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The New Zealand Active Faults Database (NZAFD) contains underpinning data to help mitigate the impacts of future surface-rupturing earthquakes in Aotearoa New Zealand. However, defining the associated hazards and risks must be undertaken at relevant scales and as such, the NZAFD contains two scale-based datasets each serving complementary, but different, purposes. The high-resolution (‘NZAFD-HighRes’) dataset contains enough detail on surface traces for cadastral scale land-use planning purposes, while the other dataset is generalised to 1:250,000 scale (‘NZAFD-AF250’). Here we document for the first time the NZAFD-HighRes dataset (v2.0) and describe the recent efforts that have focused on updating the dataset structure to increase useability, compiling data and improving GIS infrastructure. The NZAFD-HighRes and NZAFD-AF250 datasets, along with Fault Avoidance Zones and Fault Awareness Areas – land-use planning tools used to mitigate surface rupture hazard – are publicly accessible via the active faults web service and a webmap application at https://data.gns.cri.nz/af/. This upgraded webmap enables active fault data to be discovered and used for informing future surface rupture hazard assessments. The relationship of the NZAFD to other active fault datasets and models is also discussed, along with future directions and challenges. [ABSTRACT FROM AUTHOR]

In the rapidly evolving Metaverse, enhancing user immersion through clear, lifelike, and ergonomic near‐eye displays is crucial. However, existing rigid near‐eye displays encounter challenges such as insufficient resolution, limited adaptability, and suboptimal visual experiences. To address these issues, a strategic shift is proposed to flexible ultrahigh‐resolution (FUR) displays, which combine ultrahigh resolution with the ability to conform to individual eye curvature for a more realistic field of view. FUR quantum dot light‐emitting diodes (FUR‐QLEDs) featuring 9072 pixels per inch (PPI), a maximum external quantum efficiency (EQE) of 15.7%, and peak brightness of 15 163 cd m−2 are achieved through the integration of nanoimprinting and surface modification technologies. The degradation mechanism of FUR‐QLEDs under bending fatigue tests is investigated, identifying the high elastic modulus of the insulating patterned film as the primary cause through theoretical analysis, simulation, and experimental characterizations. Optimizing the elastic modulus of the patterned film enabled to maintain 91% of its initial brightness after 400 bending cycles, demonstrating exceptional bending stability and durability of FUR‐QLEDs. [ABSTRACT FROM AUTHOR]

X-ray imaging with a large field of view (FOV) and high resolution is extremely important for Rayleigh–Taylor instability measurement with a small amplitude and high spatial frequency in laser inertial confinement fusion. We developed an advanced Kirkpatrick–Baez (AKB) microscope based on the quadratic-aberration theory to realize a large FOV and high resolution. This microscope was assembled and tested in a laboratory, and it was then successfully applied for imaging the hydrodynamic instability of a perturbation target in implosion experiments at the Shenguang-III prototype laser facility. Imaging results demonstrate that the AKB microscope can achieve an optimal resolution of ~ 0.53 μm and ~ 0.40 μm and a spatial resolution of < 1.5 μm within a 300-µm FOV and < 4.5 μm in a 1-mm FOV. [ABSTRACT FROM AUTHOR]

With the development of modern medicine, the importance of continuous and reliable pulse wave monitoring has increased significantly in physiological evaluation and disease diagnosis. Among them, the 3D reconstruction of the pulse wave is indispensable, and needs rely on ultra‐high resolution sensor arrays, that is, high spatial resolution, temporal resolution, and force resolution. Herein, a flexible high‐density 32 × 32 tactile sensor array based on pressure‐sensitive tunneling mechanism is develpoed. Conformal graphene nanowalls (GNWs) pattern arrays are deposited on micro‐pyramidal structural Si substrate via mask‐assisted plasma enhanced chemical vapor deposition (PECVD) method and are adopted as pressure‐sensitive electrode, exhibiting a spatial resolution of 64 dots/cm2, high sensitivity (222.36 kPa−1) and short response time (2 ms). More importantly, HfO2 tunneling layer can effectively suppress noise current, which made it sense weak pressure signals with 1/1000 force resolution and SNR of 36.32 dB. By leveraging its high‐resolution array, more holistic pulse signals are acquired and the 3D shape of the pulse wave are successfully replicated. This work shows high‐resolution sensors have significant promise for applications in remote intelligent diagnostics. [ABSTRACT FROM AUTHOR]

Ultra‐fast magic‐angle spinning (100+kHz) has revolutionized solid‐state NMR of biomolecular systems but has so far failed to gain ground for the analysis of paramagnetic organic and inorganic powders, despite the potential rewards from substantially improved spectral resolution. The principal blockages are that the smaller fast‐spinning rotors present significant barriers for sample preparation, particularly for air/moisture‐sensitive systems, and are associated with low sensitivity from the reduced sample volumes. Here, we demonstrate that the sensitivity penalty is less severe than expected for highly paramagnetic solids and is more than offset by the associated improved resolution. While previous approaches employing slower MAS are often unsuccessful in providing sufficient resolution, we show that ultra‐fast 100+kHz MAS allows site‐specific assignments of all resonances from complex paramagnetic solids. Combined with more reliable rotor materials and handling methods, this opens the way to the routine characterization of geometry and electronic structures of functional paramagnetic systems in chemistry, including catalysts and battery materials. We benchmark this approach on a hygroscopic luminescent Tb3+ complex, an air‐sensitive homogeneous high‐spin Fe2+ catalyst, and a series of mixed Fe2+/Mn2+/Mg2+ olivine‐type cathode materials. [ABSTRACT FROM AUTHOR]

Forest fuels are the core element of fire management; each fuel component plays an important role in fire behavior. Therefore, accurate determination of their characteristics and spatial distribution is crucial. This paper introduces a novel method for mapping the spatial distribution of litter and duff fuel loads using data collected by unmanned aerial vehicles. The approach leverages a very high-resolution multispectral data analysis within a machine learning framework to achieve precise and detailed results. A set of vegetation indices and texture metrics derived from the multispectral data, optimized by a "Variable Selection Using Random Forests" (VSURF) algorithm, were used to train random forest (RF) models, enabling the modeling of high-resolution maps of litter and duff fuel loads. A field campaign to measure fuel loads was conducted in the mixed forest of the natural protected area of "Sierra de Quila", Jalisco, Mexico, to measure fuel loads and obtain field reference data for calibration and validation purposes. The results revealed moderate determination coefficients between observed and predicted fuel loads with R2 = 0.32, RMSE = 0.53 Mg/ha for litter and R2 = 0.38, RMSE = 13.14 Mg/ha for duff fuel loads, both with significant p-values of 0.018 and 0.015 for litter and duff fuel loads, respectively. Moreover, the relative root mean squared errors were 33.75% for litter and 27.71% for duff fuel loads, with a relative bias of less than 5% for litter and less than 20% for duff fuel loads. The spatial distribution of the litter and duff fuel loads was coherent with the structure of the vegetation, despite the high complexity of the study area. Our modeling approach allows us to estimate the continuous high-resolution spatial distribution of litter and duff fuel loads, aligned with their ecological context, which dictates their dynamics and spatial variability. The method achieved acceptable accuracy in monitoring litter and duff fuel loads, providing researchers and forest managers with timely data to expedite decision-making in fire and forest fuel management. [ABSTRACT FROM AUTHOR]

The assessment of wind energy resources is critical for the transition from fossil fuel to renewable energy sources. Using the outputs from high-resolution global climate models (GCMs), such as the High Resolution Model Intercomparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6), has become one of the most important tools in wind energy research. This study evaluated the reliability of the 22 GCMs available in the HighResMIP-PRIMAVERA project by simulating the wind energy climatology and variability over the Tibetan Plateau (TP) with reference to observations and investigated the differences in performance of the GCMs between high-resolution (HR) and low-resolution (LR) simulations. The results show that most models performed relatively well in simulating the probability distribution of the observed wind speed over the TP, but nearly half of the models generally underestimated the wind speed, whereas the others tended to overestimated the wind speed. Compared with the wind speed, the GCMs showed larger biases in reproducing the wind power density (WPD) and other wind energy resources, whereas the biases in multi-model ensembles were relatively smaller than those in most individual models. With respect to interannual variability, both the HR and LR models failed to capture interannual variations in WPD over the TP. Furthermore, more than half of the HR GCMs had a reduced bias relative to the corresponding LR GCMs, indicating the good performance of most HR models in simulating wind energy resources over the TP in terms of spatial pattern and temporal variability. However, the overall performance of HR GCMs varied among models, which suggests that solely improving the horizontal resolution is not sufficient to completely solve the uncertainties and deficiencies in the simulation of wind energy over complex terrain. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Taxa Methods Results Main Conclusions The purported defining features of chimpanzee (Pan troglodytes) biogeography are sometimes simplistic and contradictory, yet we rely on these metrics to shape conservation and management planning for this endangered ape. We analysed the cover and configuration of woody vegetation at the northern margins of the species' range in Senegal to elucidate key predictors of chimpanzee biogeography.Southeastern Senegal.Western chimpanzee (Pan troglodytes verus).We combined reconnaissance surveys and interviews with published accounts of chimpanzee presence to verify previous estimates of the range limits of the species. High‐resolution, multispectral satellite images were used to quantify the configuration of trees and shrubs at each site. Woody vegetation cover and physiognomy, river density and road density were compared within and outside of the species range with generalised linear mixed models.Our surveys and interviews support the current estimates of the northern limits of the species range. Chimpanzee‐occupied sites had more patchily‐distributed vegetation, greater patch edge complexity, and slightly higher river density than unoccupied sites. Unoccupied sites had more woody vegetation cover and higher road density. Of all predictors, roads had the highest impact on chimpanzee occupancy.In line with previous findings, anthropogenic modifications of the landscape had a strong impact on chimpanzee occupancy, but surprisingly, woody vegetation cover was negatively associated with chimpanzee occupancy in this region. Instead, more specific metrics of vegetation configuration were positively associated with chimpanzee site occupancy, indicating that precise measures are needed for assessing chimpanzee biogeography at the landscape scale. [ABSTRACT FROM AUTHOR]

Analyses of global climate model results for urban impacts on temperature and precipitation are rare. Previous analyses of the global Conformal Cubic Atmospheric Model simulation results for 1985–2010 have revealed urban effects on minimum and maximum temperatures. Using the same dataset to derive time‐zone‐corrected three‐hourly local time period (LTP) data, averaged diurnal cycles of temperature and precipitation were calculated for grid cells with greater than 10% urban fraction (urban grid cells) globally. Three latitudinal bands were assessed: northern extratropics (NET, 274 urban grid cells), southern extratropics (SET, 39 urban grid cells), and the Tropics (26 urban grid cells). The largest statistically significant urban influences on temperature are consistently found at night, in agreement with many previous studies on urban heat islands. Signs of urban cooling were found for a few hours, from 09:00 to 15:00 LTP, most often in summer. Influences of urban areas on precipitation varied, with small increases and decreases in all latitudinal bands and seasons. For NET, increases were generally found. In the Tropics, increases were found from 21:00 to 09:00 LTP for all seasons except DJF, with decreases for all seasons from 15:00 to 18:00 LTP. In SET, all seasons had increases for 21:00 to 00:00 LTP and decreases for 15:00 to 18:00 LTP. DJF had decreases for all LTPs except 21:00 to 00:00 LTP and SON had increases for all times except 15:00 to 18:00 LTP. Differences in rainfall in the region surrounding the urban areas were broadly similar to local changes in NET. For the Tropics and SET, regional decreases were found for DJF and JJA, with a more varied pattern for other months. Regional effects appeared to be more restricted to near‐urban areas in the Tropics than in NET. The results indicate some influence of nearby urban areas on regional temperature and precipitation. [ABSTRACT FROM AUTHOR]

Naked-eye 3D micro-LED display combines the characteristics of 3D display with the advantages of micro-LED. However, the 3D micro-LED display is still at the conceptual stage, limited by its intrinsic emission properties of large divergence angle and non-coherence, as well as difficulties in achieving large viewing angles with high luminous efficiency. In this work, we propose a double-layer metasurface film integrating functions of collimation with multiple deflections, constituting a micro-LED naked-eye 3D display system. The system is characterized through numerical simulations using the 3D finite-difference time-domain method. The simulation results show that the double-layer metasurface film restricts 90% of the emitted light of the micro-LED to the vicinity of the 0° angle, improving its spatial coherence. Subsequently, a large-angle, low-crosstalk outgoing from −45° to 45° is achieved, while providing a deflection efficiency of over 80% and a pixel density of up to 605. We believe this design provides a feasible approach for realizing naked-eye 3D micro-LED displays with a large field of view, low crosstalk, and high resolution. [ABSTRACT FROM AUTHOR]

Significance: Lensless digital inline holographic microscopy (LDIHM) is an emerging quantitative phase imaging modality that uses advanced computational methods for phase retrieval from the interference pattern. The existing end-to-end deep networks require a large training dataset with sufficient diversity to achieve high-fidelity hologram reconstruction. To mitigate this data requirement problem, physics-aware deep networks integrate the physics of holography in the loss function to reconstruct complex objects without needing prior training. However, the data fidelity term measures the data consistency with a single low-resolution hologram without any external regularization, which results in a low performance on complex biological data. Aim: We aim to mitigate the challenges with trained and physics-aware untrained deep networks separately and combine the benefits of both methods for high-resolution phase recovery from a single low-resolution hologram in LDIHM. Approach: We propose a hybrid deep framework (HDPhysNet) using a plug-andplay method that blends the benefits of trained and untrained deep models for phase recovery in LDIHM. The high-resolution phase is generated by a pre-trained high-definition generative adversarial network (HDGAN) from a single low-resolution hologram. The generated phase is then plugged into the loss function of a physics- aware untrained deep network to regulate the complex object reconstruction process. Results: Simulation results show that the SSIM of the proposed method is increased by 0.07 over the trained and 0.04 over the untrained deep networks. The average phase-SNR is elevated by 8.2 dB over trained deep models and 9.8 dB over untrained deep networks on the experimental biological cells (cervical cells and red blood cells). Conclusions: We showed improved performance of the HDPhysNet against the unknown perturbation in the imaging parameters such as the propagation distance, the wavelength of the illuminating source, and the imaging sample compared with the trained network (HDGAN). LDIHM, combined with HDPhysNet, is a portable and technology-driven microscopy best suited for point-of-care cytology applications. [ABSTRACT FROM AUTHOR]

Purpose: To achieve high‐resolution fetal brain anatomical imaging without introducing image artifacts by reducing the FOV, and to demonstrate improved image quality compared to conventional full‐FOV fetal brain imaging. Methods: Reduced FOV was achieved by applying outer volume suppression (OVS) pulses immediately prior to standard single‐shot fast spin echo (SSFSE) imaging. In the OVS preparation, a saturation RF pulse followed by a gradient spoiler was repeated three times with optimized flip‐angle weightings and a variable spoiler scheme to enhance signal suppression. Simulations and phantom and in‐vivo experiments were performed to evaluate OVS performance. In‐vivo high‐resolution SSFSE images acquired using the proposed approach were compared with conventional and high‐resolution SSFSE images with a full FOV, using image quality scores assessed by neuroradiologists and calculated image metrics. Results: Excellent signal suppression in the saturation bands was confirmed in phantom and in‐vivo experiments. High‐resolution SSFSE images with a reduced FOV acquired using OVS demonstrated the improved depiction of brain structures without significant motion and blurring artifacts. The proposed method showed the highest image quality scores in the criteria of sharpness, contrast, and artifact and was selected as the best method based on overall image quality. The calculated image sharpness and tissue contrast ratio were also the highest with the proposed method. Conclusion: High‐resolution fetal brain anatomical images acquired using a reduced FOV with OVS demonstrated improved image quality both qualitatively and quantitatively, suggesting the potential for enhanced diagnostic accuracy in detecting fetal brain abnormalities in utero. [ABSTRACT FROM AUTHOR]

The tropospheric delay is difficult to be modeled accurately resulting from the high variability of atmospheric water vapor, especially under the conditions of sparse station distribution and large elevation differences, which poses challenges for real-time precise positioning. In this contribution, a real-time high-resolution (0.01° × 0.01°) zenith tropospheric delay (ZTD) model considering sparse stations and topography variations (named GFNSS) is established by integrating the information from the Global Forecast System (GFS) and Global Navigation Satellite System (GNSS). GNSS observations and GFS forecasts in the Hong Kong area are selected for the experiments. The performance of ZTDs derived from GFNSS is assessed and validated with the real-time GNSS ZTDs obtained by the precise point positioning method and the IGS post-processed ZTD products. Results show that the root mean square error (RMSE) of GFNSS ZTDs is 5.5 mm and 12.8 mm when validated with real-time and post-processed ZTD, while those for ZTD derived from the low-order surface model (LSM) are 8.8 mm and 19.0 mm, presenting a reduction of 37.5% and 32.6%, respectively. The sensitivity of model performance to the number of modeling stations and elevation differences is also evaluated. The results reveal that the GFNSS model is resistant to station number and presents high accuracy and stability with the RMSE values varying between 4.0 and 6.0 mm as the modeling station number decreases from 13 to 4, while the RMSE for the LSM model increases dramatically from 4.0 to 27.4 mm. Meanwhile, the GFNSS model achieves an RMSE value of 5.8 mm when the elevation differences are over 300 m, indicating a notable 84.9% reduction compared to that of LSM (RMSE of 38.5 mm). [ABSTRACT FROM AUTHOR]

Abstract X-ray imaging with a large field of view (FOV) and high resolution is extremely important for Rayleigh–Taylor instability measurement with a small amplitude and high spatial frequency in laser inertial confinement fusion. We developed an advanced Kirkpatrick–Baez (AKB) microscope based on the quadratic-aberration theory to realize a large FOV and high resolution. This microscope was assembled and tested in a laboratory, and it was then successfully applied for imaging the hydrodynamic instability of a perturbation target in implosion experiments at the Shenguang-III prototype laser facility. Imaging results demonstrate that the AKB microscope can achieve an optimal resolution of ~ 0.53 μm and ~ 0.40 μm and a spatial resolution of

As global climate change accelerates, the need for precise and high-resolution carbon emission inventories becomes increasingly urgent, particularly in rapidly developing regions. This study introduces an advanced methodology for constructing regional-scale gridded anthropogenic CO2 emission inventories, integrating the latest energy consumption data and remote sensing technologies. By systematically calculating emissions across various sectors and energy types, and utilizing Geographic Information System (GIS) for spatial distribution, we have generated a high-resolution emission map with a 1 km × 1 km grid. The accuracy of the map was validated through regression analysis with the Multi-resolution Emission Inventory for China (MEIC) dataset, ensuring its reliability. This work provides critical insights for targeted emission reduction strategies, contributing to the formulation of sustainable development policies that align with global climate objectives.

The high-resolution and nondestructive co-reference measurement of the inner and outer three-dimensional (3D) surface profiles of laser fusion targets is difficult to achieve. In this study, we propose a laser differential confocal (LDC)–atomic force probe (AFP) method to measure the inner and outer 3D surface profiles of laser fusion targets at a high resolution. This method utilizes the LDC method to detect the deflection of the AFP and exploits the high spatial resolution of the AFP to enhance the spatial resolution of the outer profile measurement. Nondestructive and co-reference measurements of the inner profile of a target were achieved using the tomographic characteristics of the LDC method. Furthermore, by combining multiple repositionings of the target using a horizontal slewing shaft, the inner and outer 3D surface profiles of the target were obtained, along with a power spectrum assessment of the entire surface. The experimental results revealed that the respective axial and lateral resolutions of the outer profile measurement were 0.5 and 1.3 nm, while the respective axial and lateral resolutions of the inner profile measurement were 2.0 nm and approximately 400.0 nm. The repeatabilities of the root-mean-square deviation measurements for the outer and inner profiles of the target were 2.6 and 2.4 nm, respectively. We believe our study provides a promising method for the high-resolution and nondestructive co-reference measurement of the inner and outer 3D profiles of laser fusion targets.

According to the characteristics of the internal damage of the Cf/SiC composite material after the lightning strike, this article establishes a high‐energy ultrasonic excitation, reception, and high‐resolution ultrasonic testing model, achieving high‐energy and high‐resolution ultrasound C‐scan detection of Cf/SiC composite material samples after lightning strikes, and combining digital X‐ray and computed tomography methods for damage analysis. When the lightning energy is gradually increased, the lightning stratified damage will occur inside the sample, and the delamination damage area reaches the maximum value of 25182 mm2 at the lightning energy of S4. Subsequently, as the energy increases, significant needle‐like damage is generated at the needle‐stitched area inside the sample, and the area of delamination damage gradually decreases. When the energy continues to increase to the lightning strike energy of S6, as the energy increases, the number of needle‐like damage in the sample continues to grow, and penetrating injury appears at the needle suture site. At S8, the maximum number of needle‐like lightning damage occurred, with 53 damage points and a total volume of 256.86 mm3, respectively. Through comprehensive analysis, Cf/SiC has excellent resistance to lightning damage, but the needle‐stitched carbon fiber in the thickness direction significantly reduces its resistance to lightning penetration. [ABSTRACT FROM AUTHOR]

Abstract Many efforts have been made by the scientific community to produce gridded datasets with high spatial resolution because they are essential for climate change assessment, impact studies, decision‐making, etc. This study fits into this context and describes the methods used to prepare a 5‐km gridded product of precipitation and minimum and maximum temperatures by merging observed data from meteorological stations, from 1981 to 2016, of Bangladesh, Nepal, and Pakistan with ERA5 reanalysis. The step‐by‐step methods for station data quality control and the development of the 5‐km gridded data are presented. Additionally, we use the 5‐km dataset to show the main climate features of the three countries, which facilitate comparison with other data sources in the literature.

The NE Tibet experienced complex and distinct basin developments and uplifts in different areas. However, the reasons for such distinct surface deformation and their relationship to deep crustal geodynamic processes are not well understood. Here, we obtain a crust model of NE Tibet by jointly inverting Rayleigh wave ellipticity and phase velocity. Our results reveal that deep crustal strength contrasts across NE Tibet play an important role in controlling basin development. Extrusion of the significantly weak Qilian crust is obstructed by rigid Alxa block, resulting in deep foreland basin with dramatic topographic step. In contrast, the relatively weak crust of Longzhong absorbs outward extrusion of NE Tibet within a wide transition zone, leading to small intermontane basins. Furthermore, the systematic thinning of basins from north to south around the western Ordos Block demonstrates the tectonic transformation from extension to compression due to expansion of NE Tibet since the late Miocene. Plain Language Summary: In this study, we obtain a high‐resolution model of NE Tibet by jointly inverting Rayleigh wave phase velocity and ellipticity (the radial‐to‐vertical amplitude ratio), which provides complementary constraints to the shallow structure. Our results show a clear correlation between velocity variations in the deep crust and basin structures at the surface. We infer that the extrusion of mechanically weak mid‐to‐lower crust of Qilian is obstructed by the strong Alxa block, leading to the steep topography and deep foreland Hexi Basin. To the east, in contrast, the outward extrusion of Songpan‐Ganzi is absorbed in a wide range by the relatively weak Longzhong region, developing gently sloping topography and small intermontane basins. Furthermore, the significant structural differences of rift basins from north to south around the western Ordos likely result from tectonic regime transformation induced by the continuous expansion of NE Tibet since the late Miocene. Our results improve the understanding of how deep crustal geodynamic processes influence surface uplift and basin developments in the expanding NE Tibet. Key Points: A high‐resolution 3‐D crustal model of NE Tibet is obtained by joint inversion of Rayleigh wave ellipticity and phase dispersionWe infer that deep crust extrusion and strength differences across plateau boundaries control distinct surface deformation in NE TibetThe systematic thinning of basins in west Ordos indicates tectonic regime transformation due to expansion of Tibet since Miocene [ABSTRACT FROM AUTHOR]

Self‐powered visualized sensor with skin‐like functionality is highly anticipated for practical application in health monitoring, human‐machine interaction, and robotics. However, the existing methods are disadvantaged by their poor performance in sensitivity and spatial fidelity due to the low pixel density, brightness, or conformability. Herein, a novel self‐powered skin‐inspired visualized sensor (SP‐SIVS) is developed by means of triboelectrification‐induced electroluminescence (TIEL) technique. By integrating surface and bulk charge generation with a high‐voltage corona charging treatment, a significant improvement is achieved in the brightness (by 10‐fold) and spatial resolution (100 µm) of the SP‐SIVS. Meanwhile, Young's modulus (94 kPa) is maintained at a level comparable to human skin, with tensile strength and elongation exceeding 1000 kPa and 1000%, respectively. Furthermore, SP‐SIVS is verified as applicable to serve such purposes as wearable motion detection, fingerprint morphology imaging, and information interaction. This work contributes an innovative framework to the development of SP‐SIVS with high‐brightness, high‐resolution, and sustainability, laying a foundation for its popularization as a novel interactive medium required for advanced photonic applications. [ABSTRACT FROM AUTHOR]

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Background: Magnetic particle imaging (MPI) is a recently developed, non‐invasive in vivo imaging technique to map the spatial distribution of superparamagnetic iron oxide nanoparticles (SPIONs) in animal tissues with high sensitivity and speed. It is a challenge to reconstruct images directly from the received signals of MPI device due to the complex physical behavior of the nanoparticles. System matrix and X‐space are two commonly used MPI reconstruction methods, where the former is extremely time‐consuming and the latter usually produces blurry images. Purpose: Currently, we proposed an end‐to‐end machine learning framework to reconstruct high‐resolution MPI images from 1‐D voltage signals directly and efficiently. Methods: The proposed framework, which we termed "MPIGAN", was trained on a large MPI simulation dataset containing 291 597 pairs of high‐resolution 2‐D phantom images and each image's corresponding voltage signals, so that it was able to accurately capture the nonlinear relationship between the spatial distribution of SPIONs and the received voltage signal, and realized high‐resolution MPI image reconstruction. Results: Experiment results showed that, MPIGAN exhibited remarkable abilities in high‐resolution MPI image reconstruction. MPIGAN outperformed the traditional methods of system matrix and X‐space in recovering the fine‐scale structure of magnetic nanoparticles' spatial distribution and achieving enhanced reconstruction performance in both visual effects and quantitative assessments. Moreover, even when the received signals were severely contaminated with noise, MPIGAN could still generate high‐quality MPI images. Conclusion: Our study provides a promising AI solution for end‐to‐end, efficient, and high‐resolution magnetic particle imaging reconstruction. [ABSTRACT FROM AUTHOR]

A method for enhancing the resolution of 3D imaging reconstruction by employing the polarization modulation of electro-optical crystals is proposed. This technique utilizes two polarizers oriented perpendicular to each other along with an electro-optical modulation crystal to achieve high repetition frequency and narrow pulse width gating. By varying the modulation time series of the electro-optical crystal, three-dimensional gray images of the laser at different distances are acquired, and the three-dimensional information of the target is reconstructed using the range energy recovery algorithm. This 3D imaging system can be implemented with large area detectors, independent of the an Intensified Charge-Coupled Device (ICCD) manufacturing process, resulting in improved lateral resolution. Experimental results demonstrate that when imaging a target at the distance of 20 m, the lateral resolution within the region of interest is 2560 × 2160, with a root mean square error of 3.2 cm. [ABSTRACT FROM AUTHOR]

Stratiform and convective precipitation are known to be associated with distinct isotopic fingerprints in the tropics. Such rain type specific isotope signals are of key importance for climate reconstructions derived from climate proxies (e.g., stable isotopes in tree rings). Recently, the relation between rain type and isotope signal in present‐day climate has been intensively discussed. While some studies point out the importance of deep convection, other studies emphasize the role of stratiform precipitation for strongly depleted isotope signals in precipitation. Uncertainties arise from observational studies due to data scarcity while modeling approaches with global climate models cannot explicitly resolve convective processes and rely on parameterizations. High‐resolution climate models are particularly important for studies over complex topography and for the simulation of convective cloud formation and organization. Therefore, we applied the isotope‐enabled version of the high‐resolution climate model from the Consortium for Small‐Scale Modeling (COSMOiso) over the Andes of tropical south Ecuador, South America, to investigate the influence of stratiform and convective rain on the stable oxygen isotope signal of precipitation (δ18OP). Our results highlight the importance of deep convection for depleting the isotopic signal of precipitation and increasing its deuterium excess. Due to the opposing effect of shallow and deep convection on the δ18OP signal, the use of a stratiform fraction might be misleading. We therefore propose to use a shallow and deep convective fraction to analyze the effect of rain types on δ18OP. Plain Language Summary: Tropical rainfall can be classified as convective and stratiform rain, which carry distinct fingerprints in their water isotope signal. This implies that climate reconstructions of convective activity can be made because the isotopic signal in precipitation is conserved in climate archives (e.g., tree rings). Contrasting results emerged from observations, due to data scarcity, and from global climate models, which have shortcomings due to coarse spatial and temporal resolutions. We addressed the question of the influence of different rain types on the isotopic signal of precipitation by using a high‐resolution, isotope‐enabled climate model over the tropical Andes. We found out that particularly deep convection leads to the most negative isotope signals, whereas stratiform rain and shallow convection are related to less negative, even slightly positive isotope values. Consequently, for analyzing the effect of rain types on the isotopic signal, we recommend to avoid using the stratiform fraction, which is misleading. Instead, we introduce a shallow and deep convective fraction, which are better predictors of the isotope signal of rain in the Andes. Key Points: Rain types (stratiform, shallow, and deep convection) are associated with distinct δ18O and deuterium excess signals in precipitationDeep convection leads to low δ18O and the highest deuterium excess anomalies in precipitationIn the Andes, δ18O of precipitation reflects the fraction of deep (or shallow) convective rain rather than the fraction of stratiform rain [ABSTRACT FROM AUTHOR]

The printed electronics (PEs) market has witnessed substantial growth, reaching a valuation of USD 10.47 billion in the previous year. Driven by its extensive use in a multitude of applications, this growth trend is expected to continue with a projected compound annual growth rate of 22.3% from 2022 to 2032. Compared to screen printing, the adoption of inkjet printing (IJP) technology to manufacture PEs has been limited to laboratory‐scale research only. The fact that IJP's inability to maintain consistent high‐resolution quality over large printing areas has made transitioning IJP for commercial production arduous. Most of the previous literatures have focused on holistic discussion on material design for IJP, but this review provides insight into key aspects in material processing up to printing optimization to realize high‐resolution PEs. This review also highlights the challenges in controlling the functional ink properties and their interaction with the substrate as well as printing parameters to deliver the desired quality of the droplets and final prints. Imminent application of IJP in PEs and future perspectives are also included in this review. [ABSTRACT FROM AUTHOR]

This paper introduces an approach to deep learning for improving quality for single high-definition (H.D) picture. The method immediately acquires an end-to-end map from low- and high definition pictures. High image resolution plays a significant role when enhancing the photo as well as frames for video in gadgets including smartphones as well as computers for Better Resolution. When upscaling the image frame, the image get distorted and blurred. A highdefinition image with greater clarity is produced by using a deep convolutional neural network which accepts a lowdefinition image as input and using Discrete Cosine Transformation to create blurred image from low resolution image and subtracting this blurred image from the result of network model. The experimental result shows that the proposed algorithm works better than the other compared algorithms. [ABSTRACT FROM AUTHOR]

Many efforts have been made by the scientific community to produce gridded datasets with high spatial resolution because they are essential for climate change assessment, impact studies, decision‐making, etc. This study fits into this context and describes the methods used to prepare a 5‐km gridded product of precipitation and minimum and maximum temperatures by merging observed data from meteorological stations, from 1981 to 2016, of Bangladesh, Nepal, and Pakistan with ERA5 reanalysis. The step‐by‐step methods for station data quality control and the development of the 5‐km gridded data are presented. Additionally, we use the 5‐km dataset to show the main climate features of the three countries, which facilitate comparison with other data sources in the literature. [ABSTRACT FROM AUTHOR]

We have designed a high-resolution magnetic field imaging system using 256 unpackaged Hall elements. These unpackaged Hall elements are arranged in a Hall linear array, and the distance between adjacent elements reaches 255 µm. The sensitivity of the unpackaged Hall element array can be adjusted using a computer to measure magnetic environments with different magnetic field strengths. High-resolution magnetic field images of 256 × 256 pixels can be generated by moving the array using the X–Y axis motorized rail. This spatial resolution can reach 99.61 pixels per inch (ppi). This rail allows for the spatial resolution of the system to be further increased to 199.22 ppi by using a special movement route. In the experiments, we employ this system to image magnetized metal scissors, and the result displays the structural features of the scissor surface. We also detected the magnetic suction wireless charging coil inside an Apple phone. The image obtained shows the shape of the coil and the gap between the magnets. The high-resolution magnetic imaging system displays the magnetic characteristics of the sample very well and easily obtains information about small-shaped structures and defects on the sample surface. This provides the system with potential in several fields such as quality inspection, security, biomedicine, and detection imaging. [ABSTRACT FROM AUTHOR]

Study region: The River Frome, a chalk stream in West Dorset, UK. Study focus: High-resolution hydrological models are required to integrate with the current wave of high-frequency data and advance our understanding of pollutant sources, pathways, and sinks. This presents several challenges in chalk streams, as their high-permeability and unique hydrogeological characteristics often leads to complex models that are overparameterized and computationally burdensome. In this article, we develop a novel and parsimonious modelling approach to describe the surface hydrology for a chalk stream in high resolution (15-minute frequency, ≤ 100 m cross-section spacing), using groundwater levels as a proxy for spring discharges. New hydrological insights for the region: Our results show that chalk stream dry-weather flows can be simulated accurately and parsimoniously at high-resolution (Nash-Sutcliffe efficiency = 0.97, mean relative error = 2.86 %, for a five-year period). We also show that spring discharges are the dominant form of flow accretion in all seasons and are critical to dilute sewage treatment inputs during the ecological growing season, whilst runoff and quick-flow pathways in the river valley corridor contribute a small proportion to annual flow accretion (< 5.2 %). Due to its simplicity and few parameters to calibrate, this modelling approach has broad applicability in lowland permeable catchments. Management implications include expeditious investigations of high-resolution freshwater quality responses to pollution and informing abstraction limits to sustain robust ecological conditions.

Common validation and verification test cases for compressible flow solvers are only one- or two-dimensional. Such flows, however, are inherently three-dimensional. The provided data contains simulation results of genuine three-dimensional Riemann problems computed with the open-source compressible flow solver ALPACA. The problems are designed so that each octant's constant initial state connects two neighboring states by only one elementary wave each. Thereby, initial conditions are chosen to induce three-dimensional effects. Furthermore, the cases are designed to trigger common shortcomings of compressible flow solvers, such as spurious pressure oscillations, unphysical symmetry breaking, or the onset of shock disturbances. The cases were simulated using a finite-volume scheme with HLLC and Roe Riemann solvers and fifth-order WENO reconstruction. The simulations were conducted on over 300 cores of a compute cluster. Besides the raw binary flow field data, input files are provided next to post-processing scripts and the visualizations obtained by them. The provided files ease setting up and simulating the respective cases with different solvers and allow quantitative comparisons of the obtained results.

For many applications, raw satellite observations need to be converted to high-level products of various essential environmental variables. While numerous products are available at kilometer spatial resolutions, there are few global products at high spatial resolutions (10–30 m), which are also referred to fine or medium resolutions in the literature. To facilitate the development of more high spatial resolution products, this paper systematically reviews the state-of-the-art progress on inversion algorithms and publicly available regional and global products. We begin with an inventory of available high-resolution satellite data, and then present different algorithms for determining cloud masks, estimating aerosol optical depth, and performing atmospheric correction and topographic correction for land surface reflectance retrieval. The majority of this paper reviews the inversion algorithms and existing regional to global products of 18 variables in four major categories: 1) Land surface radiation, including broadband albedo, land surface temperature, and all-wave net radiation; 2) Terrestrial ecosystem variables, including leaf area index, fraction of absorbed photosynthetically active radiation, fractional vegetation cover, fractional forest cover, tree height, forest above-ground biomass gross primary production, net primary production, and agricultural crop yield; 3) Water cycle and cryosphere, including soil moisture, evapotranspiration, and snow cover; and 4) Land surface types, such as global land cover, impervious surface, inland water, crop type, and fire. Since the existing products over large regions are usually spatially discontinuous due to cloud contamination, different data fusion and data assimilation algorithms and some products for producing spatially seamless and temporally continuous products are presented. In the end, we discuss a variety of challenges in generating global high spatial resolution satellite products.

ABSTRACTLand cover is an important variable for climate, hydrology, and ecology studies. With the availability of various high-resolution global land cover (GLC) products, conducting a comprehensive assessment on their accuracy and consistency is important. In this study, we compared the performance of three latest 10-m-resolution GLC products, which include FROMGLC10 in 2017, ESA’s Worldcover10 in 2020, and ESRIGLC10 in 2020, and three latest 30-m-resolution GLC products, which include FROMGLC30 in 2017, GLC_FCS30 in 2020, and Globeland30 in 2020, in China. The consistency of these products was investigated in terms of spatial consistency and area consistency. Though the six GLC products demonstrate similar overall distribution patterns, their detailed spatial distributions are quite different, especially for the three 10-m-resolution products. Evidently, the cropland, forest, grassland, and bareland exhibited high inconsistencies than the other types. The classification accuracy of the six GLC products was also quantitatively assessed based on a visual-interpretation-based reference dataset. FROMGLC10 exhibits the highest overall accuracy of 65.57%, followed by FROMGLC30 (64.96%) and Worldcover10 (62.74%). ESRIGLC10 (49.79%) exhibits the lowest accuracy. The accuracies of shrubland, wetland, and tundra were relatively low. This study provides a valuable reference for selecting appropriate GLC products for potential users.

ABSTRACTThe C-band Synthetic Aperture Radar (SAR) on board of the Sentinel-1 satellites have a strong potential to retrieve Surface Soil Moisture (SSM). Using a change detection model to Sentinel-1 backscatter, an SSM product at a kilometre scale resolution over Europe could be established in the Copernicus Global Land Service (CGLS). Over areas with dense vegetation and high biomass. The geometry and water content influence the seasonality of the backscatter dynamics and hamper the SSM retrieval quality from Sentinel-1. This study demonstrates the effect of woody vegetation on SSM retrievals and proposes a masking method at the native resolution of Sentinel-1’s Interferometric Wide (IW) swath mode. At a continental 20 m grid, four dense vegetation masks are implemented over Europe in the resampling of the backscatter to a kilometre scale. The resulting backscatter is then used as input for the TUWien (TUW) change detection model and compared to both in-situ and modelled SSM. This paper highlights the potential of high-resolution vegetation datasets to mask for non-soil moisture-sensitive pixels at a sub-kilometre resolution. Results show that both correlation and seasonality of the retrieved SSM are improved by masking the dense vegetation at a 20 m resolution.

The clinical magnetic resonance scanner (field strength ≤ 3.0 T) has limited efficacy in the high-resolution imaging of experimental mice. This study introduces a novel magnetic resonance micro-coil designed to enhance the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thereby improving high-resolution imaging in experimental mice using clinical magnetic resonance scanners. Initially, a phantom was utilized to determine the maximum spatial resolution achievable by the novel micro-coil. Subsequently, 12 C57BL/6JGpt mice were included in this study, and the novel micro-coil was employed for their scanning. A clinical flexible coil was selected for comparative analysis. The scanning methodologies for both coils were consistent. The imaging clarity, noise, and artifacts produced by the two coils on mouse tissues and organs were subjectively evaluated, while the SNR and CNR of the brain, spinal cord, and liver were objectively measured. Differences in the images produced by the two coils were compared. The results indicated that the maximum spatial resolution of the novel micro-coil was 0.2 mm. Furthermore, the subjective evaluation of the images obtained using the novel micro-coil was superior to that of the flexible coil (p < 0.05). The SNR and CNR measurements for the brain, spinal cord, and liver using the novel micro-coil were significantly higher than those obtained with the flexible coil (p < 0.001). Our study suggests that the novel micro-coil is highly effective in enhancing the image quality of clinical magnetic resonance scanners in experimental mice.

In this study, an integrated crystal plasticity and cellular automaton (CA) model has been developed and employed to investigate the processes of cold rolling and annealing in ferritic stainless steel. Crystal plasticity simulates the deformation microstructure, deformation texture and corresponding dislocation density distribution in the cold rolled ferritic stainless steel. Meanwhile, CA predicts the microstructural evolution originating from nucleation and growth. The resolution of the crystal plasticity model has been improved using a newly developed remeshing technique, which allows for the capture of the grain substructure in the deformation microstructure. The developed model has been validated by experimental measurements with electron backscatter diffraction (EBSD) in terms of comparing the orientation distribution functions (ODFs), misorientation, local substructures, and grain size. The good agreement between simulations and experiments indicates that the developed crystal plasticity with CA model is reliable and efficient for predicting the microstructure and texture evolutions during cold rolling and subsequent annealing.

With advances in liquid cell transmission electron microscopy (LCTEM), it is possible to real-time characterize nanomaterials at atomic resolution in a liquid environment. This provides a key tool for understanding the underlying mechanisms of various of (bio)chemical processes. In this review, various of strategies employed in high performance LCTEM characterization are introduced. It presents the structure and manufacturing processes of different liquid cells, and summarizes the methods to improve the spatial resolution and reduce the irradiation damage of LCTEM. It also describes the liquid cells integration with external energy field for observing the dynamic response of nanomaterials under electrical, thermal or irradiating stimuli. Then recent advances in LCTEM images and data automated analysis by machine learning are highlighted. Finally, we present a perspective on the challenges and future directions for the development of in situ LCTEM.

Radiopharmaceutical analysis is limited by conventional methods. Radio-HPLC may be inaccurate for some compounds (e.g., 18F-radiopharmaceuticals) due to radionuclide sequester. Radio-TLC is simpler, faster, and detects all species but has limited resolution. Imaging-based readout of TLC plates (e.g., using Cerenkov luminescence imaging) can improve readout resolution, but the underlying chromatographic separation efficiency may be insufficient to resolve chemically similar species such as product and precursor-derived impurities. This study applies a systematic mobile phase optimization method, PRISMA, to improve radio-TLC resolution. The PRISMA method optimizes the mobile phase by selecting the correct solvent, optimizing solvent polarity, and optimizing composition. Without prior knowledge of impurities and by simply observing the separation resolution between a radiopharmaceutical and its nearest radioactive or non-radioactive impurities (observed via UV imaging) for different mobile phases, the PRISMA method enabled the development of high-resolution separation conditions for a wide range of 18F-radiopharmaceuticals ( [18F]PBR-06, [18F]FEPPA, [18F]Fallypride, [18F]FPEB, and [18F]FDOPA). Each optimization required a single batch of crude radiopharmaceutical and a few hours. Interestingly, the optimized TLC method provided greater accuracy (compared to other published TLC methods) in determining the product abundance of one radiopharmaceutical studied in more depth ( [18F]Fallypride) and was capable of resolving a comparable number of species as isocratic radio-HPLC. We used the PRISMA-optimized mobile phase for [18F]FPEB in combination with multi-lane radio-TLC techniques to evaluate reaction performance during high-throughput synthesis optimization of [18F]FPEB. The PRISMA methodology, in combination with high-resolution radio-TLC readout, enables a rapid and systematic approach to achieving high-resolution and accurate analysis of radiopharmaceuticals without the need for radio-HPLC.

The challenge of detecting changes in high-resolution remote sensing imagery often stems from the difficulties in effectively extracting features and constructing appropriate change detection models considering the scale characteristics of ground objects. To solve these issues, we propose a novel UNet 3+ change detection method that considers the scale characteristics inherent in various land-cover change types. Our method includes three key steps: a multi-scale segmentation method, a class-specific UNet 3+ method, and an object-oriented change detection method based on UNet 3+. To verify the effectiveness of this method, we select two datasets for experiments and compare our proposed method with the UNet 3+ single-scale sampling method, the class-specific UNet 3+ single-scale sampling method, and the UNet 3+ multi-scale hierarchical sampling method. Our experimental results show that our proposed method has higher overall accuracy and F1, lower missed detection rate and false detection rate, and can detect more changes in ground features than other methods. To verify the scalability of this method, we compare this method with traditional change detection methods such as PCA-k-means, OCVA, a single-scale sampling method based on random forest, and a class-specific object-based method. Experimental results and accuracy indexes show that our proposed method better considers the scale characteristics of ground objects and achieves higher accuracy. Additionally, we compared our proposed method with other DLCD methods including LamboiseNet, BIT, CDNet, FCSiamConc, and FCSiamDiff. Our results show that our proposed method effectively considers edge information and has an acceptable time consumption. Our approach not only considers the full-scale characteristics of the feature extraction but also the scale characteristics of the change detection model. In addition, it considers a more practical feature extraction unit (object), making it more accurate. [ABSTRACT FROM AUTHOR]

Background: High‐resolution brain positron emission tomography (PET) scanner is emerging as a significant and transformative non‐invasive neuroimaging tool to advance neuroscience research as well as improve diagnosis and treatment in neurology and psychiatry. Time‐of‐flight (TOF) and depth‐of‐interaction (DOI) information provide markedly higher PET imaging performance by increasing image signal‐to‐noise ratio and mitigating spatial resolution degradation due to parallax error, respectively. PET detector modules that utilize light sharing can inherently carry DOI information from the multiple timestamps that are generated per gamma event. The difference between two timestamps that are triggered by scintillation photons traveling in opposite directions signifies the event's depth‐dependent optical photon TOF (oTOF). However, light leak at the crystal‐readout interface substantially degrades the resolution of this oTOF‐based depth encoding. Purpose: We demonstrate the feasibility of oTOF‐based depth encoding by mitigating light leak in single‐ended‐readout Prism‐PET detector modules using tapered crystals. Minimizing light leak also improved both energy‐based DOI and coincidence timing resolutions. Methods: The tapered Prism‐PET module consists of a 16 ×$\times$ 16 array of 1.5 ×$\times$ 1.5 ×$\times$ 20 mm3${\rm {mm}}^3$ lutetium yttrium oxyorthosillicate (LYSO) crystals, which are tapered down to 1.2 ×$\times$ 1.2 mm2${\rm {mm}}^2$ at the crystal‐readout interface. The LYSO array couples 4‐to‐1 to an 8 ×$\times$ 8 array of 3 ×$\times$ 3 mm2${\rm {mm}}^2$ silicon photomultiplier (SiPM) pixels on the tapered end and to a segmented prismatoid light guide array on the opposite end. Performance of tapered and non‐tapered Prism‐PET detectors was experimentally characterized and evaluated by measuring flood histogram, energy resolution, energy‐, and oTOF‐based DOI resolutions, and coincidence timing resolution. Sensitivities of scanners using different Prism‐PET detector designs were simulated using Geant4 application for tomographic emission (GATE). Results: For the tapered (non‐tapered) Prism‐PET module, the measured full width at half maximum (FWHM) energy, timing, energy‐based DOI, and oTOF‐based DOI resolutions were 8.88 (11.18)%, 243 (286) ps, 2.35 (3.18) mm, and 5.42 (13.87) mm, respectively. The scanner sensitivities using non‐tapered and tapered crystals, and 10 rings of detector modules, were simulated to be 30.9 and 29.5 kcps/MBq, respectively. Conclusions: The tapered Prism‐PET module with minimized light leak enabled the first experimental report of oTOF‐based depth encoding at the detector module level. It also enabled the utilization of thinner (i.e., 0.1 mm) inter‐crystal spacing with barium sulfate as the reflector while also improving energy‐based DOI and timing resolutions. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are projected to increase in frequency across much of the land‐surface as the global climate warms, but such projections have typically relied on coarse‐resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land‐surface might enhance the fidelity of the simulated contribution of large‐magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large‐storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station‐observed daily precipitation (4,803 GHCN‐D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse‐resolution simulations produce less large‐storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large‐storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large‐storm dominance across multiple sub‐regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large‐storm dominance. Plain Language Summary: As global temperatures rise, there's growing concern about an increase in the frequency of extreme precipitation events. However, projections of future changes to large‐magnitude precipitation events often rely on coarse‐resolution models which simulate meteorology on a spatial scale larger than most storms. To address this, we explored the potential of higher resolution models from the Coupled Model Intercomparison Project HighResMIP ensemble to improve the fidelity of simulated precipitation variability across the western United States. We compared four HighResMIP models with their coarse‐resolution counterparts from 1950 to 2014 and found that coarse‐resolution models consistently underestimated the dominance of large storms compared to observed data. While some HighResMIP models showed improvement, others remained unchanged or even showed increased bias. This suggests that besides spatial resolution alone, factors like model structure and numerical representation of fine scale precipitation processes play a role in accurately simulating intense storms. These findings highlight the importance of considering both resolution and climate model structure in predicting future extreme precipitation events, especially in regions where precipitation is generated by processes that operate on a wide range of spatial scales. Key Points: Increasing spatial resolution of general circulation models (GCMs) does not consistently improve simulation of large‐storm dominance across the western United StatesCoarse resolution GCMs simulate large‐storm dominance well in regions where the largest daily precipitation events are due to synoptic scale precipitationModel structure and parameterization of precipitation generation appear to be equally as important as resolution in the simulation of large‐storm dominance [ABSTRACT FROM AUTHOR]

The clinical magnetic resonance scanner (field strength ≤ 3.0 T) has limited efficacy in the high-resolution imaging of experimental mice. This study introduces a novel magnetic resonance micro-coil designed to enhance the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), thereby improving high-resolution imaging in experimental mice using clinical magnetic resonance scanners. Initially, a phantom was utilized to determine the maximum spatial resolution achievable by the novel micro-coil. Subsequently, 12 C57BL/6JGpt mice were included in this study, and the novel micro-coil was employed for their scanning. A clinical flexible coil was selected for comparative analysis. The scanning methodologies for both coils were consistent. The imaging clarity, noise, and artifacts produced by the two coils on mouse tissues and organs were subjectively evaluated, while the SNR and CNR of the brain, spinal cord, and liver were objectively measured. Differences in the images produced by the two coils were compared. The results indicated that the maximum spatial resolution of the novel micro-coil was 0.2 mm. Furthermore, the subjective evaluation of the images obtained using the novel micro-coil was superior to that of the flexible coil (p < 0.05). The SNR and CNR measurements for the brain, spinal cord, and liver using the novel micro-coil were significantly higher than those obtained with the flexible coil (p < 0.001). Our study suggests that the novel micro-coil is highly effective in enhancing the image quality of clinical magnetic resonance scanners in experimental mice. [ABSTRACT FROM AUTHOR]

The rapid and disordered expansion of artificial marine aquaculture areas has caused severe ecological and environmental problems. Accurate monitoring of offshore aquaculture areas is urgent and significant in order to support the scientific and sustainable management and protection of coastal marine resources. Artificial intelligence provides a valuable tool to improve marine resource monitoring. Deep learning methods have been widely used for marine object detection, but You Only Look Once (YOLO) models have not been employed for offshore aquaculture area monitoring. This study therefore evaluated the capacity of two well-known YOLO models, YOLOv5 and YOLOv7, to detect offshore aquaculture areas based on different high-resolution optical remote sensing imagery. Compared with YOLOv7 based on a satellite dataset, YOLOv5 increased the Precision value by approximately 3.29% (to 95.33%), Recall value by 3.02% (to 93.02%), mAP_0.5 by 2.03% (to 96.22%), and F1 score by 2.65% (to 94.16%). Based on the Google Earth dataset, YOLOv5 and YOLOv7 showed similar results. We found that the spatial resolution could affect the deep learning models' performances. We used the Real-ESRGAN method to enhance the spatial resolution of satellite dataset and investigated whether super-resolution (SR) methods improved the detection accuracy of the YOLO models. The results indicated that despite improving the image clarity and resolution, the SR methods negatively affected the performance of the YOLO models for offshore aquaculture object detection. This suggests that attention should be paid to the use of SR methods before the application of deep learning models for object detection using remote sensing imagery. [ABSTRACT FROM AUTHOR]