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Summary: As various algorithms and models spring up, object detection‐based deep learning for image analysis has become an established technology in the field of computer vision. Furthermore, object detection for high‐resolution videos derived from ubiquitous surveillance cameras draws many researchers' attention due to its practical significance and the challenge on detecting accuracy and speed. Existing object detection methods mainly resize and compress each frame in the video and then apply the detection algorithm, but with the method, the detection for small objects in dense scenes is far from satisfactory in terms of accuracy because small objects are reduced to pixel‐level sizes in compressed frame images. In order to fix this issue, we propose AROD, a parallel detection framework based on adaptive image cropping. Unlike the traditional first‐compression‐and‐then‐detection methods, AROD adopts adaptive image cropping for distributed parallel detection. In this way, AROD significantly improves the accuracy of small object detection in dense scenes with acceptable overhead. In our experimental evaluation, YOLOv2‐tiny model equipped with AROD reaches 29.29 FPS along with high detection accuracy. Experimental results verify that AROD ensures the high throughput of real‐time video analytics while maintaining high detection accuracy. [ABSTRACT FROM AUTHOR]

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-h time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected "virtual buoy" locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications. • 42-year regional hindcast datasets were generated for wave resource characterization in the U.S. • The hindcast datasets cover the entire U.S. Exclusive Economic Zone with 200 m resolution nearshore. • Spatial and seasonal variability of wave climate and resource was analyzed for different regions. • Aggregated resource statistics show the West Coast has the highest wave energy and the Gulf of Mexico the smallest. • Datasets are publicly disseminated through an Amazon Web Service open-data registry. [ABSTRACT FROM AUTHOR]

With the goal of developing a total-body small-animal PET system with a high spatial resolution of ∼0.5 mm and a high sensitivity >10% for mouse/rat studies, we simulated four scanners using the graphical processing unit-based Monte Carlo simulation package (gPET) and compared their performance in terms of spatial resolution and sensitivity. We also investigated the effect of depth-of-interaction (DOI) resolution on the spatial resolution. All the scanners are built upon 128 DOI encoding dual-ended readout detectors with lutetium yttrium oxyorthosilicate (LYSO) arrays arranged in 8 detector rings. The solid angle coverages of the four scanners are all ∼0.85 steradians. Each LYSO element has a cross-section of 0.44 × 0.44 mm2 and the pitch size of the LYSO arrays are all 0.5 mm. The four scanners can be divided into two groups: (1) H2RS110-C10 and H2RS110-C20 with 40 × 40 LYSO arrays, a ring diameter of 110 mm and axial length of 167 mm, and (2) H2RS160-C10 and H2RS160-C20 with 60 × 60 LYSO arrays, a diameter of 160 mm and axial length of 254 mm. C10 and C20 denote the crystal thickness of 10 and 20 mm, respectively. The simulation results show that all scanners have a spatial resolution better than 0.5 mm at the center of the field-of-view (FOV). The radial resolution strongly depends on the DOI resolution and radial offset, but not the axial resolution and tangential resolution. Comparing the C10 and C20 designs, the former provides better resolution, especially at positions away from the center of the FOV, whereas the latter has 2× higher sensitivity (∼10% versus ∼20%). This simulation study provides evidence that the 110 mm systems are a good choice for total-body mouse studies at a lower cost, whereas the 160 mm systems are suited for both total-body mouse and rat studies.

PURPOSEInterstitial lung disease (ILD) accounts for a significant proportion of mortality and morbidity in patients with rheumatoid arthritis (RA). The aim of this cross-sectional study is to evaluate the performance of novel photon-counting detector computed tomography (PCD-CT) in the detection of pulmonary parenchymal involvement.METHODSSixty-one patients with RA without a previous definitive diagnosis of ILD underwent high-resolution (HR) (0.4 mm slice thickness) and ultra-high-resolution (UHR) (0.2 mm slice thickness) PCDCT examination. The extent of interstitial abnormalities [ground-glass opacity (GGO), reticulation, bronchiectasis, and honeycombing] were scored in each lobe using a Likert-type scale. Total ILD scores were calculated as the sum of scores from all lobes.RESULTSReticulation and bronchiectasis scores were higher in the UHR measurements taken compared with the HR protocol [median (quartile 1, quartile 3): 2 (0, 3.5) vs. 0 (0, 3), P < 0.001 and 2 (0, 2) vs. 0 (0, 2), P < 0.001, respectively]; however, GGO and honeycombing scores did not differ [2 (2, 4) vs. 2 (2, 4), P = 0.944 and 0 (0, 0) vs. 0 (0, 0), P = 0.641, respectively]. Total ILD scores from both HR and UHR scans showed a mild negative correlation in diffusion capacity for carbon monoxide (HR: r = –0.297, P = 0.034; UHR: r = –0.294, P = 0.036). The pattern of lung parenchymal involvement did not differ significantly between the two protocols. The HR protocol had significantly lower volume CT dose index [0.67 (0.69, 1.06) mGy], total dose length product [29 (24.48, 33.2) mGy*cm] compared with UHR scans [8.18 (6.80, 9.23) mGy, P < 0.001 and 250 (218, 305) mGy*cm, P < 0.001].CONCLUSIONUHR PCD-CT provides more detailed information on ILD in patients with RA than low-dose HR PCDCT. HR PCD-CT image acquisition with a low effective radiation dose may serve as a valuable, low-radiation screening tool in the selection of patients for further, higher-dose UHR PCD-CT screening.

Abstract Background Fundus fluorescein angiography (FA) can be used to diagnose fundus diseases by observing dynamic fluorescein changes that reflect vascular circulation in the fundus. As FA may pose a risk to patients, generative adversarial networks have been used to convert retinal fundus images into fluorescein angiography images. However, the available methods focus on generating FA images of a single phase, and the resolution of the generated FA images is low, being unsuitable for accurately diagnosing fundus diseases. Methods We propose a network that generates multi-frame high-resolution FA images. This network consists of a low-resolution GAN (LrGAN) and a high-resolution GAN (HrGAN), where LrGAN generates low-resolution and full-size FA images with global intensity information, HrGAN takes the FA images generated by LrGAN as input to generate multi-frame high-resolution FA patches. Finally, the FA patches are merged into full-size FA images. Results Our approach combines supervised and unsupervised learning methods and achieves better quantitative and qualitative results than using either method alone. Structural similarity (SSIM), normalized cross-correlation (NCC) and peak signal-to-noise ratio (PSNR) were used as quantitative metrics to evaluate the performance of the proposed method. The experimental results show that our method achieves better quantitative results with structural similarity of 0.7126, normalized cross-correlation of 0.6799, and peak signal-to-noise ratio of 15.77. In addition, ablation experiments also demonstrate that using a shared encoder and residual channel attention module in HrGAN is helpful for the generation of high-resolution images. Conclusions Overall, our method has higher performance for generating retinal vessel details and leaky structures in multiple critical phases, showing a promising clinical diagnostic value.

Gay, bisexual, and other men who have sex with men (MSM) experience disproportionately high burdens of Human Papilloma Virus (HPV)-associated anal cancers. Recent focus has shifted to anorectal cancer prevention through high-resolution anoscopy (HRA); however, little is known about sexual minority men's perceptions, attitudes, or beliefs regarding HRA. We conducted 4 qualitative Focus Group Discussions (FGDs) (n = 15) with sexual minority men, focusing on their beliefs, attitudes, and perceptions of undergoing HRA. Participants discussed their experiences of HPV/HRA as influenced by both their gender and sexuality, including unawareness of HPV disease as a male health issue, challenges relating to female-oriented HPV/HRA language, conception of HPV/HRA as related to prostate health, and connecting their sexual behavior identification as "bottoms" to their need for HRA. As efforts to improve HRA knowledge, access, and uptake among sexual and gender minority communities increase, special attention should be paid to language and messaging choices around HRA.

Abstract For better projections of sea level rise, two things are needed: an improved understanding of the contributing processes and their accurate representation in climate models. A major process is basal melting of ice shelves and glacier tongues by the ocean, which reduces ice sheet stability and increases ice discharge into the ocean. We study marine melting of Greenland's largest floating ice tongue, the 79° North Glacier, using a high‐resolution, 2D‐vertical ocean model. While our fjord model is idealized, the results agree with observations of melt rate and overturning strength. Our setup is the first application of adaptive vertical coordinates to an ice cavity. Their stratification‐zooming allows a vertical resolution finer than 1 m in the entrainment layer of the meltwater plume, which is important for the plume development. We find that the plume development is dominated by entrainment only initially. In the stratified upper part of the cavity, the subglacial plume shows continuous detrainment. It reaches neutral buoyancy near 100 m depth, detaches from the ice, and transports meltwater out of the fjord. Melting almost stops there. In a sensitivity study, we show that the detachment depth depends primarily on stratification. Our results contribute to the understanding of ice–ocean interactions in glacier cavities. Furthermore, we suggest that our modeling approach with stratification‐zooming coordinates will improve the representation of these interactions in global ocean models. Finally, our idealized model topography and forcing are close to a real fjord and completely defined analytically, making the setup an interesting reference case for future model developments.

Geometric distortion is a major limiting factor for spatial specificity in high-resolution fMRI using EPI readouts and is exacerbated at higher field strengths due to increased B0 field inhomogeneity. Prominent correction schemes are based on B0 field-mapping or acquiring reverse phase-encoded (reversed-PE) data. However, to date, comparisons of these techniques in the context of fMRI have only been performed on 2DEPI data, either at lower field or lower resolution. In this study, we investigate distortion compensation in the context of sub-millimetre 3DEPI data at 7T. B0 field-mapping and reversed-PE distortion correction techniques were applied to both partial coverage BOLD-weighted and whole brain MT-weighted 3DEPI data with matched distortion. Qualitative assessment showed overall improvement in cortical alignment for both correction techniques in both 3DEPI fMRI and whole-brain MT-3DEPI datasets. The distortion-corrected MT-3DEPI images were quantitatively evaluated by comparing cortical alignment with an anatomical reference using dice coefficient (DC) and correlation ratio (CR) measures. These showed that B0 field-mapping and reversed-PE methods both improved correspondence between the MT-3DEPI and anatomical data, with more substantial improvements consistently obtained using the reversed-PE approach. Regional analyses demonstrated that the largest benefit of distortion correction, and in particular of the reversed-PE approach, occurred in frontal and temporal regions where susceptibility-induced distortions are known to be greatest, but had not led to complete signal dropout. In conclusion, distortion correction based on reversed-PE data has shown the greater capacity for achieving faithful alignment with anatomical data in the context of high-resolution fMRI at 7T using 3DEPI.

Accurate information on waves and storm surges is essential to understand coastal hazards that are expected to increase in view of global warming and rising sea levels. Despite the recent advancement in development and application of large-scale coastal models, nearshore processes are still not sufficiently resolved due to coarse resolutions, transferring errors to coastal risk assessments and other large-scale applications. Here we developed a 73-year hindcast of waves and storm surges on an unstructured mesh of >650,000 nodes with an unprecedented resolution of 2-4 km at the global coast. Our modelling system is based on the circulation model SCHISM that is fully coupled with the WWM-V (WindWaveModel) and is forced by surface winds, pressure, and ice coverage from the ERA5 reanalysis. Results are compared with observations from satellite altimeters, tidal gauges and buoys, and show good skill for both Sea Surface Height (SSH) and Significant Wave Height (Hs), and a much-improved ability to reproduce the nearshore dynamics compared with previous, lower-resolution studies. Besides SSH, the modelling system also produces a range of other wave-related fields at each node of the mesh with a time step of 3 hours, including the spectral parameters of the first three largest energy peaks. This dataset offers the potential for more accurate global-scale applications on coastal hazard and risk.

High‐resolution atmospheric models are powerful tools for hurricane track and intensity predictions. Although using high resolution contributes to better representation of hurricane structure and intensity, its value in the prediction of steering flow and storm tracks is uncertain. Here we present experiments suggesting that biases in the predicted North Atlantic hurricane tracks in a high‐resolution (approximately 3 km grid‐spacing) model originates from the model's explicit simulation of deep convection. Differing behavior of explicit convection leads to changes in the synoptic‐scale pattern and thereby to the steering flow. Our results suggest that optimizing small‐scale convection activity, for example, through the model's horizontal advection scheme, can lead to significantly improved hurricane track prediction (∼10% reduction of mean track error) at lead times beyond 72 hr. This work calls attention to the behavior of explicit convection in high‐resolution models, and its often overlooked role in affecting larger‐scale circulations and hurricane track prediction. Plain Language Summary: High‐resolution models (approximately 3 km grid spacing or finer) covering a large domain are emerging powerful tools for hurricane prediction. However, the use of high resolution in the model can be a double‐edged sword—while it helps improve hurricane intensity prediction, it can also make the model more prone to develop errors in the prediction of steering flow and hurricane tracks due to the possible impact of prevalent small‐scale features resolved by the model. Our results suggest that regulating small‐scale convection activity in a high‐resolution model can significantly improve hurricane track predictions at days 4 and 5. Key Points: Better regulation of explicit convection reduces North Atlantic hurricane track errors in a high‐resolution model by 10% at days 4 and 5Improved track forecasts are related to a more realistic representation of the North Atlantic subtropical ridgeExplicit convection is modulated by implicit diffusivity in the model's advection scheme [ABSTRACT FROM AUTHOR]

Background: Small‐animal positron emission tomography (PET) systems are widely used in molecular imaging research and drug development. There is also growing interest in organ‐dedicated clinical PET systems. In these small‐diameter PET systems, the measurement of the depth‐of‐interaction (DOI) of annihilation photons in scintillation crystals allows for the correction of parallax error in PET system, leading to an improvement on the spatial resolution uniformity. The DOI information is also useful for improving the timing resolution of PET system as it enables the correction of DOI‐dependent time walk in the arrival time difference measurement of annihilation photon pairs. The dual‐ended readout scheme is one of the most widely investigated DOI measurement methods, which collects visible photons using a pair of photosensors located at both ends of the scintillation crystal. Although the dual‐ended readout allows for simple and accurate DOI estimation, it requires twice the number of photosensors compared to the single‐ended readout scheme. Purpose: To effectively reduce the number of photosensors in a dual‐ended readout scheme, we propose a novel PET detector configuration that employs 45° tilted and sparsely arranged silicon photomultipliers (SiPMs). In this configuration, the angle between the scintillation crystal and SiPM is 45°. Therefore, and thus, the diagonal of the scintillation crystal matches one of the lateral sides of the SiPM. Accordingly, it allows for the use of SiPM device larger than the scintillation crystal, thereby improving light collection efficiency with a higher fill factor and reducing SiPM quantity. In addition, all scintillation crystals can achieve more uniform performance than other dual‐ended readout methods with a sparse SiPM arrangement because 50% of the scintillation crystal cross section is commonly in contact with the SiPM. Methods: To demonstrate the feasibility of our proposed concept, we implemented a PET detector that employs a 4×${\rm{\;}} \times \;$4 LSO block with a single crystal dimension of 3.03 × 3.03 × 20 mm3 and a 45° tilted SiPM array. The 45° tilted SiPM array consists of 2 × 3 SiPM elements at the top ("Top SiPM") and 3 × 2 SiPM elements at the bottom ("Bottom SiPM"). Each crystal element of the 4 × 4 LSO block is optically coupled with each quarter section of the Top SiPM and Bottom SiPM pair. To characterize the performance of the PET detector, the energy, DOI, and timing resolution were measured for all 16 crystals. The energy data was obtained by summing all the charges from the Top SiPMs and Bottom SiPMs, and the DOI resolution was measured by irradiating the side of the crystal block at five different depths (2, 6, 10, 14, and 18 mm). The timing was estimated by averaging the arrival time of the annihilation photons measured at the Top SiPMs and Bottom SiPMs (Method 1). The DOI‐dependent time‐walk effect was further corrected by using DOI information and statistical variations in the trigger times at the Top SiPMs and Bottom SiPMs (Method 2). Results: The average DOI resolution of the proposed PET detector was 2.5 mm, thereby resolving the DOI at five different depths, and the average energy resolution was 16% full width at half maximum (FWHM). When Methods 1 and 2 were applied, the coincidence timing resolutions were 448 and 411 ps FWHM, respectively. Conclusions: We expect that our novel low‐cost PET detector design with 45° tilted SiPMs and a dual‐ended readout scheme would be a suitable solution for constructing a high‐resolution PET system with DOI encoding capability. [ABSTRACT FROM AUTHOR]

Exploiting advanced patterning strategies and materials to achieve smaller sizes and higher precision is urgently and constantly pursued. In this work, to suppress the radiolytic low-energy secondary electron diffusion and accompanying unpredictable defects, we demonstrate a patterning control strategy to realize high-precision nanoscale fabrication through a quenching mechanism and achieve significant resolution and roughness enhancement in ∼10 nm patterning. [Display omitted] Nanopatterning is a well-established approach to fabricating nanostructures in electronics and optics, and exploiting patterning strategy to achieve smaller feature sizes and higher precision is urgently and constantly pursued. State-of-the-art extreme ultraviolet lithography and electron beam lithography have proven to produce smaller sizes. However, for such energetic radiation-based approaches, the serious diffusion behavior of the radiolytic low-energy secondary electrons will result in unpredictable defects in the unexposed matrix, limiting the ultimate resolution and hindering its potential in sub-10 nm patterning. Herein, we report significant progress in high-resolution patterning via suppressing of residues caused by secondary electron diffusion, and 10 nm line-space nanostructures are achieved by utilizing a free radical quencher in patterning a highly sensitive zirconium-containing photoresist. Lithography evaluation combined with theoretical calculation reveals this novel radical quencher approach can effectively suppress undesired electronic excitation and ionization reactions, thereby significantly improving resolution and edge roughness. By inhibiting secondary electron-induced active species, this quenching mechanism is found to increase the onset dose and effectively narrow the energy deposition; thus improving the patterning contrast and facilitating the acquisition of straight lines with sharp edges. This work provides a new perspective on active species diffusion control for higher precision nanoscale fabrication. [ABSTRACT FROM AUTHOR]

In practical inverse synthetic aperture radar (ISAR) imaging application, the data received by the radar is incomplete, which is called sparse aperture (SA) data. Sparse recovery (SR) theory can reconstruct unknown full aperture (FA) data from SA data by solving sparse optimization problems. The atomic norm minimization (ANM) solves the grid mismatch problem of the traditional SR theory effectively, but the imaging resolution is still limited. In this study, a sparse ISAR imaging method based on frequency-selective atomic norm minimization (FSANM) is proposed in this paper. The proposed method utilizes prior information to achieve frequency estimation within a given interval, effectively improving the resolution of the ANM method. The results of simulated and real data verify the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

A consistent theme in British prehistoric studies is a perceived lack of evidence for settlement and agriculture in the Neolithic (c.4000 cal BC – 2500 cal BC), which then increases in prevalence during the Bronze Age. This lack of evidence for Neolithic settlement and farming has been linked to a collapse of agriculture associated with population decline and climatic deterioration from c.3600 cal BC onwards, with population recovery and the resumption of widespread agriculture in the later 3rd millennium cal BC. Updated palaeoenvironmental reconstructions from Lairg, Sutherland, incorporating a high-resolution Bayesian chronology based on 14C dates from 20 samples indicate that mixed agriculture (including cereal cultivation) continued in this area during the proposed Middle – Late Neolithic agricultural recession described above. We suggest that an existing reliance on barley, which is more tolerant of cooler, wetter conditions than other cereals, and the use of well-drained sites within the landscape allowed cereal cultivation to continue in the face of climatic deterioration. We also suggest that a lack of high-resolution chronologies for palaeoenvironmental reconstructions and a reliance on woodland clearance as a proxy for agriculture could potentially obscure pollen evidence for Middle and Late Neolithic farming. Issues of chronological precision in legacy datasets are addressed. [ABSTRACT FROM AUTHOR]

Advanced micro/nano-flexible sensors, displays, electronic skins, and other related devices provide considerable benefits compared to traditional technologies, aiding in the compactness of devices, enhancing energy efficiency, and improving system reliability. The creation of cost-effective, scalable, and high-resolution fabrication techniques for micro/nanostructures built from optoelectronic materials is crucial for downsizing to enhance overall efficiency and boost integration density. The electrohydrodynamic jet (EHD) printing technology is a novel additive manufacturing process that harnesses the power of electricity to create fluid motion, offering unparalleled benefits and a diverse spectrum of potential uses for microelectronic printing in terms of materials, precision, accuracy, and cost-effectiveness. This article summarizes various applications of EHD printing by categorizing them as zero-dimensional (0D), one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) printing materials. Zero-dimensional (quantum dot) materials are predominantly utilized in LED applications owing to their superb optoelectronic properties, high color fidelity, adjustable color output, and impressive fluorescence quantum yield. One- and two-dimensional materials are primarily employed in FET and sensor technologies due to their distinctive physical structure and exceptional optoelectronic properties. Three-dimensional materials encompass nanometals, nanopolymers, nanoglass, and nanoporous materials, with nanometals and nanopolymers finding widespread application in EHD printing technology. We hope our work will facilitate the development of small-feature-size, large-scale flexible electronic devices via EHD printing. [ABSTRACT FROM AUTHOR]

High-precision time–frequency (TF) analysis (TFA) can accurately detect anomalies in oil and gas reservoirs. However, conventional TFA methods cannot effectively identify fractured-vuggy carbonate reservoirs, which are deep and strongly heterogeneous. In this letter, a TFA method based on analytical mode decomposition (AMD) and the Hilbert–Huang transform (HHT) is proposed. Specifically, seismic signals are decomposed by empirical mode decomposition (EMD) and then by AMD. Finally, the TF distribution (TFD) of the seismic signals is obtained after Hilbert spectrum analysis (HSA). Synthetic seismic data demonstrate that the AMD-HHT method can effectively eliminate mode mixing and provide higher time and frequency resolutions and energy focus than conventional TFA methods. In addition, model and field seismic data also verify that the AMD-HHT method can effectively detect hydrocarbon-related energy anomalies hidden in broadband seismic data. [ABSTRACT FROM AUTHOR]

Every year, 3.8 million premature deaths are registered due to household air pollution, with a great fraction occurring in developing countries due to the regular use of open fires and inefficient stoves. This study aims to develop a high-resolution residential atmospheric emission inventory and provide an up-to-date fuel consumption dataset to be used by developing countries. For that, Santiago Island (Cabo Verde) was selected as a case study and a survey-based approach was considered, due to the lack of data in this region. From the survey to main conclusions pop-out: i) no significant differences in residential fuel consumption in the analysed municipalities were observed; ii) the highest fuel consumption per capita was recorded for the firewood, followed by charcoal and Liquefied Petroleum Gas (LPG); iii) the maximum household consumption share was estimated for LPG, followed by firewood and charcoal; and iv) charcoal and LPG registered higher cost per capita than firewood. Based on such data, it was concluded that the residential atmospheric emissions estimated by up-to-date global inventories are unlikely to be true values in the study area. The estimated inventory showed that the maximum emissions were recorded in May and the minimum values in February, with firewood being the main emission source. The weekly profiles presented higher residential emissions on weekends than on weekdays. Charcoal was mainly used on the weekends, while the weekend was estimated as the lowest contribution for both firewood and LPG. Fuel switching to LPG would result in a substantial reduction in residential emissions and, consequently, outdoor/indoor air quality and human health improvement. The developed dataset can be used to improve the spatial and temporal accuracy of the urban and large-scale residential atmospheric emissions in the developing countries. [ABSTRACT FROM AUTHOR]

Hydrofluorocarbons (HFCs) are strong greenhouse gases and regulated by the Montreal Protocol as substitutes of ozone depletion substances. Currently, Chinese HFC emissions keep increasing, and the inventory is only on a national or city level. A high-resolution gridded HFC emission inventory is needed to develop HFC reduction policy and phase-down schedule. We developed a method by integrating point sources with longitude and latitude information and area sources using the proxy factor to explore the distribution of R-410A [a mixture of HFC-32 (CH2F2) and HFC-125 (C2HF5)] emissions from the room air-conditioning sector on a 10 × 10 km2 grid scale. Variety of regression models (including the principal component analysis, multiple linear regressions, stepwise regressions, and linear regression), analysis scale (national level and provincial level), and data dimensions (the proxy factor and unit-area value) were tested. The gross domestic product was found as the optimal proxy factor and used to spatialize R-410A emissions at a high-resolution scale. Compared to the national-level analysis, model evaluation parameters were largely improved for the provincial-level regression analysis, including root-mean-square error (from 20.96 to 11.35), normalized mean bias (from 0.16 to − 0.01), normalized mean error (from 0.45 to 0.20), mean absolute error (from 11.27 to 4.97), correlation coefficient (from 0.91 to 0.97), and relative error (from 39% to 76%), suggesting a better performance for the provincial-level analysis. This study provides a cost-effective method to establish fine-resolution HFC inventory. Meanwhile, high-resolution emissions grid data could be further applied to implement site-specific management of low-carbon development. [ABSTRACT FROM AUTHOR]

We propose an integral imaging 3D display based on top‐hat diffusing screen. The integral imaging 3D display consists of a 2D display, a micro‐lens array, and a top‐hat diffusing screen. The top‐hat diffusing screen is composed of multiple elemental lenses with different pitches. The light rays pass through the top‐hat diffusing screen to form a regular light spot, and the light intensity distribution of the light spot is top‐hat type. This can make the 3D image smooth and natural. The experimental results verify the feasibility of the proposed integral imaging 3D display. [ABSTRACT FROM AUTHOR]

We have developed 12‐inch 100 PPI active‐matrix micro‐LED stretchable displays that can be stretched up to 20%. Our work has involved the development of key technologies such as stretchable interconnect, stretchable film, micro‐LED, and the integration process for these components. The stretchable displays exhibited various types of deformations, suggesting the potential for new applications in information display. [ABSTRACT FROM AUTHOR]

We have presented an inverse Q filtering scheme that can compensate absorption and dispersion caused by intrinsic attenuation in subsurface media with a heterogeneous Q model. We have adopted two methods to suppress high-frequency noise at the same time, one is to design a compensation operator with a fixed gain limit, and the other is to introduce an adaptive frequency-varying band calculation method. We use VSP data and seismic velocity data to estimate Q model of the whole work area in a unique way. The proposed scheme can be incorporated into conventional seismic data processing workflow. Tests on synthetic and real data set demonstrate effectiveness of the proposed inverse Q filtering. [ABSTRACT FROM AUTHOR]

Microtopography plays an important role in various ecological, hydrologic, and biogeochemical processes. However, quantifying the characteristics of microtopography represents a data-intensive challenge. Over the last decade, high-resolution or close-range remote sensing data and techniques have emerged as powerful tools to quantify microtopography. Traditional field surveys were mostly limited to transects or small plots, using limited sets of observations but with the decrease in the cost of close-range remote sensing technologies and the increase in computing performance, the microtopography even in forested environments can be assessed. The main objective of this article is to provide a systematic framework for microtopographic studies using close-range remote sensing technologies. This is achieved by reviewing the application of close-range remote sensing to capture microtopography and develop microtopographic models in natural ecosystems. Specifically, to achieve the main objectives, we focus on addressing the following questions: (1) What terrain attributes represent microtopography in natural ecosystems? (2) What spatial resolution of terrain attributes is needed to represent the microtopography? (3) What methodologies have been adopted to collect data at selected resolutions? (4) How to assess microtopography? Current research, challenges, and applicability of close-range remote sensing techniques in different terrains are analyzed with an eye to enhancing the use of these new technologies. We highlight the importance of using a high-resolution DEM (less than 1 m2 spatial resolution) to delineate microtopography. Such a high-resolution DEM can be generated using close-range remote sensing techniques. We also illustrate the need to move beyond elevation and include terrain attributes, such as slope, aspect, terrain wetness index, ruggedness, flow accumulation, and flow path, and assess their role in influencing biogeochemical processes such as greenhouse gas emissions, species distribution, and biodiversity. To assess microtopography in terms of physical characteristics, several methods can be adopted, such as threshold-based classification, mechanistically-based delineation, and machine learning-based delineation of microtopography. The microtopographic features can be analyzed based on physical characteristics such as area, volume, depth, and perimeter, or by using landscape metrics to compare the classified microtopographic features. Remote sensing techniques, when used in conjunction with field experiments/data, provide new avenues for researchers in understanding ecological functions such as biodiversity and species distribution, hydrological processes, greenhouse gas emissions, and the environmental factors that influence those parameters. To our knowledge, this article provides a comprehensive and detailed review of microtopography data acquisition and quantification for natural ecosystem studies. [ABSTRACT FROM AUTHOR]

Building change detection is an important task in the remote sensing field, and the powerful feature extraction ability of the deep neural network model shows strong advantages in this task. However, the datasets used for this study are mostly three-band high-resolution remote sensing images from a single data source, and few spectral features limit the development of building change detection from multisource remote sensing images. To investigate the influence of spectral and texture features on the effect of building change detection based on deep learning, a multisource building change detection dataset (MS-HS BCD dataset) is produced in this paper using GF-1 high-resolution remote sensing images and Sentinel-2B multispectral remote sensing images. According to the different resolutions of each Sentinel-2B band, eight different multisource spectral data combinations are designed, and six advanced network models are selected for the experiments. After adding multisource spectral and texture feature data, the results show that the detection effects of the six networks improve to different degrees. Taking the MSF-Net network as an example, the F1-score and IOU improved by 0.67% and 1.09%, respectively, compared with high-resolution images, and by 7.57% and 6.21% compared with multispectral images. [ABSTRACT FROM AUTHOR]

To evaluate the diagnostic performance of a non-contrast magnetic resonance imaging (MRI) protocol combining high-resolution diffusion-weighted images (HR-DWI) using readout-segmented echo planar imaging, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI), using our modified Breast Imaging-Reporting and Data System (modified BI-RADS). Two experienced radiologists, blinded to the final pathological diagnosis, categorized a total of 108 breast lesions (61 malignant and 47 benign) acquired with the above protocol using the modified BI-RADS with a diagnostic decision tree. The decision tree included subcategories of category 4, as in mammography (categories 2, 3, 4A, 4B, 4C, and 5). These results were compared with the pathological diagnoses. The area under the ROC curve (AUC) was 0.89 (95% confidence interval [CI]: 0.83–0.95) for reader 1, and 0.89 (95% CI: 0.82–0.96) for reader 2. When categories 4C and above were classified as malignant, the sensitivity, specificity, and accuracy were 73.8%, 93.6%, and 82.4%, for reader 1; and 82.0%, 89.4%, and 85.2% for reader 2, respectively. Our results suggest that using HR-DWI, T1WI/T2WI analyzed with a modified BI-RADS and a decision tree showed promising diagnostic performance in breast lesions, and is worthy of further study. • HR-DWI enabled morphological evaluation of malignant lesions without contrast. • A modified BI-RADS MRI decision tree achieved promising diagnostic accuracy. • The area under the ROC curve for diagnosing malignancy was 0.89 for both readers. [ABSTRACT FROM AUTHOR]

This paper describes the development of 12-in. 100 PPI active-matrix micro-LED stretchable displays, capable of being stretched up to 20%. Our work has involved the development of key technologies such as stretchable interconnects, stretchable films, and micro-LEDs and the integration process for these components. The stretchable displays exhibited various types of deformations, suggesting the potential for new applications in information display. [ABSTRACT FROM AUTHOR]

This letter addresses high-resolution sea clutter modelling. Radar clutter modelling is essential to develop constant false alarm rate (CFAR) detection schemes. In this paper, we propose a compound model by the use of the inverted exponentiated Rayleigh (IER) distribution as texture component. The probability density function (PDF) of the compound inverted exponentiated Rayleigh (CIER) distribution is a function of three parameters including the thermal noise power. To assess the modelling performance of the CIER distribution, several tests have been performed using the Intelligent PIXel Processing Radar (IPIX) database where the fitted PDFs and the complimentary cumulative density functions (CCDFs) of the CIER, the well-known compound K and the compound inverse Gaussian (CIG) distributions are compared with the real PDF and CCDF. The results obtained validate the ability of the CIER distribution to model high-resolution sea clutter. [ABSTRACT FROM AUTHOR]

Systematic toxicological analysis (STA) is the process of using an adequate analytical methodology to detect and identify as many potentially toxicologically relevant compounds as possible in biological samples. STA is an important part of everyday routine work within forensic toxicology, and several methods for STA have frequently been published and reviewed independently. However, the many drugs and other substances involved, as well as the constant emergence of new ones, may pose a major challenge in STA, which often demands a strategy involving multiple analytical methods in parallel. Such strategies have been published and evaluated less frequently despite their relevance in forensic toxicology. This mini‐review briefly summarizes commonly applied methods for STA in forensic toxicology, including gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–MS (LC–MS) methods, and highlights some of their potential pitfalls. Second, it provides an overview of previously reported strategies to conduct STA, including a presentation of the STA strategy applied in the authors' laboratory. This involves broad drug screening by LC–high‐resolution MS, supported by targeted screening and quantification using LC–tandem MS, headspace (HS)‐GC–MS, HS‐GC–flame ionization detector and other complementary methods. The STA strategy aims to cover as many potentially relevant drugs as possible and seeks to reduce potential pitfalls arising in forensic casework. The review underlines that not every substance can be identified in all circumstances even with a comprehensive STA strategy. [ABSTRACT FROM AUTHOR]

Ischemic stroke caused by intracranial arterial stenosis (ICAS) is characterized by high morbidity and mortality. Conventional clinical examination methods include transcranial Doppler ultrasound, CT angiography, magnetic resonance angiography, and X-ray digital subtraction angiography. These methods are aimed at vascular stenosis and do not show vascular wall lesions. High-resolution magnetic resonance vessel wall imaging (HR-VWI) is a new imaging method that can non-invasively display vascular wall lesions and has important value in judging the nature of ICAS lesions. In this paper, the application of HR-VWI in the identification of ICAS etiology is reviewed.

There are thousands of observation satellites orbiting the earth, each of which captures massive-scale photographs covering millions of square kilometers everyday. In practice, these aerial photos are with high-resolution and usually contain tens to hundreds of ground objects (e.g., vehicles and rooftops). Understanding the categories of a rich variety of high-resolution aerial photos is an indispensable technique for many applications, such as intelligent transportation, natural disaster prediction, and smart agriculture. In this work, we propose a cross-resolution perceptual experiences transfer framework for categorizing high-resolution aerial photos, focusing on leveraging the perceptual features from low-resolution aerial photos to enhance the feature selection of high-resolution ones. More specifically, we first construct gaze shifting path to mimic human visual perception to both low-resolution and high-resolution aerial photos, wherein the corresponding deep gaze shifting path features are engineered. Afterward, a kernel-induced feature selection algorithm is formulated to obtain a succinct set of deep gaze shifting path features discriminative across low- and high-resolution aerial photos. Based on the selected features, low- and high-resolution aerial photos’ labels are collaboratively utilized to train a linear classifier for categorizing high-resolution ones. Extensive comparative studies have validated the superiority of our method.

Recognizing the multiple categories of an high-resolution (HR) aerial photos is an indispensable technique in geoscience and remote sensing. In this work, a perceptual low-rank algorithm combined with a geometry-preserving feature selection (FS) is proposed for categorizing HR aerial photos. In practice, the theory of human visual perception indicates that for each scenery, the background non-salient regions are highly correlated, whereas the foreground visually/semantically salient regions are almost uncorrelated. Motivated by this, we design a novel low-rank algorithm that seeks a sparse set of foreground visually/semantically salient image patches. These patches are sequentially linked into a so- called GSP (path reflecting gaze movement) to mimick human vision system. Afterward, a geometry-preserving FS algorithm is proposed to select highly discriminative features from the aforementioned gaze features, wherein a classifier can be trained simultaneously. Comprehensive experimental validation on our Internet-scale image set have shown its superiority.

In recent years, the research on FMCW radar utilizing the mmWave band has gained significant attention, especially in its application to autonomous driving systems. As a potential replacement for lidar, high-resolution studies have been pursued, necessitating large aperture sizes and a considerable number of channels. In this study, high angular resolution is achieved by creating Multiple-Input Multiple-Output (TDM-MIMO) radar system with 6 Tx 8 Rx based on a co-prime array configuration in the W-band. To simplify the interface between the proposed array configuration and the actual chipsets, a 65-nm CMOS process-based single-channel T/Rx chipset with one-to-one aperture coupling to the antenna is employed, minimizing losses, phase deviations, and radiation pattern impacts seen in direct coupling methods using transmission lines in the mmWave band. In addition, the proposed co-prime array exhibits more sidelobe effects, making it challenging to extract actual targets from the angular spectrum. To tackle this, the paper presents the Iterative Peak Component Elimination (IPCE) algorithm, validated through experiments, effectively resolving the issue. The study’s findings contribute to advancing high-resolution radar systems, enhancing target detection and tracking capabilities, particularly in automotive radar applications.

Numerical simulation and analysis are effective means to explore the basic laws of the world, in which a synthetic model provides a model basis for research. A random medium model can effectively characterize the small-scale heterogeneity in artificial or natural complex media and has been widely used in reservoir, deposit, material, engineering, and other fields. Many efficient and accurate modeling methods have been proposed for stationary random medium models. However, the modeling efficiency of a random medium with nonstationary statistical characteristics is very low. In this paper, an efficient nonstationary random medium modeling method based on Fast Fourier transform moving average (FFT-MA) method and convolutional neural network is proposed. FFT-MA is used to quickly simulate a low-resolution random medium model, which satisfies the change of large-scale statistical characteristics. Then we transform the low-resolution model into a high-resolution model by a convolutional neural network. The convolutional neural network is trained using a set of stationary random medium models. The modeling experiments show that this method can accurately construct a nonstationary random medium model meeting the nonstationary statistical characteristics and improves the modeling efficiency.

Brain PET scanners that simultaneously provide high-resolution across the field-of-view and high-sensitivity can be constructed using detectors based on SiPM arrays coupled to both ends of scintillator arrays with finely segmented and long detector elements. To reduce the dead space between detector modules and hence improve the sensitivity of PET scanners, crystal arrays with curved surfaces are proposed. In this paper, the performance of a proof-of-concept detector module with nine detector submodules based on SiPMs coupled to both ends of a curved LYSO array with a pitch size of 1.0  ×  1.0 mm2 at the front-end and a length of 30 mm was evaluated. A simple signal multiplexing method using the shared-photodetector readout method was evaluated to identify the crystals. The results showed that all the LYSO elements in the detector module of interest could be clearly resolved. The energy resolution, depth-of-interaction resolution, and timing resolution were 14.6%  ±  3.6%, 2.77  ±  0.39 mm, and 1.15  ±  0.07 ns, respectively, obtained at a bias voltage of 28.0 V and a temperature of 16.8 °C  ±  0.2 °C.

The evolution of the East Asian winter monsoon (EAWM) during the Holocene remains controversial because of the lack of reliable proxies. In this study, a link between cross‐front transport and the EAWM was found in the North Yellow Sea, and the EAWM intensity could be imprinted in the leaked sediment, which permits reliable reconstruction of the EAWM on well‐preserved offshore deposition. By comparison with instrumental data, the wind speed of the EAWM since 4.4 ka was quantitatively reconstructed for the first time at high resolution (∼5 yr). The results show that the EAWM gradually weakened in the mid‐to‐late Holocene, which was likely controlled by solar insolation, and that there has been a negative relationship between the EAWM and temperature on the centennial scale since ∼2.0 ka; this relationship was reversed by increasing CO2 emissions during the Current Warm Period. Plain Language Summary: The East Asian monsoon is characterized by a distinct seasonal reversal of monsoon flow driven by temperature differences between the Pacific Ocean and the East Asian continent. It influences more than 2 billion people around East Asia by regulating the risk of drought or flooding. However, the evolution of the East Asian winter monsoon (EAWM) in the mid‐to‐late Holocene remains elusive, limiting our understanding of its driving force and projections under climate change. This study analyzed sediment transport dynamics under varying winter monsoons in the North Yellow Sea. It was found that coastal sediments could be transported offshore, and their pathways differed under different strengths of the winter monsoon. Based on the sedimentary records of offshore muddy deposition, the EAWM proxy was extracted, and the wind speed of the EAWM from 4.4 ka was quantitatively reconstructed. A gradual weakening trend during 4.4–0.7 ka and a dramatic strengthening after 0.2 ka were found in the EAWM; the EAWM was negatively related to the temperature on the centennial scale since ∼2.0 ka, but this relationship was reversed by an increase in CO2 emissions since ∼1800 CE, indicating a shift in the controlling mechanism. Key Points: A link between cross‐front transport and the East Asian winter monsoon (EAWM) was found in the shelf sea, and the leaked sediment was used to reconstruct the EAWMThe wind speed of the EAWM since 4.4 ka was quantitatively reconstructed at high resolution (∼5 yr) for the first timeCorrelation between the EAWM and the temperature on the centennial scale since ∼2.0 ka was reversed by the increasing CO2 at the Current Warm Period [ABSTRACT FROM AUTHOR]

The satellite-based Aerosol Optical Depth (AOD) retrieval algorithms are generally needed to construct Land Surface Reflectance (LSR) database. However, errors are unavoidable due to the surface complexity, especially for the short observation period and high-resolution images, such as Sentinel-2 Multi-Spectral Instrument (MSI) data. To address this, reference day images are used instead of the LSR database. The surface is assumed to be Lambertian; however, the fact is that not all pixels meet it well. Therefore, we proposed a window-based AOD retrieval algorithm, which can ignore the unreliable/non-Lambertian pixels in a retrieval window based on two main filtering processes. Finally, using Sentinel-2 Band 1 (60 m), the AODs (120 m) of 134 reference images to 43 reference images were retrieved by this algorithm from 2017 to 2021 in Beijing region, China. The results show that the retrieved AOD with the proposed algorithm exhibits good agreement with the ground-based measured AOD (R > 0.97). The high-resolution AOD presents comparable spatial distributions to the Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm AOD (1 km) products. Moreover, the very little noise and very high spatial continuity of retrieval AOD imply that this algorithm could be ported to other algorithms as part of improving AOD quality. [ABSTRACT FROM AUTHOR]

Large molecule protein therapeutics have steadily grown and now represent a significant portion of the overall pharmaceutical market. These complex therapies are commonly manufactured using cell culture technology. Sequence variants (SVs) are undesired minor variants that may arise from the cell culture biomanufacturing process that can potentially affect the safety and efficacy of a protein therapeutic. SVs have unintended amino acid substitutions and can come from genetic mutations or translation errors. These SVs can either be detected using genetic screening methods or by mass spectrometry (MS). Recent advances in Next-generation Sequencing (NGS) technology have made genetic testing cheaper, faster, and more convenient compared to time-consuming low-resolution tandem MS and Mascot Error Tolerant Search (ETS)-based workflows which often require ~6 to 8 weeks data turnaround time. However, NGS still cannot detect non-genetic derived SVs while MS analysis can do both. Here, we report a highly efficient Sequence Variant Analysis (SVA) workflow using high-resolution MS and tandem mass spectrometry combined with improved software to greatly reduce the time and resource cost associated with MS SVA workflows. Method development was performed to optimize the high-resolution tandem MS and software score cutoff for both SV identification and quantitation. We discovered that a feature of the Fusion Lumos caused significant relative under-quantitation of low-level peptides and turned it off. A comparison of common Orbitrap platforms showed that similar quantitation values were obtained on a spiked-in sample. With this new workflow, the amount of false positive SVs was decreased by up to 93%, and SVA turnaround time by LC-MS/MS was shortened to 2 weeks, comparable to NGS analysis speed and making LC-MS/MS the top choice for SVA workflow. [ABSTRACT FROM AUTHOR]

Objectives: The study aimed to develop a deep neural network (DNN)–based noise reduction and image quality improvement by only using routine clinical scans and evaluate its performance in 3D high-resolution MRI. Methods: This retrospective study included T1-weighted magnetization-prepared rapid gradient-echo (MP-RAGE) images from 185 clinical scans: 135 for DNN training, 11 for DNN validation, 20 for qualitative evaluation, and 19 for quantitative evaluation. Additionally, 18 vessel wall imaging (VWI) data were included to evaluate generalization. In each scan of the DNN training set, two noise-independent images were generated from the k-space data, resulting in an input-label pair. 2.5D U-net architecture was utilized for the DNN model. Qualitative evaluation between conventional MP-RAGE and DNN-based MP-RAGE was performed by two radiologists in image quality, fine structure delineation, and lesion conspicuity. Quantitative evaluation was performed with full sampled data as a reference by measuring quantitative error metrics and volumetry at 7 different simulated noise levels. DNN application on VWI was evaluated by two radiologists in image quality. Results: Our DNN-based MP-RAGE outperformed conventional MP-RAGE in all image quality parameters (average scores = 3.7 vs. 4.9, p < 0.001). In the quantitative evaluation, DNN showed better error metrics (p < 0.001) and comparable (p > 0.09) or better (p < 0.02) volumetry results than conventional MP-RAGE. DNN application to VWI also revealed improved image quality (3.5 vs. 4.6, p < 0.001). Conclusion: The proposed DNN model successfully denoises 3D MR image and improves its image quality by using routine clinical scans only. Key Points: • Our deep learning framework successfully improved conventional 3D high-resolution MRI in all image quality parameters, fine structure delineation, and lesion conspicuity. • Compared to conventional MRI, the proposed deep neural network-based MRI revealed better quantitative error metrics and comparable or better volumetry results. • Deep neural network application to 3D MRI whose pulse sequences and parameters were different from the training data showed improvement in image quality, revealing the potential to generalize on various clinical MRI. [ABSTRACT FROM AUTHOR]

We present a high‐resolution white light quantitative phase nanoscopy (WLQPN) system that can be utilized to visualize nanoparticles and subcellular features of the biological specimens. The five‐phase shifting technique, along with deconvolution, is adopted to obtain super‐resolution in phase imaging. The phase shifting technique can provide full detector resolution, making it beneficial as compared to the well‐known Fourier analysis method. The Fourier transform method requires minimum angle of sin−13fxλ, where fx is maximum achievable spatial frequency. It limits the highest achievable resolution to much below the actual diffraction limit of the system. Thus, to obtain a high‐resolution phase map of the biological specimen, a two‐step process is adopted. First, the phase map is recovered using the five‐phase shifting algorithm, with full detector spatial resolution. Second, the complex field is obtained from the recovered phase map and further processed using the Richardson Lucy total variation deconvolution algorithm to obtain super‐resolution phase images. The present technique was tested on 1951 USAF resolution chart, 200 nm polystyrene beads and Escherichia coli bacteria using a 50×, 0.55NA objective lens. The 200 nm polystyrene beads are visually resolvable and subcellular features of the E. coli bacteria are also observed, suggesting a significant improvement in the resolution. [ABSTRACT FROM AUTHOR]

The global climate of the early Oligocene was characterized by initiated Antarctic glaciation and meridional overturning circulation, which then led to profound eutrophication in the upper ocean. Generating a high‐resolution coccolith record helps to understand the responses of marine phytoplankton to the newly established environment. Using highly resolved (∼6 kyr time‐resolution) marine sediment samples from Deep Sea Drilling Project Site 522 in the South Atlantic Ocean, we conducted a comprehensive morphological study on coccoliths from the genera Reticulofenestra, Dictyococcites, and Coccolithus, which dominated the study interval between ∼33.1 and 32.8 Ma. Our results showed that the size variations of the three measured genera were significantly correlated (p < 0.01) with each other, indicating homogeneous responses to the environmental changes. Moreover, spectrum analysis on integrated morphologic data of all measured coccoliths showed distinct obliquity (∼40‐kyr) and precession (∼23‐kyr and ∼18‐kyr) cycles. We suggest that these variations were mainly driven by temperate, short‐term ecological fluctuations, which periodically altered the nutrient conditions in the common living habitats of the studied coccolithophores. We proposed two tentative explanations focusing on the obliquity signal. First, the cyclic variation could result from obliquity‐modulated changes in ice volume and variations in ocean circulation intensity, which influenced nutrient export from deep waters to the upper ocean. Alternatively, the changes in coccolith size may indicate the strength of seasonality that influenced upper ocean mixing on the west coast of South Africa. Plain Language Summary: Phytoplankton sizes yield important information to understand changes in marine ecosystems. Coccoliths, the fossil remains of unicellular algae coccolithophores, are preserved in marine sediments and thus are ideal for recording phytoplankton size changes in the past. We present a high‐resolution coccolith record spanning a period of ∼300,000 years in the early Oligocene (∼33 million years ago). Our data showed changes in coccolithophore sizes, corresponding to Earth's orbital cycles. We propose that the increased size of coccolithophore corresponded to nutrient‐rich conditions. Strengthened ocean circulation or enhanced wind‐driven mixing that is related to Earth's orbital cycles are two possible driving mechanisms for the nutrient increase in the upper ocean. Our study provides direct evidence of Earth's orbital forcings on the marine ecosystem in the early Oligocene when Antarctica started to be covered by a continental ice sheet. Key Points: This study presents a high‐resolution morphological record of early Oligocene coccolithophores in the South AtlanticThree measured coccolithophore genera showed homogenous responses to short‐term ecological fluctuationsOcean circulation and seasonality modulated by orbital forcing might have controlled the nutrient availability in the upper ocean [ABSTRACT FROM AUTHOR]

The southern Lake Michigan region of the United States, home to Chicago, Milwaukee, and other densely populated Midwestern cities, frequently experiences high pollutant episodes with unevenly distributed exposure and health burdens. Using the two‐way coupled Weather Research Forecast and Community Multiscale Air Quality Model (WRF‐CMAQ), we investigate criteria pollutants over a southern Lake Michigan domain using 1.3 and 4 km resolution hindcast simulations. We assess WRF‐CMAQ's performance using data from the National Climatic Data Center and Environmental Protection Agency Air Quality System. Our 1.3 km simulation slightly improves on the 4 km simulation's meteorological and chemical performance while also resolving key details in areas of high exposure and impact, that is, urban environments. At 1.3 km, we find that most air quality‐relevant meteorological components of WRF‐CMAQ perform at or above community benchmarks. WRF‐CMAQ's chemical performance also largely meets community standards, with substantial nuance depending on the performance metric and component assessed. For example, hourly simulated NO2 and O3 are highly correlated with observations (r > 0.6) while PM2.5 is less so (r = 0.4). Similarly, hourly simulated NO2 and PM2.5 have low biases (<10%), whereas O3 biases are larger (>30%). Simulated spatial pollutant patterns show distinct urban‐rural footprints, with urban NO2 and PM2.5 20%–60% higher than rural, and urban O3 6% lower. We use our 1.3 km simulations to resolve high‐pollution areas within individual urban neighborhoods and characterize seasonal changes in O3 regimes across tight spatial gradients. Our findings demonstrate both the benefits and limitations of high‐resolution simulations, particularly over urban settings. Plain Language Summary: In this study we use an air quality model to simulate air pollution at very fine spatial scales over a central midwestern U.S. domain that includes Chicago, IL and Milwaukee, WI. We assess our model's performance relative to meteorological and air quality observations and then characterize the spatial patterns of modeled pollutants. We find large differences in air pollution between urban and rural settings. Because our model operates at fine spatial scales, we are also able to discuss differences in air pollution in different neighborhoods in individual cities. In Chicago, we find elevated pollution near highways and in south and west side neighborhoods, findings that are consistent with previous reports of disparate air quality related health impacts. Key Points: We perform nested air quality simulations over the Midwestern US, with the inner domain resolution at a 1.3 km neighborhood‐scaleNO2 and PM2.5 hotspots are simulated adjacent to major roadways, with substantial pollutant differences between urban and rural settingsPollutants within Chicago are simulated to have substantial neighborhood‐to‐neighborhood concentration disparities [ABSTRACT FROM AUTHOR]