLRU module based on the U-shape structure can supplement high-frequency features by fusing dynamic Laplacian residual into the process of upsampling, and the residual is dynamically learnable due to the addition of convolutional operation. Also, the ASPP (atrous spatial pyramid pooling) module is introduced to capture image context at multiple scales though multiple parallel atrous convolutional layers, and the depth map fusion module is used for combining high and low frequency features from depth maps with different spatial resolution. Experiments demonstrate that the proposed model with moderate model size is superior to other previous competitors on the KITTI and NYU Depth V2 datasets. Furthermore, 3D reconstruction and target ranging by utilizing the estimated depth maps prove the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In Brillouin optical correlation-domain reflectometry (BOCDR), sinusoidal modulation is applied to the output frequency of a light source, with spatial resolution inversely related to the modulation amplitude. We have developed an effective method to estimate the modulation amplitude using the width of the noise spectrum caused by Rayleigh scattering, eliminating the need for an optical spectrum analyzer or modifications to existing equipment. However, the Rayleigh noise spectrum often displays a three-layered structure, complicating the identification of the appropriate spectral components for estimating the modulation amplitude. In this work, we investigate the origins of this three-layered Rayleigh noise spectrum and identify the directivity of an optical circulator as the source of the third noise component. As replacing the circulator with alternative optical components is not easy, it remains an essential part of the system. Our analysis shows that the third noise component exhibits significantly small variation in spectral width with changes in modulation frequency compared to the first and second components. This characteristic allows for the effective separation and identification of the third noise component, thereby enhancing the accuracy and convenience of modulation amplitude estimation in BOCDR. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A high-accuracy, continuous air temperature (Ta) dataset with high spatiotemporal resolution is essential for human health, disease prediction, and energy management. Existing datasets consider factors such as elevation, latitude, and surface temperature but insufficiently address meteorological and spatiotemporal factors, affecting accuracy. Additionally, no high-resolution dataset currently includes daily maximum (Tmax), minimum (Tmin), and mean (Tmean) temperatures generated using a unified methodology. Here, we introduce the four-dimensional spatiotemporal deep forest (4D-STDF) model, integrating 12 multisource factors, encompassing static and dynamic parameters, and six refined spatiotemporal factors to produce Ta datasets. This approach generates three high-accuracy Ta datasets at 1 km spatial resolution covering mainland China from 2003 to 2022. These datasets, in GeoTIFF format with WGS84 projection, comprise daily Tmax, Tmin, and Tmean. The overall RMSE are 1.49 °C, 1.53 °C, and 1.18 °C for the estimates. The 4D-STDF model can also be applied to other regions with sparse meteorological stations. [ABSTRACT FROM AUTHOR]

The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales. [ABSTRACT FROM AUTHOR]

Satellite altimetry is the main tool for constructing global or regional marine gravity fields. To improve the accuracy and spatial resolution, it is necessary to fuse multi-mission altimeters. How to determine the weights of multi-mission altimeters is a crucial issue, making the conventional calculation process very complex. In addition, traditional satellite inversion methods are often independent of shipborne gravity, which is used only as validation data, thus not take full advantages of high accuracy and resolution of shipborne gravity. In this study, we introduce a convolutional neural network (CNN) to merge the vertical deflections (DOVs) obtained from multi-altimeter missions to construct a marine gravity model in the South China Sea. High-accuracy shipborne gravity and a dataset comprising DOVs and geo-locations are employed as input data for neural network training. For the validation of CNN method, the gravity model is also computed by conventional Inverse Vening Meinesz (IVM) method. Independent shipborne gravity measurements and SIO V32.1, DTU17 models are used as validation data. The evaluation results show that the CNN-derived model achieves a higher level of accuracy, yielding a standard deviation (STD) of 3.21 mGal, with an improvement of 36.56% compared to IVM-derived model. More than 92% of the differences between the CNN-derived model and shipborne gravity are less than 5 mGal. In addition, spectral analysis results further show that the CNN-derived model has stronger energy at short wavelengths (less than 25 km) compared to other models. These findings reveal that CNN method is feasible for marine gravity recovery and the CNN-derived model can achieve higher accuracy. The CNN method can improve the accuracy and spectral characteristics of the constructed gravity model by taking advantage of the high accuracy and high resolution of shipborne gravity. [ABSTRACT FROM AUTHOR]

In this work, we propose a structured-illumination lensless digital holographic microscopy (SI-LDHM). SI-LDHM illuminates a sample with 24 structured illuminations (8 orientations χ 3 phase shifts) and records the defocused interferogram formed by two copies of object waves along the ±1st diffraction orders of each SI. The reconstructed object waves under different illumination orientations are respectively propagated to the sample plane along the +1st diffraction order and then averaged, thus yielding a clean image without the artifact of twin images. Experimental results demonstrated that thanks to the multi-oriented SI strategy, the twin image in SI-LDHM is sevenfold reduced compared to conventional DHM, while the spatial resolution is 1.15 times higher. [ABSTRACT FROM AUTHOR]

Highly sensitive airborne virus monitoring is critical for preventing and containing epidemics. However, the detection of airborne viruses at ultra-low concentrations remains challenging due to the lack of ultra-sensitive methods and easy-to-deployment equipment. Here, we present an integrated microfluidic cartridge that can accurately detect SARS-COV-2, Influenza A, B, and respiratory syncytial virus with a sensitivity of 10 copies/mL. When integrated with a high-flow aerosol sampler, our microdevice can achieve a sub-single-copy spatial resolution of 0.83 copies/m3 for airborne virus surveillance with an air flow rate of 400 L/min and a sampling time of 30 minutes. We then designed a series of virus-in-aerosols monitoring systems (RIAMs), including versions of a multi-site sampling RIAMs (M-RIAMs), a stationary real-time RIAMs (S-RIAMs), and a roaming real-time RIAMs (R-RIAMs) for different application scenarios. Using M-RIAMs, we performed a comprehensive evaluation of 210 environmental samples from COVID-19 patient wards, including 30 aerosol samples. The highest positive detection rate of aerosol samples (60%) proved the aerosol-based SARS-CoV-2 monitoring represents an effective method for spatial risk assessment. The detection of 78 aerosol samples in real-world settings via S-RIAMs confirmed its reliability for ultra-sensitive and continuous airborne virus monitoring. Therefore, RIAMs shows the potential as an effective solution for mitigating the risk of airborne virus transmission. Effective surveillance of respiratory virus particles in air requires highly sensitive tools that can produce timely and accurate results. Here, the authors develop a device for detection of four respiratory viruses in air with high spatial viral resolution and test it in different settings. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Vectorial holography technology plays a fundamental role by exploring the vectorial nature of light and enriches disruptive applications in optical encryption, display, and manipulation. With its flexible control methods and high‐precision subwavelength design, metasurface possesses rich modulation flexibility that surpasses traditional optical modulation devices. Here, a vectorial holographic design is implemented that can cover various polarization information by using a birefringent metasurface. The design adopts a gradient descent optimization method to jointly optimize the phase information of the orthogonal polarization channels with the birefringent metasurface, for continuous variation of polarization information in the entire vector space on the Poincare Sphere with efficient information integration. Based on the detailed depiction of full vector space polarization texture, such efficient vectorial holographic optimization methods can expand the encryption channels and achieve high spatial resolution allowing large capacity multiplexing, data storage, pattern recognition, etc. [ABSTRACT FROM AUTHOR]

A broad spectrum of electromagnetic waves has been explored for wireless neuromodulation. Transcranial magnetic stimulation, with long wavelengths, cannot provide submillimeter spatial resolution. Visible light, with its short wavelengths, suffers from strong scattering in the deep tissue. Microwaves have centimeter-scale penetration depth and have been shown to reversibly inhibit neuronal activity. Yet, microwaves alone do not provide sufficient spatial precision to modulate target neurons without affecting surrounding tissues. Here, we report a split-ring resonator (SRR) that generates an enhanced microwave field at its gap with submillimeter spatial precision. With the SRR, microwaves at dosages below the safe exposure limit are shown to inhibit the firing of neurons within 1 mm of the SRR gap site. The microwave SRR reduced seizure activity at a low dose in both in vitro and in vivo models of epilepsy. This microwave dosage is confirmed to be biosafe via histological and biochemical assessment of brain tissue. [ABSTRACT FROM AUTHOR]

Single‐molecule localization microscopy (SMLM), which has revolutionized nanoscale imaging, faces challenges in densely labelled samples due to fluorophore clustering, leading to compromised localization accuracy. In this paper, we propose a novel convolutional neural network (CNN)‐assisted approach to address the issue of locating the clustered fluorophores. Our CNN is trained on a diverse data set of simulated SMLM images where it learns to predict point spread function (PSF) locations by generating Gaussian blobs as output. Through rigorous evaluation, we demonstrate significant improvements in PSF localization accuracy, especially in densely labelled samples where traditional methods struggle. In addition, we employ blob detection as a post‐processing technique to refine the predicted PSF locations and enhance localization precision. Our study underscores the efficacy of CNN in addressing clustering challenges in SMLM, thereby advancing spatial resolution and enabling deeper insights into complex biological structures. [ABSTRACT FROM AUTHOR]

Due to the difficulty of collecting a sufficient number of photons within a narrow spectral window, hyperspectral images often have low spatial resolution. In order to solve this problem without increasing hardware costs, a large number of image super-resolution algorithms have been proposed and have shown promising results. However, the performance of these algorithms is inconsistent in different datasets. To fix this situation, we proposed a multi-model deep prior regularization method for multispectral and hyperspectral image fusion. This method leverages mechanistic knowledge to combine various super-resolution results into a single algorithm, significantly improving the quality of the hyperspectral images. Specifically, we first proposed a variable residual mixed attention network. This network takes a low-resolution hyperspectral image and a high-resolution multispectral image as inputs, producing deep prior results from three different modes by adjusting spatial and channel attention. Then, we reconstructed the prior results along the spectral dimension and used them as regularization terms in our mechanistic model. We transformed the problem of finding the optimal solution of the model into solving the Sylvester equation and applied a fast solver for this equation to create the high-resolution hyperspectral images. Additionally, we used a genetic algorithm to determine the best hyperparameters for different deep prior results. Through validation on three datasets, we have demonstrated that our method performs better than existing methods in both quantitative and qualitative comparisons. [ABSTRACT FROM AUTHOR]

\nHIGHLIGHTSThe Visible Infrared Imaging Radiometer Suite (VIIRS) is a visible, near and shortwave, to thermal infrared multispectral scanning instrument operational on three polar orbiting satellites, Suomi-NPP, JPSS-1, and JPSS-2. In the present paper, the processing of VIIRS using ACOLITE is introduced, using the Dark Spectrum Fitting (DSF) algorithm for processing of the visible to shortwave infrared bands. ACOLITE now includes support for processing both the imaging (I) and moderate (M) resolution bands at 375 m and 750 m spatial resolution, respectively. In most conditions encountered in the present study, the SWIR bands (either I or M) are automatically selected by the DSF for performing the aerosol correction. The processing is evaluated for turbid water remote sensing via autonomous hyperspectral radiometry from four sites across coastal and estuarine waters: two sites in Belgium and one each in France and Argentina. Through analysis of hundreds of matchups between the satellite and in situ measurements, a generally good performance is found for both I and M bands, especially for bands with the largest water signal, i.e. bands between 490 and 670 nm, where on average relative differences of 10–15% were found. Reflectance biases are generally less than 0.01, with a negative sign in the green and red bands and a positive sign in the blue and NIR bands. Similar matchup results are found for the I and M red and NIR bands, with a slightly higher scatter for the NIR bands. An additional comparison with OCSSW/l2gen processing of the M band data is performed for various configurations. Overall, DSF performance is better in the visible bands, whereas l2gen outputs are more closely aligned with the in situ measurements in the NIR. On average, negative biases are found for all l2gen configurations, up to −0.02 in the blue bands. Using either the SWIR1 + 2 or SWIR1 + 3 bands for the aerosol correction gives the best performance for l2gen processing. For the three VIIRS instruments separately, the average spectral differences with in situ measurements are comparable, with the most important deviation occurring at the Suomi-NPP shortest blue bands, where DSF processing gives a larger positive bias, up to nearly 0.02. For these bands, results from l2gen correspond more closely across the three instruments – although with significant negative biases for all three sensors up to −0.02 – presumably due to the use of system vicarious calibration gains in that processor. An operational network of autonomous hyperspectral instruments provides validation data for any overpassing optical imaging satellite in its commissioning or operational phase and eliminates the need for spectral interpolation or band shifting. In the case of VIIRS specifically, the hyperspectral instruments provide adequate data for the validation of the 20, 40 and 80 nm wide bands. With three operational wide-swath instruments, which provide largely interoperable data, a high frequency of observations is available, especially for study areas at higher latitudes. The novel exploitation of the I bands is now possible, thanks to the free and open source availability of ACOLITE. The advantage of the higher resolution I band data, combined with multiple VIIRS overpasses per day, is demonstrated for mapping turbidity in nearshore regions with high spatial variabilty and for detecting under-resolved floating algae. The open-source ACOLITE processor was adapted for VIIRS I (375 m) and M (750 m) dataThree operational VIIRS (Suomi-NPP, JPSS-1 and JPSS-2) were processed and validatedIn situ autonomous hyperspectral radiometry was used for   performance evaluationACOLITE I and M band outputs compared well across hundreds of turbid water matchupsTurbidity and FAI product resolution were improved with ACOLITE I bandprocessingThe open-source ACOLITE processor was adapted for VIIRS I (375 m) and M (750 m) dataThree operational VIIRS (Suomi-NPP, JPSS-1 and JPSS-2) were processed and validatedIn situ autonomous hyperspectral radiometry was used for   performance evaluationACOLITE I and M band outputs compared well across hundreds of turbid water matchupsTurbidity and FAI product resolution were improved with ACOLITE I bandprocessing [ABSTRACT FROM AUTHOR]

Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping. [ABSTRACT FROM AUTHOR]

Li‐air batteries attract significant attention due to their highest theoretical energy density among all existing energy storage technologies. Currently, challenges related to extending lifetime and long‐term stability limit their practical application. To overcome these issues and enhance the total capacity of Li‐air batteries, this study introduces an innovative approach with NiO/ZrO2 catalysts. Operando advanced chemical imaging with micrometer spatial resolution unveils that NiO/ZrO2 catalysts substantially change the kinetics of crystalline lithium hydroxide (LiOH) formation and facilitate its rapid decomposition with heterogeneous distribution. Moreover, ex situ combined neutron and X‐ray computed tomography (CT) analysis, provide evidence of distinct lithium phases homogeneously distributed in the presence of NiO/ZrO2. These findings underscore the material's superior physico‐chemical and electronic properties, with more efficient oxygen diffusion and indications of lower obstruction to its active sites, avoiding clogging in the active electrode, a common cause of capacity loss. Electrochemical tests conducted at high current density demonstrated a significant kinetic enhancement of the oxygen reduction and evolution reactions, resulting in improved charge and discharge processes with low overpotential. This pioneering approach using NiO/ZrO2 catalysts represents a step toward on developing the full potential of Li‐air batteries as high‐energy‐density energy storage systems. [ABSTRACT FROM AUTHOR]

Fengyun-4A (FY-4A) is the latest generation of China's geostationary satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4A can provide high-precision, high-frequency observation data, which makes a new possibility for estimating the downwelling surface longwave radiation (DSLR) with high spatial and temporal resolution. This work presents a new method for estimating DSLRs under all-sky conditions using a genetic algorithm–artificial neural network (GA-ANN) algorithm based on brightness temperature (BT) from the FY-4A AGRI infrared channels and near-surface air temperature and dew point temperature from ERA5 reanalysis data. Based on the verification results of two independent observation sites, it is shown that the bias and RMSE are - 4.31 W/m2 and 35.28 W/m2, respectively. Compared the CERES SYN all-sky DSLR product with the DSLR estimated by the new method, the bias and RMSE are 0.86 W/m2 and 26.87 W/m2, respectively, and the new method has a higher spatial resolution (4 km), which can display more details of spatial variation. [ABSTRACT FROM AUTHOR]

Background: Monolithic or semi‐monolithic detectors are attractive for positron emission tomography (PET) scanners with depth‐of‐interaction (DOI) capability. However, they often require complicated calibrations to determine the interaction positions of gamma photons. Purpose: We introduce a novel hybrid detector design that combines pixelated and semi‐monolithic elements to achieve DOI capability while simplifying the calibrations for positioning. Methods: A prototype detector with eight hybrid lutetium–yttrium oxyorthosilicate (LYSO) layers having dimensions of 25.8 × 12.9 × 15 mm3 was constructed. The energy‐weighted and energy‐squared weighted averages were used for estimating the x‐ (pixelated direction) and y‐positions (non‐pixelated direction). Pseudo‐pixels were defined as discrete areas on the flood image based on the crystal look‐up table (LUT). The intrinsic spatial resolutions in the pixelated and non‐pixelated directions were measured. The ratio of the maximum to the sum of the multipixel photon counter (MPPC) signals was used to estimate the DOI positions. The coincidence timing resolution (CTR) was measured using the average and energy‐weighted average of the earliest n time stamps. Two energy windows of 250–700 and 400–600 keV were applied for the measurements. Results: The pattern of the flood images showed discrete event clusters, demonstrating that simple calibrations for determining the x‐ and y‐positions of events could be achieved. Under 400–600 keV energy window, the average intrinsic spatial resolutions were 1.15 and 1.34 mm for the pixelated and non‐pixelated directions; the average DOI resolution of the second row of pseudo‐pixels was 5.1 mm in full width at half maximum (FWHM); when using the energy‐weighted average of the earliest four‐time stamps, the best CTR of 350 ps was achieved. Applying a broader energy window of 250–700 keV only slightly degrades the DOI resolution while maintaining the intrinsic resolution; the best CTR degrades to 410 ps. Conclusions: The proposed hybrid detector concept was verified, and a prototype detector showed high performance for 3D positioning and timing resolution. The novel detector concept shows promise for preclinical and clinical PET scanners with DOI capability. [ABSTRACT FROM AUTHOR]

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the Convolutional Block Attention Module (CBAM) attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images. [ABSTRACT FROM AUTHOR]

The electrical resistivity of subsurface rocks is one of the important sensitive parameters characterizing the internal physics of the Earth. Currently, research on subsurface electrical structures using geomagnetic sounding methods primarily focuses on two approaches: the first is based on observations from a few geomagnetic stations, which have low spatial resolution and cannot effectively describe the distribution of anomalies; the second is based on mobile geomagnetic observations, which have low temporal resolution and cannot promptly reflect anomalies. To address these issues, this study deployed a dense geomagnetic array for long-term observation in the southern segment of the Xiaojiang Fault Zone in the Yuxi area of southern Yunnan. This setup aims to promptly capture seismic magnetic anomalies, providing more data support and fundamental information for short-term earthquake prediction. Based on the long-term observation data from the dense array, the study of the subsurface electrical structure is carried out. The results indicate that during the observation period, which was seismically quiet, the regional subsurface electrical structure remained stable. A large-scale subsurface low-resistivity body was observed in the region, and the electrical structures at the two ends of the southern segment of the Xiaojiang Fault Zone were found to be completely different. [ABSTRACT FROM AUTHOR]

The Standardized Precipitation Evapotranspiration Index (SPEI) is a widely recognized and effective tool for monitoring meteorological droughts. However, existing SPEI datasets suffer from spatial discontinuity or coarse spatial resolution problems, which limits their applications at the local level for drought monitoring research. Therefore, we calculated the SPEI index at meteorological stations, combined with the Global Precipitation Measurement (GPM) Precipitation (Pre), Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST), ERA5‐Land Shortwave Radiation (SR), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) datasets and Random Forest Regression (RFR) model, developed a high spatial resolution (1 km) SPEI (HSPEI) datasets with multiple time scales in mainland China from 2001 to 2022. Compared to other SPEI datasets, the HSPEI datasets have higher spatial resolution and can effectively identify the detailed characteristics of drought in mainland China from 2001 to 2022. Overall, the HSPEI datasets can be effectively applied to the research of different droughts in China from 2001 to 2022. [ABSTRACT FROM AUTHOR]

Lack of a long‐term time series of dataset with a high spatiotemporal resolution at a global scale poses a great challenge to clarify the characteristics of DOC in space and depth, and its trend in time. Thus, there is an urgent need for the development of a global DOC gridded dataset in space, time and depth. The Biogeochemical Argo (BGC‐Argo) provides an important data source for obtaining global DOC, but is limited by irregular spatial sampling locations. Besides, BGC‐Argo has shorter time series coverage and fewer profiles compared to Core‐Argo. Thus, this manuscript aims at reconstructing the DOC profiles based on the Core‐Argo and BGC‐Argo profiles and then developing a spatial, temporal and depth‐specific gridded DOC dataset, named G4D‐DOC. Validation results demonstrate that G4D‐DOC has a good overall consistency with WOA18 and GLODAPv2 datasets, particularly at depths of 10 dbar and 1000 dbar, where it surpasses consistency at other standard depths. In addition, compared to WOA18, G4D‐DOC has achieved a breakthrough in a temporal resolution from a climatological monthly to monthly, and compared to GLODAPv2, G4D‐DOC has achieved a breakthrough in space from irregular discrete locations to regular grids. Further, G4D‐DOC can be widely used to conduct the characteristics of DOC in space and depth and its trends at global and regional scales. The metadata of G4D‐DOC is as follows: four dimensions mean two dimensions in space (longitude and latitude), one in time and one in depth; data format is standard Hierarchical Data Format Version 4 (HDF4) with a spatial resolution of 1 degree and temporal resolutions of annual, seasonal and monthly intervals at 26 standard layers above 2000 dbar in depth; the spatial coverage is global and the time period is from 2005 to 2022. [ABSTRACT FROM AUTHOR]

Brain voltage plays a crucial role in indicating internal functions or diseases, and optical voltage imaging has gained intensive attention in recent years. Despite encouraging progress, current implementations encounter limitations pertaining to penetration depth, field of view (FOV), and photostability of indicators. To mitigate these challenges, a robust voltage‐sensitive dye (VSD)‐based whole‐field photoacoustic brain detection (WF‐PABD) platform is proposed, enabling direct evaluation of voltage dynamics across the whole brain, forming as PA‐VSD. WF‐PABD is equipped with a 512‐element ring‐array ultrasound detector capable of 360‐degree scanning, providing a large FOV (≈5 cm), high spatial resolution (≈110 µm), and rapid imaging acquisition. The proposed VSD remained ≈75% photostability after 30 min laser exposure, much greater than most calcium sensors. The optical voltage‐response mechanisms are validated and the capability of PA‐VSD to directly screen seizures is established. It is demonstrated that investigating connectivity among different brain regions allows to identify the precise location of active epileptic foci as well as the electrical conduction pathways and their directionality through fast temporal visualization. In summary, this study not only addresses the need for non‐invasive, high‐resolution, long‐term, and direct monitoring of brain voltage but also empowers exciting venues for PA applications in neuroscience. [ABSTRACT FROM AUTHOR]

The interseismic behavior of faults (whether they are locked or creeping) and their quantitative kinematic constraints are critical for assessing the seismic hazards of faults and their surrounding areas. Currently, the creep of the eastern segment of the Laohushan Fault in the Haiyuan Fault Zone at the northeastern margin of the Tibetan Plateau, as revealed by InSAR observations, lacks confirmation from other observational methods, particularly high-precision GNSS studies. In this study, we utilized nearly seven years of observation data from a dense GNSS continuous monitoring profile (with a minimum station spacing of 2 km) that crosses the eastern segment of the Laohushan Fault. This dataset was integrated with GNSS data from regional continuous stations, such as those from the Crustal Movement Observation Network of China, and multiple campaign measurements to calculate GNSS baseline change time series across the Laohushan Fault and to obtain a high spatial resolution horizontal crustal velocity field for the region. A comprehensive analysis of this primary dataset indicates that the Laohushan Fault is currently experiencing left-lateral creep, characterized by a partially locked shallow segment and a deeper locked segment. The fault creep is predominantly concentrated in the shallow crustal region, within a depth range of 0–5.7 ± 3.4 km, exhibiting a creep rate of 1.5 ± 0.7 mm/yr. Conversely, at depths of 5.7 ± 3.4 km to 16.8 ± 4.2 km, the fault remains locked, with a loading rate of 3.9 ± 1.1 mm/yr. The shallow creep is primarily confined within 3 km on either side of the fault. Over the nearly seven-year observation period, the creep movement within approximately 5 km of the fault's near field has shown no significant time-dependent variation, instead demonstrating a steady-state behavior. This steady-state creep appears unaffected by postseismic effects from historical large earthquakes in the adjacent region, although the deeper (far-field) tectonic deformation of the Laohushan Fault may have been influenced by the postseismic effects of the 1920 Haiyuan M8.5 earthquake. [ABSTRACT FROM AUTHOR]

Urban trees support vital ecological functions and help with the mitigation of and adaption to climate change. Yet, their monitoring and management require significant public resources. remote sensing could facilitate these tasks. Recent hyperspectral satellite programs such as PRISMA have enabled more advanced remote sensing applications, such as species classification. However, PRISMA data's spatial resolution (30 m) could limit its utility in urban areas. Improving hyperspectral data resolution with pansharpening using the PRISMA coregistered panchromatic band (spatial resolution of 5 m) could solve this problem. This study addresses the need to improve hyperspectral data resolution and tests the pansharpening method by classifying exemplative urban tree species in Naples (Italy) using a convolutional neural network and a ground truths dataset, with the aim of comparing results from the original 30 m data to data refined to a 5 m resolution. An evaluation of accuracy metrics shows that pansharpening improves classification quality in dense urban areas with complex topography. In fact, pansharpened data led to significantly higher accuracy for all the examined species. Specifically, the Pinus pinea and Tilia x europaea classes showed an increase of 10% to 20% in their F1 scores. Pansharpening is seen as a practical solution to enhance PRISMA data usability in urban environments. [ABSTRACT FROM AUTHOR]

This work presents two pre-processing patches to automatically correct the residual local misalignment of datasets acquired by very/extremely high resolution (VHR/EHR) satellite multispectral (MS) scanners, one for, e.g., GeoEye-1 and Pléiades, featuring two separate instruments for MS and panchromatic (Pan) data, the other for WorldView-2/3 featuring three instruments, two of which are visible and near-infra-red (VNIR) MS scanners. The misalignment arises because the two/three instruments onboard GeoEye-1 / WorldView-2 (four onboard WorldView-3) share the same optics and, thus, cannot have parallel optical axes. Consequently, they image the same swath area from different positions along the orbit. Local height changes (hills, buildings, trees, etc.) originate local shifts among corresponding points in the datasets. The latter would be accurately aligned only if the digital elevation surface model were known with sufficient spatial resolution, which is hardly feasible everywhere because of the extremely high resolution, with Pan pixels of less than 0.5 m. The refined co-registration is achieved by injecting the residue of the multivariate linear regression of each scanner towards lowpass-filtered Pan. Experiments with two and three instruments show that an almost perfect alignment is achieved. MS pansharpening is also shown to greatly benefit from the improved alignment. The proposed alignment procedures are real-time, fully automated, and do not require any additional or ancillary information, but rely uniquely on the unimodality of the MS and Pan sensors. [ABSTRACT FROM AUTHOR]

In 2023, the Chinese company PIESAT launched the multi-static X-band SAR constellation Hongtu-1 (HT1). HT1 consists of the active monostatic SAR sensor HT1-A and the three additional passive SAR receivers HT1-B, HT1-C and HT1-D. The passive sensors are arranged as a cartwheel in a circle around the active sensor. For our SAR interferometric investigation, we were able to use a multi-static HT1 recording. After a brief introduction of HT1, we describe the processing performed. Based on the phases of the six single-pass interferometric pairs, we calculated height differences relative to the Copernicus DEM. Larger deviations were observed mainly for mining areas and for forest areas. Thanks to the simultaneous acquisition of the interferometric pairs, the high spatial resolution and the good signal quality, the necessary processing was relatively easy to perform. Besides the interferometric phase, we also investigated possible applications of multi-static coherence. Forest can be recognized by its reduced single-pass coherence values. Based on our results, we expect that the multi-static HT1 coherence and its dependence on the interferometric baseline can be used to estimate parameters such as forest biomass. [ABSTRACT FROM AUTHOR]