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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]

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]

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]

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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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]

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]

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 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.

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]

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]

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]

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]

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]

With the goal of eradication by 2030, Malaria poses a significant health threat, profoundly influenced by meteorological and hydrological conditions. In support of malaria vector control efforts, we present a high‐resolution, coupled physically‐based modeling approach integrating WRF‐Hydro and VECTRI. This model approach accurately captures topographic details at the scale of larvae habitats in the Nouna Health and Demographic Surveillance Systems in Sub‐Saharan Africa. Our study demonstrates the proficiency of the high‐resolution hydrometeorological model, WRF‐Hydro, in replicating observed climate characteristics. Comparisons with in‐situ local weather data reveal root mean square errors between 0.6 and 0.87 mm/day for rainfall and correlations ranging from 0.79 to 0.87 for temperatures. Additionally, WRF‐Hydro's surface hydrology reproduces the seasonal and intraseasonal variability of the ponded water fraction with 96% accuracy, validated against Sentinel‐1 data at a 100‐m resolution. The VECTRI model demonstrates sensitivity to surface hydrology representation, particularly when comparing conceptual and detailed physical process models, for variables such as larvae density, mosquito abundance, and EIR. The model's ability to replicate the seasonality of malaria transmission aligns well with available cohort malaria data suggesting its potential for predicting the impacts of climate change on mosquito abundance and transmission intensity in endemic tropical and subtropical zones. This integrated approach opens avenues for enhanced understanding and proactive management of malaria. Key Points: A coupled atmosphere‐hydrology‐malaria transmission model system consisting of WRF‐Hydro and VECTRI is developedCoupled WRF‐Hydro‐VECTRI scheme reproduces the peak of malaria as well as the seasonal variabilityVECTRI is sensitive to the representation of mosquito breeding habitat avail‐ability [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]

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]

High‐resolution (HR) displays are in much demand as metaverse makes near‐eye displays the most important equipment in recent years. Based on wave optics, a microscale optical crosstalk model for HR display devices is proposed. It is indicated that the pixel pattern will be distorted over long‐distance transmission in light‐emitting devices with inner periodic microstructure, and the optical distortion is related to the size of pixels and the distance from the emitting layer to the outlet. A bottom emissive HR red quantum dot light‐emitting diode (QLED) array is introduced to confirm the model and a top emission scheme is provided to effectively reduce the transmission distance and suppress the pixel distortion. Optical‐crosstalk‐free pixels are finally achieved by adopting the optimized top‐emission cathode thickness of 30 nm. This study provides a direction for realizing high‐quality and high‐resolution micro‐displays. [ABSTRACT FROM AUTHOR]

The strong drying expected in the Eastern Mediterranean with climate change could cause mass migration of people already living under water shortages. On the other hand, precipitation is expected to increase toward the region's north, particularly those along the interior of the eastern Black Sea coasts, which could worsen existing floods. However, this double‐sided adverse phenomenon and its underlying reasons have been investigated by relatively low‐resolution models in the Eastern Mediterranean Black Sea (EMBS) region, where orographic precipitation prevails. This study performs 4 km resolution Weather Research and Forecasting (WRF) model simulations with and without climate change signals retrieved from a CMIP6 GCM ensemble via pseudo‐global‐warming (PGW). The WRF simulations captured the large‐scale dynamics affecting the EMBS fairly well: an anticyclonic low‐level circulation and enhancing subsidence stem from anomalous ridge development over the central Mediterranean in winter (DJF), and a cyclonic low‐level circulation and weakening subsidence rise from heat‐low development over the Eastern Mediterranean in summer (JJA). The resulting picture of future warming and drying over the area generally supports the literature, although new insights emerge in anomalous precipitation increase, especially in the summer season over the Greater Caucasus and nearby regions. Most likely, the warmer‐than‐expected Caspian Sea induces a large increase in specific humidity and, thus, a large moisture source in the lower troposphere and an extension of the heat‐low effect in the mid‐troposphere. In addition, the high‐resolution WRF simulations provide added value over the complex topography of the Caucasian Mountain range for new insights into this region. Plain Language Summary: One of the most vulnerable parts of the Mediterranean climate change hotspot is the Eastern Mediterranean Black Sea (EMBS) region. The future climate change signal of the state‐of‐the‐art CMIP6 GCM ensemble between 2071–2100 and 1985–2014 periods was retrieved for the region under the Shared Socioeconomic Pathway (SSP5‐8.5). The signal was used to perturb ERA5 reanalysis data for the IPCC AR6 baseline period, 1995–2014, in the context of the pseudo‐global warming (PGW) method. The Weather Research and Forecasting (WRF) model was used to obtain very high‐resolution (4 km) climate data. According to the study, the future winters were as expected: anomalous ridge formation over southern Italy, resultant northeasterlies over the EMBS, the large‐scale mid‐ and upper tropospheric subsidence, attenuation of the land‐sea temperature contrast and finally, strong drying. The summers exhibit negative vertical pressure velocity changes, strengthening Etesians, and strong surface warming in line with the expectations of the literature. However, summers brought anomalous precipitation to the Greater Caucasus and nearby areas in our high‐resolution WRF simulations. We think these new insights are primarily caused by the warmer‐than‐expected Caspian Sea stemming from the SST availability among the ensemble, a very convective environment for the region and better‐resolved complex‐orography‐induced precipitation. Key Points: The WRF‐PGW simulations perfectly capture the low‐level circulation change and warming/drying signals from CMIP6 for the EMBS in winterThe WRF‐PGW simulations show heat‐low formation over the EMBS in summer, in line with the CMIP6 GCM ensembleThe new insights appear in future summer precipitation increases over the Greater Caucasus and nearby regions [ABSTRACT FROM AUTHOR]

In this study, a novel variable-stiffness structure (VSS) featuring temperature-dependent characteristics is introduced for the optical payload of the Jilin1-GF04X satellite, which has an image resolution of 0.3 m. The VSS, constructed from polyurethane material, was engineered to operate at room temperature (22 °C) during the rocket launch phase in order to withstand adverse dynamic conditions. Additionally, the structure was designed to function at a higher temperature (35 °C) while in orbit to improve the efficiency of microvibration isolation. The assessment of the suppression capabilities of the presented vibration isolator, as compared to the present carbon fiber structure, was conducted through an integrated analysis. Ground-based microvibration tests were subsequently carried out to evaluate the capacity of the VSS for adjusting its stiffness and damping properties. The results showed that the maximum amplitude of pixel offset reduced by 27.6% at 35 °C compared to 22 °C, confirming the efficacy of the structure. • A variable-stiffness structure (VSS) featuring temperature-dependent characteristics is proposed. • The dynamic properties of polyurethane materials are characterized using the fractional derivative constitutive equation. • The integrated model is established by the optical model, structural model, control model of the satellite and the perturbations of the flywheel. • Ground-based microvibration tests are carried out to evaluate the capacity of the VSS for adjusting its stiffness and damping properties. [ABSTRACT FROM AUTHOR]

Ultrasound switchable fluorescence (USF) imaging, a hybrid imaging technology that combines the advantages of both fluorescence sensitivity and acoustic resolution in centimeter‐deep tissue, has great potential for biomedical different applications. A camera‐based USF imaging system reveals its capability of capturing both spatial and temporal dynamics of the USF signal in tissue. In this study, various algorithms were explored to enhance the spatial resolution and signal‐to‐noise ratio (SNR) of USF images, utilizing temporal and spatial information from a camera‐based time‐domain USF imaging system. The correlation method proved effective in boosting SNR, while the ascending‐slope‐weighted method enhanced spatial resolution. Additionally, the spatially back‐projection method significantly improved spatial resolution in silicone phantoms. The results underscore the advantages of incorporating temporal and spatial information from USF signals. [ABSTRACT FROM AUTHOR]

At high latitudes, submesoscale dynamics act on scales of O $\mathcal{O}$(100 m–1 km) and are associated with the breakdown of geostrophic balance, vertical velocities, and energy cascading to small scales. Submesoscale features such as fronts, filaments, and eddies are ubiquitous in marginal ice zones forced by the large horizontal density gradients. In July 2020, we identified multiple fronts and filaments using a towed undulating vehicle near the sea ice edge in central Fram Strait, the oceanic gateway to the Arctic Ocean between Greenland and Svalbard. Sea ice covered the entire study region 1–2 weeks earlier, and a stratified meltwater layer was present. We observed a front between warm and saline Atlantic Water (AW) and cold and fresh Polar Water (PW) at 30–85 m depth, where we identified a subsurface maximum in chlorophyll fluorescence and other biogeochemical properties extending along the tilted isopycnals down to 75 m, indicating subduction of AW (mixed with meltwater) that had previously occurred. The meltwater layer also featured multiple shallow fronts, one of which exhibited high velocities and a subsurface maximum in chlorophyll fluorescence, possibly indicating subduction of PW below the meltwater layer. The fronts at different depth levels suggest a stepwise subduction process near the ice edge, where water subducts from the surface below the meltwater and then further down along subsurface fronts. The submesoscale features were part of a larger‐scale mesoscale pattern in the marginal ice zone. As sea ice continuously retreats, such features may become more common in the Arctic Ocean. Plain Language Summary: Submesoscale dynamics are small‐scale (100 m–1 km) horizontal and vertical flows that create fronts and whirls. These features are typical in the transition from open ocean to sea ice. In the summer of 2020, we observed multiple fronts near the sea ice edge in central Fram Strait. Fram Strait is the oceanic gateway between the Atlantic Ocean and the Arctic Ocean. At the time, fresh meltwater covered the area. Below the meltwater layer, we observed a front between warm and saline Atlantic Water (AW) and cold and fresh Polar Water (PW). The amount of fluorescence (a measure of biomass) observed below the PW showed that AW moved downwards. Prior observations show that AW is in touch with the sea surface in the eastern part of Fram Strait. Our observations thus suggest a step‐by‐step process happening near the ice edge. AW moves from the surface below the meltwater in the first step and down below PW in the second step. This process can increase the vertical transport of biological material (i.e., carbon) near the ice edge in summer. The vertical transport is part of the global carbon cycle. Key Points: We present a study of temporally and spatially highly resolved observations of a front system near the ice edgeWe observe no mixed layer and the ocean is stratified up to the surfaceWe propose that subduction occurs stepwise along surface meltwater fronts and subsurface fronts separating Polar Water and Atlantic Water [ABSTRACT FROM AUTHOR]

Hippocampal atrophy (tissue loss) has become a fundamental outcome parameter in clinical trials on Alzheimer's disease. To accurately estimate hippocampus volume and track its volume loss, a robust and reliable segmentation is essential. Manual hippocampus segmentation is considered the gold standard but is extensive, time-consuming, and prone to rater bias. Therefore, it is often replaced by automated programs like FreeSurfer, one of the most commonly used tools in clinical research. Recently, deep learning-based methods have also been successfully applied to hippocampus segmentation. The basis of all approaches are clinically used T1-weighted whole-brain MR images with approximately 1 mm isotropic resolution. However, such T1 images show low contrast-to-noise ratios (CNRs), particularly for many hippocampal substructures, limiting delineation reliability. To overcome these limitations, high-resolution T2-weighted scans are suggested for better visualization and delineation, as they show higher CNRs and usually allow for higher resolutions. Unfortunately, such time-consuming T2-weighted sequences are not feasible in a clinical routine.We propose an automated hippocampus segmentation pipeline leveraging deep learning with T2-weighted MR images for enhanced hippocampus segmentation of clinical T1-weighted images based on a series of 3D convolutional neural networks and a specifically acquired multi-contrast dataset. This dataset consists of corresponding pairs of T1- and high-resolution T2-weighted images, with the T2 images only used to create more accurate manual ground truth annotations and to train the segmentation network. The T2-based ground truth labels were also used to evaluate all experiments by comparing the masks visually and by various quantitative measures.We compared our approach with four established state-of-the-art hippocampus segmentation algorithms (FreeSurfer, ASHS, HippoDeep, HippMapp3r) and demonstrated a superior segmentation performance. Moreover, we found that the automated segmentation of T1-weighted images benefits from the T2-based ground truth data.In conclusion, this work showed the beneficial use of high-resolution, T2-based ground truth data for training an automated, deep learning-based hippocampus segmentation and provides the basis for a reliable estimation of hippocampal atrophy in clinical studies.

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.

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.

We present a variable‐resolution global chemistry‐climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban‐to‐rural chemical regimes, mesoscale convective systems, and land‐surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi‐decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25‐km uniform‐resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal‐to‐diurnal cycles and extremes, notably reducing the central US dry‐and‐warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process‐level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land‐biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate. Plain Language Summary: NOAA's Geophysical Fluid Dynamics Laboratory has developed a new variable‐resolution global chemistry‐climate model for research at the nexus of US climate and air quality extremes. In contrast with the global models contributing to the latest Intergovernmental Panel on Climate Change Report, this model features more than 10 times finer spatial resolution over the contiguous US, allowing it to better resolve cities, mountain valleys, thunderstorms, and urban‐to‐rural air quality variations. This model features much improved representation of regional rainfall extremes, drought, and severe air pollution events in diverse US air basins, including California. Notably, this model reduces the central US dry‐and‐warm bias that has persisted in many generations of climate models. As global climate change leads to more hot and dry weather, the resulting droughts are creating dust‐prone bare lands or stressing plants, making them less able to remove ozone pollution from the air. These effects are included in this model, with particular focus on integrating physical, chemical, and biological components at high spatial resolution to understand Earth system feedbacks to US air quality extremes in a changing climate. Key Points: A new variable‐resolution global chemistry‐climate model has been developed for research at the nexus of US climate and air quality extremesThis model unifies component advances in physics, chemistry and land‐atmosphere interactions within a seamless variable‐resolution frameworkThis model features much improved US regional precipitation, drought, and air quality extremes compared to previous models [ABSTRACT FROM AUTHOR]

Owing to the increasing challenges associated with coal mine exploration and development, extremely precise surveys with high-resolution images are required to support production. Conventional inversion methods cannot provide sufficiently precise images of the complex lithologies in coal measure strata. Accordingly, this study performed research in Qiyuan mining area, Shanxi Province, China, and predicted the complex lithology on the basis of facies control using waveform indication inversion and waveform indication simulation. Horizontal changes in seismic waveforms were used to reflect lithologic assemblage characteristics for facies-controlled constraints, and the vertical mapping the connection between seismic waveform and logging curves was shown. Moreover, high-resolution inversions of wave impedance and natural gamma parameters were conducted. Combined with lithologic shielding and accurate time–depth conversion, the inversions enabled the precise characterization of the lithological assemblage distribution in the study area. Our results showed that waveform indication inversion could distinguish between coal seams and limestone, whereas waveform indication simulation based on natural gamma could effectively distinguish between sandstone and mudstone. Furthermore, the horizontal resolution was improved and the vertical resolution extended to a thickness of 2–3 m. In addition, the inversion results were highly consistent with drilling results, with an error <0.1 m. Therefore, waveform indication inversion and simulation could be applied to coal mines for safe and efficient production. [ABSTRACT FROM AUTHOR]

This paper presents an innovative common voltage VCOM compensation solution aimed at improving H‐Crosstalk in high‐resolution, high‐refresh‐rate VA products. The proposed approach involves the utilization of TFT VCOM feedback for direct compensation on the CF VCOM, while concurrently using the coupling effect between CF and TFT to indirectly compensate TFT VCOM. [ABSTRACT FROM AUTHOR]

In actual data processing, the parabolic Radon transform is a commonly used method to suppress multiples. However, it is difficult to suppress those multiples whose curvatures are very similar to that of the primaries using the conventional Radon transform. This paper gives a multiple-suppression method with curvature amplification to improve the multiple-suppression effect. By studying the parameters that affect the curvature of the event, it is found that compressing the offset or increasing the time difference can amplify the difference between the curvatures of the multiples and primaries, and the separation degrees of the multiples and primaries in the Radon domain are increased, so the multiple-suppression ability is improved. To improve the computational efficiency and accuracy of the Radon transform, the λ-f domain high-resolution parabolic Radon transform is used to eliminate the frequency dependence of the conventional Radon transform operator by introducing a new variable λ. Because the new transform operator and its inverse operator need to be calculated only once, the computational efficiency is significantly improved. The theoretical model data and the actual data processing show that the method can suppress the surface multiples well, and it also has a certain ability to suppress the internal multiples that have a particular curvature difference from the primaries. [ABSTRACT FROM AUTHOR]

Since the first demonstration in the early 1990s, functional MRI (fMRI) has emerged as one of the most powerful, noninvasive neuroimaging tools to probe brain functions. Subsequently, fMRI techniques have advanced remarkably, enabling the acquisition of functional signals with a submillimeter voxel size. This innovation has opened the possibility of investigating subcortical neural activities with respect to the cortical depths or cortical columns. For this purpose, numerous previous works have endeavored to design suitable functional contrast mechanisms and dedicated imaging techniques. Depending on the choice of the functional contrast, functional signals can be detected with high sensitivity or with improved spatial specificity to the actual activation site, and the pertaining issues have been discussed in a number of earlier works. This review paper primarily aims to provide an overview of the subcortical fMRI techniques that allow the acquisition of functional signals with a submillimeter resolution. Here, the advantages and disadvantages of the imaging techniques will be described and compared. We also summarize supplementary imaging techniques that assist in the analysis of the subcortical brain activation for more accurate mapping with reduced geometric deformation. This review suggests that there is no single universally accepted method as the gold standard for subcortical fMRI. Instead, the functional contrast and the corresponding readout imaging technique should be carefully determined depending on the purpose of the study. Due to the technical limitations of current fMRI techniques, most subcortical fMRI studies have only targeted partial brain regions. As a future prospect, the spatiotemporal resolution of fMRI will be pushed to satisfy the community's need for a deeper understanding of whole‐brain functions and the underlying connectivity in order to achieve the ultimate goal of a time‐resolved and layer‐specific spatial scale. Evidence Level: 1 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

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Despite the improvements in the quality of digital images brought about by the development of digital cameras and smartphones, taking clean photographs remains a challenge due to the presence of noise such as moiré patterns, especially for high-resolution images commonly used in today's real life. To more efficiently and accurately identify and remove moirés in high-resolution images, this paper proposes an improved network based on the high-resolution network HRDN. We conducted experiments on the FHDMi dataset to evaluate the effectiveness of our method. The results demonstrate that the improved algorithm significantly enhances the performance of moiré detection in high-resolution images, making it better suited to meeting the demands of real-world applications. [ABSTRACT FROM AUTHOR]