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PURPOSE Interstitial 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. METHODS Sixty-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) PCD-CT 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. RESULTS Reticulation 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]. CONCLUSION UHR PCD-CT provides more detailed information on ILD in patients with RA than low-dose HR PCD-CT. 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 FROM AUTHOR]

Urban flooding has become one of the most common natural hazards threatening people's lives and assets globally due to climate change and rapid urbanization. Hydraulic structures, e.g., sluicegates and pumping stations, can directly influence flooding processes and should be represented in flood modeling and risk assessment. This study aims to present a robust numerical model by incorporating a hydraulic structure simulation module to accurately predict the highly transient flood hydrodynamics interrupted by the operation of hydraulic structures to support object-level risk assessment. Source-term and flux-term coupling approaches are applied and implemented to represent different types of hydraulic structures in the model. For hydraulic structures such as a sluicegate, the flux-term coupling approach may lead to more accurate results, as indicated by the calculated values of NSE and RMSE for different test cases. The model is further applied to predict different design flood scenarios with rainfall inputs created using Intensity-Duration-Frequency relationships, Chicago Design Storm, and surveyed data. The simulation results are combined with established vehicle instability formulas and depth-damage curves to assess the flood impact on individual objects in an urbanized case study area in Zhejiang Province, China. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Perovskite quantum dots (PQDs) have been widely used to realize light‐emitting diodes (LEDs) with high color purity, efficiency, and wide color gamut. Due to the demand for huge amount of display information, development on high‐resolution PQD LEDs (PQLEDs) with massive pixel density are highly desired. Herein, high‐resolution PQLEDs are fabricated by using nanoimprint technique. By preparing a honeycomb‐insulating polymer polymethyl methacrylate (PMMA) film, the high‐resolution pixelated quantum dot emissive layer is formed. In addition, the leakage current of the device is significantly reduced because the direct contact between electron transport layer and hole transport layer is avoided. The PQLEDs based on nanoimprint technology show a resolution of 9072 pixels per inch (PPI), and a maximum external quantum efficiency (EQE) and brightness of 7.81% and 146 400 cd m−2, respectively. The results demonstrate that high‐resolution PQLEDs can be realized by using nanoimprint technology, holding great commercial potential for next‐generation near‐eye displays. [ABSTRACT FROM AUTHOR]

Typhoon Faxai hit Japan in 2019 and severely damaged the Tokyo metropolitan area. To mitigate such damages, a good track forecast is necessary even before the typhoon formation. To investigate the predictability of the genesis and movement of a precursor vortex and its relationship with the synoptic‐scale flow, 100‐member ensemble simulations of Typhoon Faxai were performed using a 14‐km mesh global nonhydrostatic atmospheric model, which started from 16 different initial days (i.e., 1,600 members in total). The results show that the model could predict an enhanced risk of a Faxai‐like vortex heading toward Japan 2 weeks before landfall, which was up to 70%. The reason for the enhancement was a rapid increase in the members reproducing a precursor vortex from 15 to 12 days before landfall in Japan. In addition, the upper‐tropospheric vortex played an essential role in the track simulation of Faxai. Plain Language Summary: Tropical cyclones severely damage coastal regions yearly. Typhoon Faxai hit Japan in 2019 and severely damaged buildings, power grids, and cell phone networks in the Tokyo metropolitan area. To mitigate such damages, better track forecast is necessary even from the timing before typhoon formation. A large ensemble member (1,600‐member in total) and high‐resolution (14‐km) simulation was performed to investigate the genesis and movement of the precursor vortex of Faxai in 2019 and its relationship with the synoptic‐scale environmental flow using a global nonhydrostatic atmospheric model on the Supercomputer Fugaku. The results show the model could predict an enhanced risk of a Faxai‐like vortex heading toward Japan 2 weeks before landfall. A reason for the enhancement was a rapid increase in the members reproducing a precursor vortex from 15 to 12 days before landfall in Japan. In addition, the upper‐tropospheric vortex played an essential role in the movement of the Faxai‐like vortex. Key Points: A 1,600‐member ensemble simulation in total for Typhoon Faxai (2019) was performed using a 14‐km mesh nonhydrostatic atmospheric modelThe model successfully predicts the risk of Faxai's landfall in Japan 2 weeks in advanceReproducibilities of the precursor vortex and upper‐tropospheric vortex yield good prediction of the formation and track of Faxai [ABSTRACT FROM AUTHOR]

Successful salient object detection is largely dependent on large-scale fine-grained annotated datasets. However, pixel-level annotation is a laborious process compared with weak labels and scant research has been done on high-resolution images. To mitigate these drawbacks, we propose a distinctive network to explore salient object in high-resolution images under scribble-supervised and relabel a previous high-resolution dataset with scribbles, namely Scr-HRSOD, in which each image is labelled in a few seconds. Since scribble labels lack structural information about objects, a boundary structure maintenance branch with shallow layers is introduced to capture low-level spatial details. Within the constraint of boundary branches, a lightweight contextual semantic branch process compressed inputs to obtain high-level semantic context and iteratively propagates the partially annotated pixels to surrounding similar regions, which are then employed as pseudo-labels to supervise the network. Extensive evaluations on five datasets illustrate the effectiveness of our introduced method. On HRSOD datasets, we achieve higher 0.861 Fmax and 0.887 Sm values, which outperforms the existing foremost weakly supervised methods and even the fully supervised methods. [ABSTRACT FROM AUTHOR]

A complementary metal–oxide–semiconductor (CMOS) detector array is proposed to improve the sub-terahertz imaging resolution for objects in the conveyor belt system. The image resolution is limited to the implemented configuration, such as the wide spacing in the detector array, the high conveyor belt speed, and the slow response of the signal conditioning block. The proposed array can improve the image resolution in the direction perpendicular to the movement of the belt, which is determined by the size and interval of the detector pixel, by configuring the array into two replaceable columns located at the misaligned horizontal positions. Replaceable detector unit pixels are individually attached to the motherboard after measuring and evaluating the detection performance to construct the proposed array. The intensities of 32 detector pixels placed under the conveyor belt with a width of 160 mm were initially calibrated in every image, including the beam pattern of 0.2 THz signals generated from the gyrotron. The image resolution of the perpendicular direction obtained from the proposed array was measured to be approximately 5 mm at a conveyor belt speed of 16 mm/s, demonstrating a 200% improvement in resolution compared to the conventional linear array under the same conditions. [ABSTRACT FROM AUTHOR]

Now object detection based on deep learning tries different strategies. It uses fewer data training networks to achieve the effect of large dataset training. However, the existing methods usually do not achieve the balance between network parameters and training data. It makes the information provided by a small amount of picture data insufficient to optimize model parameters, resulting in unsatisfactory detection results. To improve the accuracy of few shot object detection, this paper proposes a network based on the transformer and high-resolution feature extraction (THR). High-resolution feature extractionmaintains the resolution representation of the image. Channels and spatial attention are used to make the network focus on features that are more useful to the object. In addition, the recently popular transformer is used to fuse the features of the existing object. This compensates for the previous network failure by making full use of existing object features. Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection. [ABSTRACT FROM AUTHOR]

Developing scintillators with high light yield (LY), superior irradiation stability, and weak afterglow is of significance for the realization of low-dose high-resolution X-ray excited optical luminescence (XEOL) imaging. Lanthanide doped fluoride nanoparticles possess low toxicity, superior environmental stability, facial fabrication process, and tunable emissions, which are appropriate candidates for the next generation nanoscintillators (NSs). However, the low LY and strong positive hysteresis greatly restrict their practical application. Here, we propose an effective strategy that engineers energy gap to significantly enhance the LY. Our results verify that the tetragonal LiLuF4 host benefits the crystal level splitting of Tb3+ ions, which greatly promotes the electrons population on the Tb3+:5D4 level followed by the enhanced LY. The LY of LiLuF4:Tb@LiLuF4 NSs is calculated to be ∼ 31,169 photons/MeV, which is much higher than the lead halide perovskite colloidal CsPbBr3 (∼ 21,000 photons/MeV) and LuAG:Ce (∼ 22,000 photons/MeV) scintillators. Moreover, the positive hysteresis is remarkably restricted after coating a thin shell. The X-ray detection limit and spatial resolution are measured to be ∼ 21.27 nGy/s and ∼ 7.2 lp/mm, respectively. We further verify that this core/shell NS can be employed as scintillating screen to realize XEOL imaging under the low dose rate of 13.86 µGy/s. Our results provide an effective route to develop high performance NSs, which will promote great opportunities for the development of low-dose high-resolution XEOL imaging devices. [ABSTRACT FROM AUTHOR]

To address the problem of a small linear sensing range in differential confocal measurement methods, this study proposes a large linear sensing range measurement method for a laser differential confocal (LDC) based on transverse split-spot detection using a multi-element detector (MED). This method converts the axial defocusing change of the measured object surface into transverse movement of the spot on the detected focal plane through the principle of transverse differential confocal (TDC), where the intensity of the spot on the focal plane is split and detected by MED. Two-by-two differential subtraction and normalization are processed to obtain multiple sets of normalized TDC response curves, which realize a large-range sensing confocal measurement without sacrificing the high-resolution axial fixed-focus measurement. The analytical and experimental validation shows that the axial resolution of this method is 0.5 nm, the linear sensing range is up to 13.667 µm, which is about 11.4 times of the linear sensing range of TDC, and the maximum fitting error is 2.3 nm in the large sensing measurement range. This provides a new method for high-precision, large-range, and fast sensor-scanning measurement of precision element surfaces. [ABSTRACT FROM AUTHOR]

Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional fixed monitoring methods. However, the sparsity of mobile monitoring data still makes it a challenge to recover the high-resolution pollutant concentration across an entire area. To tackle the sparsity issue and fulfill a prediction of the spatiotemporal distribution of PM 2.5 , a high-resolution urban PM 2.5 prediction method was proposed based on mobile monitoring data in this study. This method enables prediction with a spatial resolution of 500m × 500m and a temporal resolution of 1 h. First, a Light Gradient Boosting Machine (LightGBM) was trained using mobile monitoring of PM 2.5 concentration and exogenous features to obtain complete spatiotemporal PM 2.5 concentration. Second, a model consisting of Convolutional Neural Network and Transformer (CNN-Transformer) with a customised loss function was established to predict high-resolution PM 2.5 concentration based on complete spatiotemporal data. The method was validated using real-world data collected from Cangzhou, China. The numerical results from cross-validation showed an R2 of 0.925 for imputation and 0.887 for prediction, demonstrating this method is suitable for high-resolution spatiotemporal prediction of PM 2.5 concentration based on mobile monitoring data. [Display omitted] • Using LightGBM and spatiotemporal features for PM 2.5 concentration imputation. • Establishing CNN-Transformer for high-resolution PM 2.5 concentration prediction. • Discussing the influence of the number of monitoring vehicles on prediction results. [ABSTRACT FROM AUTHOR]

Machine learning, especially deep learning, can outperform traditional atmospheric models in air quality assessment, offering enhanced efficiency and accuracy without relying on detailed emission inventories and atmospheric chemical mechanisms. Despite their predictive power, deep learning models often grapple with the perception of being "black boxes" due to their intricate architectures. Here, we develop an attention-based convolutional neural network (CNN-attention) model that incorporates observational data, the parallelized large-eddy-simulation model (PALM), and urban morphology data for high-resolution spatial estimation of urban air quality. Our findings indicate that the CNN-attention model outperforms traditional CNN with higher accuracy and efficiency, achieving R2 = 0.987 and root mean square error (RMSE) = 0.15 mg/m3, while significantly reducing training time and memory usage. Compared to traditional machine learning models, the CNN exhibits higher R2 values and lower RMSE, showcasing its adeptness at capturing complex nonlinear patterns. The inclusion of attention layer further improves the model's performance by dynamically assigning attention scores to key features, enabling the model to focus on areas of critical emissions and distinctive urban features such as highways, arterial roads, intersections, and dense building clusters. This approach also reveals fluid dynamical principles, highlighting the significant disparities in pollutant concentration across roadways caused by atmospheric turbulence, and the distinct plume formations influenced by land use and topography. When applied to various urban settings, the CNN-attention model exhibits superior generalizability and transferability. This study provides valuable scientific insights and technical support for urban planning, air quality management, and exposure risk evaluation. • The model integrates observational and simulation data, outperforming traditional CNNs in air quality estimation. • Dynamic attention weights boost efficiency by focusing on critical features. • Model reveals fluid dynamics laws, enhancing understanding of pollution patterns. • Model exhibits strong transferability and generalization across diverse cities. [ABSTRACT FROM AUTHOR]

Hainan province which located in the southernmost part of China is an important special economic zone and experimental zone of free trade.However， the studies of underlying surface impact on weather prediction in Hainan is fewer than other places.To understand the impact of underlying surface on numerical weather prediction， the WRF （Weather Research and Forecasting） model is used to simulate the meteorological field over Hainan in 2013.The comparison between default land surface data （land use， vegetation fraction， terrain， and soil type） and new high-resolution land surface data， i.e.Moderate Resolution Imaging Spectroradiometer （MODIS） land use data in 2013 processed based on IGBP （International geosphere biosphere program） classification standard， vegetation fraction data calculated based on MODIS normalized difference vegetation index （NDVI） in 2013， Shuttle Radar Topography Mission 3 （SRTM3） terrain data and Harmonized World Soil Database （HWSD） soil type data is investigated through numerical sensitivity tests to analyze the impact on meteorological field simulations.The study is divided into four parts.The first part is the introduction； The second part mainly introduces the research area， data sources， model design and evaluation methods； The third part compares the simulation results of using new high-resolution land surface data with the WRF default land surface data and evaluates the simulation results； Then the fourth part is about the result and discussion.The result indicates that using default or new high-resolution land surface data can both simulate the temporal-spatial distribution of regional meteorological fields.Using the new high-resolution land surface data can improve the simulated results of 2 m temperature and 2 m relative humidity， with the Root Mean Square Error （RMSE） reduced by 7.2% （6.5%） and 6.1%（7.7%） in spring （summer）， and the Hit Rate （HR） increased by 3.7%（2.8%） and 3.2%（2.9%） in spring （summer）.The impact of high-resolution land surface on 10 m wind speed simulation seems smaller than that on 2 m temperature and 2 m relative humidity.10 m wind speed error reduces slightly after using high-resolution land surface data， with the RMSE reduced by 3.8%（4.5%） and HR increased by 2.1%（2.9%） in spring （summer）.Compared to other underlying surface data， land use data has more significant impact on near-surface meteorological fields simulation.In general， using the high-resolution land surface data in Hainan can improve the accuracy of WRF model and reduce the simulation error.This study can deepen the understanding of numerical forecast products for forecasters， improve the accuracy of weather forecast， and provide theoretical basis for Seismic Prevention and Disaster Mitigation.

Based on the global high-resolution climate model CAMS-CSM developed by Chinese Academy of Meteorological Sciences, the seasonal prediction skill of downward short-wave radiation flux (DSWRF) in China and three key regions is evaluated during the period of 2011-2020. The results show that the high-resolution version of CAMS-CSM can well predict the seasonal and interannual variability of DSWRF, but the predicted intensity is relatively weaker in spring and summer, while slightly stronger in autumn and winter compared to the observation. The prediction of the climate mean state doesn't change much with the lead time, indicating the systematic bias of the DSWRF is formed steadily in the early stage of model integration. However, there are obvious diversities in the prediction skill of the DSWRF anomalies in different seasons and different regions. From the anomalous spatial and temporal correlation coefficients, it can be noted that the prediction skill is higher in Inner Mongolia and Northwest China in autumn and winter, while lower in some areas of Beijing-Tianjin-Hebei in summer and autumn. From the perspective of comprehensive assessment of trend anomalies (P index), the model can score more than 70 points for all seasons in China at 0-month lead time, and the best performance can be close to 80 points for summer and autumn in Northwest China. Overall, the high-resolution version of CAMS-CSM climate model has certain prediction capability for DSWRF at 0-1 month ahead in China, especially in northwest regions where the solar-radiation is rich all year, which can provide specific scientific guidance for the future DSWRF short-term prediction and the solar energy site selection. In addition to the systematic bias of the model, there is a significant negative correlation between the predicted DSWRF bias and the total cloud cover bias, indicating that the bias of DSWRF prediction mainly comes from the simulation bias of total cloud cover, especially in spring and summer, as well as in autumn and winter in South China. In order to improve the prediction accuracy of DSWRF, it is an effective way to reduce the uncertainty of the model cloud microphysical processes. However, it is difficult to meet the demand of practical application with only high-resolution climate model, and its results still need to be processed with methods such as dynamic downscaling and bias revision to further improve the prediction skills.

• A high-resolution helicopter rotor flowfield simulation method is proposed based on the TENO-P scheme. • The TENO-P scheme performs better in resolving the multi-scale blade tip vortex structures and the sound pressure peak than the WENO5 and TENO5 schemes. • The negative part of the harmonic control is beneficial for reducing BVI noise while the positive part of the harmonic control exacerbates the BVI noise. • The HHC reduces the collective pitch angle in the region where the tip vortex is about to be disturbed, decreasing the strength of the blade tip vortex in this region. • The reduction in vortex strength reduces the strength of the parallel and near-parallel interaction on the advancing side, leading to the reduction of the positive peak impulsive of the BVI noise. Accurately predicting the formation, evolution, and breakdown of helicopter blade tip vortex is crucial for simulating the Blade-Vortex Interaction (BVI) phenomenon. Firstly, the high-order Perturbed polynomial reconstructed Targeted Essentially Non-Oscillatory (TENO-P) scheme proposed by our research group is employed to improve the resolution of the helicopter rotor flowfield solver. The TENO-P scheme, building on the fifth-order TENO5 scheme, achieves one-order of accuracy improvement by adaptively adjusting the values of the free-parameter introduced by perturbed polynomial reconstruction. Subsequently, the AH-1 helicopter model rotor undergoing blade-vortex interaction is analyzed using the improved rotor flowfield solver and the Farassat-1A formula. The implementation of the TENO-P scheme notably enhances the resolution of the rotor flowfield solver in resolving the blade tip vortex structures, and the predicted noise results are in good agreement with the experimental data. Finally, the alteration of the BVI noise and its reduction mechanism of the AH-1 model rotor under different Higher Harmonic Controls (HHC) are analyzed. The findings show that the phase modulation margin increases with a decrease in harmonic order, and the noise reduction effect significantly improves as well. The negative component of the higher harmonic control is beneficial for reducing BVI noise while the positive component of the higher harmonic control exacerbates the BVI noise. The HHC reduces the collective pitch angle in the region where the tip vortex is about to be disturbed, decreasing the strength of the blade tip vortex in this region. This reduction weakens the strength of the parallel and near-parallel interaction on the advancing side, leads to the reduction of the positive peak impulsive of the BVI noise, and consequently lowers the intensity of the BVI noise. [ABSTRACT FROM AUTHOR]

Brain tumor segmentation in MRI images is challenging due to variability in tumor characteristics and low contrast. We propose HRGUNet, which combines a high-resolution generative adversarial network with an improved UNet architecture to enhance segmentation accuracy. Our proposed GAN model uses an innovative discriminator design that is able to process complete tumor labels as input. This approach can better ensure that the generator produces realistic tumor labels compared to some existing GAN models that only use local features. Additionally, we introduce a Multi-Scale Pyramid Fusion (MSPF) module to improve fine-grained feature extraction and a Refined Channel Attention (RCA) module to enhance focus on tumor regions. In comparative experiments, our method was verified on the BraTS2020 and BraTS2019 data sets, and the average Dice coefficient increased by 1.5% and 1.2% respectively, and the Hausdorff distance decreased by 23.9% and 15.2% respectively, showing its robustness and generalization for segmenting complex tumor structures. [ABSTRACT FROM AUTHOR]

Scintillators with high thermal stability and excellent spatial resolution are desirable for X-ray imaging applications in harsh environments. Here, Tb3+-doped borosilicate glass scintillators were prepared using conventional melt-quenching method. The optimized Tb3+-doped borosilicate glass (G-15Tb) scintillator exhibited a significantly higher X-ray excited luminescence intensity (147 % of Bi 4 Ge 3 O 12). Furthermore, it possessed a remarkable imaging spatial resolution of 20 lp/mm, surpassing the standard requirement for X-ray imaging and medical computed tomography scans (2 lp/mm). Moreover, the X-ray excited luminescence intensity of G-15Tb glass sample remained unchanged even after being submerged in water for 30 days, indicating exceptional water resistance. Additionally, the integral intensity of photoluminescence at 573 K is 96.7 % of its initial value. Most importantly, the G-15Tb glass scintillator remains high-quality X-ray images even at elevated operating temperatures. Therefore, the Tb3+-doped borosilicate glass scintillator holds immense potential for high-resolution X-ray imaging in harsh environments. • Tb3+-doped borosilicate glasses were synthesized for scintillator. • The G-15Tb glass exhibits excellent thermal stability and water resistance. • X-ray imaging with impressive spatial resolution reaches 20 lp mm−1. • The prepared glass for high-resolution X-ray detection in harsh conditions. • All glass samples exhibit 87 % transmittance at 550 nm. [ABSTRACT FROM AUTHOR]

Accurate wind speed forecasting (WSF) plays a pivotal role in anticipating the power output of wind farms. Nevertheless, the stochastic and variable nature of wind speed presents a significant challenge in achieving accurate WSF. Hence, this study proposes a novel 4D high-resolution system, Windformer, for wind speed vector forecasting (WSVF). Windformer combines the ability of convolutional neural networks (CNNs) for feature extraction and transformers based on attention mechanisms for information fusion. In the Windformer, the input and output layers are mainly composed of 3D CNNs. The input layer is employed for feature extraction and information compression, while the output layer is tasked with recovering the WSV field. The key components of the model consist of encoders and decoders built upon temporal shifted window multi-head self-attention. This architecture is capable of effectively integrating spatio-temporal information. Trained on 39 years of regional reanalysis data, Windformer obtains the most accurate forecast results compared to 5 WSF baseline models. Most importantly, within the next 6 hours, Windformer demonstrates higher accuracy compared to the High-Resolution Deterministic Forecast (HRES) from the European Centre for Medium-Range Weather Forecasts (ECMWF). • Windformer enables high-resolution regional-level wind speed vector predictions. • Windformer surpasses baseline models in forecasting wind speeds for the upcoming 1 to 48 h. • Training Windformer only requires two RTX4090 GPUs. • Windformer shows an MAE about 0.05–0.2 m/s lower than HRES within the next 6-hour wind speed forecast. • Windformer efficiently handles spatio-temporal information using temporal shifted window multi-head self-attention. [ABSTRACT FROM AUTHOR]

We present a new method for imaging dissolved manganese at millimeter scale by coupling DET (diffusive equilibrium in thin film) and colorimetric techniques. The method is an adaptation of the porphyrin approach for the measurement of dissolved Mn by substitution of Mn(II) and Mn(III) to Cd in the Cd(II)–POR complex. Optimization of the Cd-POR concentrations was required for transposition to 2D-DET. A commercial flatbed scanner and a hyperspectral camera were used for imaging. Using the hyperspectral camera, detection limit is about 5 μM and measuring range is up to 520 μM. The method was applied on the field in a tidal mudflat of the French Atlantic coast and in sediments inhabited by polychaetes. These first images allowed to precisely describe two-dimensional millimeter features such as burrows and highlighted the role of bioirrigation in benthic Mn fluxes. This new technique offers the possibility to investigate the reactivity of microenvironments towards dissolved Mn in two dimensions in a wide range of laboratory and in situ studies using a non-destructive tool. • A new method for measuring 2D dissolved Mn at millimeter-scale is presented. • Development was achieved by coupling DET (diffusive equilibrium in thin film) and a porphyrin colorimetric method. • Mn sediment microenvironments can be investigated using a non-destructive tool. • Bioirrigation effect can be evidenced through the observation of burrow structures. [ABSTRACT FROM AUTHOR]

Recent advancements in high-resolution ion mobility spectrometry-mass spectrometry (IMS-MS) have enabled the separation of isotopologues and isotopomers based on their mass distribution-based isotopic shifts (i.e., changes in center of mass and moments of inertia). To better understand the fundamental nature of these isotopic shifts, we investigated whether they were additive in nature by introducing varying isotopic substitutions (e.g., 13C, 2H/D, and 81Br) through either hydrogen deuterium exchange or permethylation. From there, we measured the relative arrival times between light and heavy isotopologues with high-resolution cyclic ion mobility separations. Globally, we observed that the isotopic shifts were approximately additive in nature regardless of the molecule system or specific isomer studied. Furthermore, we discovered that additivity occurs in the isotopic shifts irrespective of the absolute shift, potentially indicating this observation may be more global in nature. We believe that our findings will serve to better understand the fundamental nature of mass distribution-based isotopic shifts and will inform theoretical ion mobility calculations in the future. [Display omitted] • Mass distribution-based isotopic shifts stem from changes in center of mass and moments of inertia. • Additivity of isotopic shifts was observed across varying molecule classes. • Additivity was observed irrespective of the isomer-specific nature of isotopic shifts. [ABSTRACT FROM AUTHOR]

Data are presented demonstrating that absorbance detection can be used during high-speed sedimentation velocity analytical ultracentrifugation (hs-SV-AUC) experiments to characterize the size distribution of adeno-associated virus (AAV) drug products accurately. Advantages and limitations of being able to use this detector in this specific type of SV-AUC experiment are discussed. [Display omitted] • Valid absorbance data can be obtained when doing AAV hs-SV-AUC. • Dual-wavelength characterization of AAV hs-SV-AUC peaks. • Dual-wavelength hs-SV-AUC shows low speed SV-AUC AAV full peak consists of 3 peaks. [ABSTRACT FROM AUTHOR]

Mesoscale eddies are prevalent throughout the global ocean and have significant implications on the exchange of heat, salt, volume, and biogeochemical properties. These small-scale features can potentially influence regional and global climate systems. However, the effects of climate change on ocean eddies remain uncertain due to limited long-term observational data. To address this knowledge gap, our study focuses on examining the impact of greenhouse warming on surface mesoscale eddy characteristics, utilizing a high-resolution climate simulation project. Our model experiments provided valuable insights into the potential effects of greenhouse warming on mesoscale eddies, suggesting that mesoscale eddies will likely become more frequent under greenhouse warming conditions and exhibit larger amplitudes and radii, especially in regions characterized by strong ocean currents such as the Antarctic Circumpolar Current and western boundary currents. However, a distinctive pattern emerged in the Gulf Stream, with increases in eddy occurrence and radius and significant decreases in eddy amplitude. This phenomenon can be attributed to the relationship between eddy lifespans and their properties. Specifically, in the Kuroshio Current, the amplitude of eddies increased due to the increased occurrence of long-lived eddies. In contrast, in the Gulf Stream, the amplitude of eddies decreased significantly due to the decreased occurrence of long-lived eddies. This distinction arises from the fact that long-lived eddies can accumulate more energy than shorter-lived eddies throughout their lifetime. These findings provide valuable insights into the complex dynamics of mesoscale eddies in a warming world.

This paper presents the design and development of a high-resolution 3D ultrasound imaging system based on a 1 × 256 piezoelectric ring array, achieving an accuracy of 0.1 mm in both ascending and descending modes. The system achieves an imaging spatial resolution of approximately 0.78 mm. A 256 × 32 cylindrical sensor array and a digital phantom of breast tissue were constructed using the k-Wave toolbox. The signal is acquired layer by layer using 3D acoustic time-domain simulation, resulting in the collection of data from each of the 32 layers. The 1 × 256 ring array moves on a vertical trajectory from the chest wall to the nipple at a constant speed. A data set was collected at intervals of 1.5 mm, resulting in a total of 32 data sets. Surface rendering and volume rendering algorithms were used to reconstruct 3D ultrasound images from the volume data obtained via simulation so that the smallest simulated reconstructed lesion had a diameter of 0.3 mm. The reconstructed three-dimensional image derived from the experimental data exhibits the contour of the breast model along with its internal mass. Reconstructable dimensions can be achieved up to approximately 0.78 mm. The feasibility of applying the system to 3D breast ultrasound imaging has been demonstrated, demonstrating its attributes of resolution, precision, and exceptional efficiency.

The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped the h e i ght of t rees a nd b uildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments.

The use of deep learning methods to extract buildings from remote sensing images is a key contemporary research focus, and traditional deep convolutional networks continue to exhibit limitations in this regard. This study introduces a novel multi-feature fusion network (MFFNet), with the aim of enhancing the accuracy of building extraction from high-resolution remote sensing images of various sources. MFFNet improves feature capture for building targets by integrating deep semantic information from various attention mechanisms with multi-scale spatial information from a spatial pyramid module, significantly enhancing the results of building extraction. The performance of MFFNet was tested on three datasets: the self-constructed Jilin-1 building dataset, the Massachusetts building dataset, and the WHU building dataset. Notably, experimental results from the Jilin-1 building dataset demonstrated that MFFNet achieved an average intersection over union (MIoU) of 89.69%, an accuracy of 97.05%, a recall rate of 94.25%, a precision of 94.66%, and an F1 score of 94.82%. Comparisons with the other two public datasets also showed MFFNet’s significant advantages over traditional deep convolutional networks. These results confirm the superiority of MFFNet in extracting buildings from different high-resolution remote sensing data compared to other network models.

The representation of 2-m air temperature and 10-m wind speed in the high-resolution (with a 2.5-km grid spacing) Copernicus Arctic Regional Reanalysis (CARRA) and the coarser resolution (ca. 31-km grid spacing) global European Center for Medium-range Weather Forecasts fifth-generation reanalysis (ERA5) for Svalbard, northern Norway, Sweden and Finland is evaluated against observations. The largest differences between the two reanalyses are found in regions with complex terrain and coastlines, and over the sea ice for temperature in winter. In most aspects, CARRA outperforms ERA5 in its agreement with the observations, but the value added by CARRA varies with region and season. Furthermore, the added value by CARRA is seen for both parameters but is more pronounced for temperature than wind speed. CARRA is in better agreement with observations in terms of general evaluation metrics like bias and standard deviation of the errors, is more similar to the observed spatial and temporal variability and better captures local extremes. A better representation of high-impact weather like polar lows, vessel icing and warm spells during winter is also demonstrated. Finally, it is shown that a substantial part of the difference between reanalyses and observations is due to representativeness issues, that is, sub-grid variability, which cannot be represented in gridded data. This representativeness error is larger in ERA5 than in CARRA, but the fraction of the total error is estimated to be similar in the two analyses for temperature but larger in ERA5 for wind speed.

Abstract 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

Abstract 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.

Abstract Accurate plantar pressure mapping systems with low dependence on the external power supply are highly desired for preventative healthcare and medical diagnosis. Herein, we propose a self‐powered smart insole system that can perform both static and dynamic plantar pressure mapping with high accuracy. The smart insole system integrates an insole‐shaped sensing unit, a multi‐channel data acquisition board, and a data storage module. The smart insole consists of a 44‐pixel sensor array based on triboelectric nanogenerators (TENGs) to transduce pressure to the electrical signal. By optimizing the sensor architecture and the system's robustness, the smart insole achieves high sensitivity, good error‐tolerance capability, excellent durability, and short response–recovery time. Various gait and mobility patterns, such as standing, introversion/extraversion, throwing, and surpassing obstacles, can be distinguished by analyzing the acquired electrical signals. This work paves the way for self‐powered wearable devices for gait monitoring, which might enable a new modality of medical diagnosis.

Abstract Typhoon Faxai hit Japan in 2019 and severely damaged the Tokyo metropolitan area. To mitigate such damages, a good track forecast is necessary even before the typhoon formation. To investigate the predictability of the genesis and movement of a precursor vortex and its relationship with the synoptic‐scale flow, 100‐member ensemble simulations of Typhoon Faxai were performed using a 14‐km mesh global nonhydrostatic atmospheric model, which started from 16 different initial days (i.e., 1,600 members in total). The results show that the model could predict an enhanced risk of a Faxai‐like vortex heading toward Japan 2 weeks before landfall, which was up to 70%. The reason for the enhancement was a rapid increase in the members reproducing a precursor vortex from 15 to 12 days before landfall in Japan. In addition, the upper‐tropospheric vortex played an essential role in the track simulation of Faxai.

Background and Aims: Legalization of cannabis (CB) for both medicinal and, in some states, recreational use, has given rise to increasing usage rates across the country. Of particular concern are indications that frequent CB use may be selectively harmful to the developing adolescent brain compared with adult-onset usage. However, the long-term effects of heavy, adolescent CB use on brain structure and cognitive performance in late-life remain unknown. A critical brain region is the hippocampus (HC), where there is a striking intersection between high concentrations of cannabinoid 1 (CB1) receptors and age-related pathology. Design: We investigated whether older adults (average age=66.6+7.2 years old) with a history of early life CB use show morphological differences in hippocampal subregions compared with older, nonusers. Methods: We performed high-resolution magnetic resonance imaging combined with computational techniques to assess cortical thickness of the medial temporal lobe, neuropsychological testing, and extensive drug use histories on 50 subjects (24 formerly heavy cannabis users [CB+ group] abstinent for an average of 28.7 years, 26 nonusers [CB- group]). We investigated group differences in hippocampal subregions, controlling for age, sex, and intelligence (as measured by the Wechsler Test of Adult Reading), years of education, and cigarette use. Results: The CB+ subjects exhibited thinner cortices in subfields cornu ammonis 1 [CA1; F(1,42)=9.96, p=0.0003], and CA2, 3, and the dentate gyrus [CA23DG; F(1,42)=23.17, p

Functional magnetic resonance imaging (fMRI) at Ultra-high field (UHF, ≥ 7 T) benefits from significant gains in the BOLD contrast-to-noise ratio (CNR) and temporal signal-to-noise ratio (tSNR) compared to conventional field strengths (3 T). Although these improvements enabled researchers to study the human brain to unprecedented spatial resolution, the blood pooling effect reduces the spatial specificity of the widely-used gradient-echo BOLD acquisitions. In this context, vascular space occupancy (VASO-CBV) imaging may be advantageous since it is proposed to have a higher spatial specificity than BOLD. We hypothesized that the assumed higher specificity of VASO-CBV imaging would translate to reduced overlap in fine-scale digit representation maps compared to BOLD-based digit maps. We used sub-millimeter resolution VASO fMRI at 7 T to map VASO-CBV and BOLD responses simultaneously in the motor and somatosensory cortices during individual finger movement tasks. We assessed the cortical overlap in different ways, first by calculating similarity coefficient metrics (DICE and Jaccard) and second by calculating selectivity measures. In addition, we demonstrate a consistent topographical organization of the targeted digit representations (thumb-index-little finger) in the motor areas. We show that the VASO-CBV responses yielded less overlap between the digit clusters than BOLD, and other selectivity measures were higher for VASO-CBV too. In summary, these results were consistent across metrics and participants, confirming the higher spatial specificity of VASO-CBV compared to BOLD. [ABSTRACT FROM AUTHOR]

Background Atrial tachycardias (ATs) frequently develop after a surgical Maze procedure. We aimed to elucidate the electrophysiologic mechanisms and their arrhythmogenic substrates of these ATs. Methods and results We retrospectively reviewed 20 patients (14 females, mean age of 55.5 ± 8.6 years) with post-Maze ATs who underwent high-resolution mapping at three institutions. The slow conduction areas, reentry circuits, voltage signals, complex electrograms, and their correlation with the surgical incisions and lesions placed in the surgical Maze procedures were analyzed. Thirty-six ATs with a mean cycle length of 260.0 ± 67.6 ms were mapped in these patients. Among them, 22 (61.1%) were anatomical macro-reentrant ATs (AMAT), 12 (33.3%) non-AMATs (localized ATs), and 2 (5.6%) focal ATs, respectively. Epicardial conduction bridges were observed in 6/20 (30.0%) patients and 7/36 (19.4%) ATs. Different arrhythmogenic substrates were identified in these ATs, including slow conduction regions within the previous lesion areas or between the incisions and anatomical structures, the prolonged activation pathways caused by the short lesions connecting the tricuspid annulus, and the circuits around the long incisions and/or lesions. Conclusions Reentry is the main mechanism of the post-Maze ATs. The pro-arrhythmic substrates are most likely caused by surgical incisions and lesions. The slow conduction regions and the protected channels yielded from these areas are the major arrhythmogenic factors. [ABSTRACT FROM AUTHOR]

Excessive sediment runoff as a result of anthropogenic activities is a major concern for watershed ecologic health. This study sought to determine the sources, storage, and delivery of sediment using a sediment budget approach for the predominantly pasture and forested Smith Creek watershed, Virginia United States, a tributary to the Chesapeake Bay. Utilizing a novel combination of the Universal Soil Loss Equation (USLE) model and an index of connectivity along with field surveys of channels, this study indicated that streambanks and pastures were major sources of sediment. Overestimation of fine‐grained sediment flux exported from the watershed according to this study's models (3811 Mg/year) compared to export measured at the outlet (2918 Mg/year) most likely indicates underestimation of storage in the watershed from unaccounted for geomorphic features (ponds, toe slopes, and colluvial slopes). Sediment budget results indicating that streambanks are a major source of sediment in the watershed support previous sediment fingerprinting results and provide a framework for managers to address the sediment problem in Smith Creek and similar tributaries to the Chesapeake Bay. [ABSTRACT FROM AUTHOR]

In this study, a novel structural design of the p‐type low‐temperature polycrystalline silicon thin‐film transistors (p‐type LTPS TFTs) applied to the pixel structure of displays is proposed. Compared to the conventional pixel structure of displays, the proposed architecture can achieve the aperture ratio improvement by stacking the switch thin‐film transistor and the storage capacitor in a pixel region to enlarge the active space. Therefore, the demands of high‐resolution characteristics, such as a high aperture ratio, and high pixel densities for high‐end displays or novel technologies, can be satisfied by the adoption of the proposed design concept. Furthermore, the discussion of experimental and simulated results in terms of device physics of the transistor indicates that proposed TFTs possess higher performance and reliability properties. By modulating the geometry of the drain‐connected bottom metal in stacked TFTs, output characteristics and hot carrier phenomenon in devices can be further improved. Time‐dependent transfer characteristics, extracted electrical parameters, and numerical simulation results are performed to support our design. [ABSTRACT FROM AUTHOR]

Image deraining aims to restore the clean scenes of rainy images, which facilitates a number of outdoor vision systems, such as autonomous driving, unmanned aerial vehicles and surveillance systems. This paper proposes a high-resolution detail-recovering image deraining network (HDRD-Net) to effectively remove rain streaks and recover lost details, as well as improving the quality of derained images. HDRD-Net consists of three sub-networks. First, we combine the residual network and Squeeze-and-Excitation block for rain streak removal. Second, we integrate the Structure Detail Context Aggregation block into the detail-recovering network to extract detail features form rainy images. Third, a dual super-resolution reconstruction network is utilized to enhance the quality of derained images. In addition, we extend the Rain100 dataset by incorporating low-resolution rainy images to construct a new Rain100++ dataset for high-resolution image deraining. Experimental results on several datasets show that HDRD-Net outperforms state-of-the-art methods in terms of rain removal, detail preservation and visual quality. [ABSTRACT FROM AUTHOR]

Background: Body surface gastric mapping (BSGM) is a new clinical tool for gastric motility diagnostics, providing high‐resolution data on gastric myoelectrical activity. Artifact contamination was a key challenge to reliable test interpretation in traditional electrogastrography. This study aimed to introduce and validate an automated artifact detection and rejection system for clinical BSGM applications. Methods: Ten patients with chronic gastric symptoms generated a variety of artifacts according to a standardized protocol (176 recordings) using a commercial BSGM system (Alimetry, New Zealand). An automated artifact detection and rejection algorithm was developed, and its performance was compared with a reference standard comprising consensus labeling by 3 analysis experts, followed by comparison with 6 clinicians (3 untrained and 3 trained in artifact detection). Inter‐rater reliability was calculated using Fleiss' kappa. Key Results: Inter‐rater reliability was 0.84 (95% CI:0.77–0.90) among experts, 0.76 (95% CI:0.68–0.83) among untrained clinicians, and 0.71 (95% CI:0.62–0.79) among trained clinicians. The sensitivity and specificity of the algorithm against experts was 96% (95% CI:91%–100%) and 95% (95% CI:90%–99%), respectively, vs 77% (95% CI:68%–85%) and 99% (95% CI:96%–100%) against untrained clinicians, and 97% (95% CI:92%–100%) and 88% (95% CI:82%–94%) against trained clinicians. Conclusions & Inferences: An automated artifact detection and rejection algorithm was developed showing >95% sensitivity and specificity vs expert markers. This algorithm overcomes an important challenge in the clinical translation of BSGM and is now being routinely implemented in patient test interpretations. [ABSTRACT FROM AUTHOR]

Rapid extraction of liquefaction induced by strong earthquakes is helpful for earthquake intensity assessment and earthquake emergency response. Supervised classification methods are potentially more accurate and do not need pre-earthquake images. However, the current supervised classification methods depend on the precisely delineated polygons of liquefaction by manual and landcover maps. To overcome these shortcomings, this study proposed two binary classification methods (i.e., random forest and gradient boosting decision tree) based on typical samples. The proposed methods trained the two machine learning methods with different numbers of typical samples, then used the trained binary classification methods to extract the spatial distribution of liquefaction. Finally, a morphological transformation method was used for the postprocessing of the extracted liquefaction. The recognition accuracies of liquefaction were estimated by four evaluation indices, which all showed a score of about 90%. The spatial distribution of liquefaction pits is also consistent with the formation principle of liquefaction. This study demonstrates that the proposed binary classification methods based on machine learning could efficiently and quickly provide the spatial distribution of liquefaction based on post-earthquake emergency satellite images. [ABSTRACT FROM AUTHOR]

This paper presents a 16-bit 1-Msps successive-approximation-register analog-to-digital converter (ADC) with a split-ADC digital calibration scheme based on dynamic element matching. A multi-segment capacitor array with redundant bits is utilised for ensuring that missing-level errors are calibrated in the digital domain, reducing the area and power consumption. The key circuit modules are optimised, such as the low-power dual-mode cascade comparator and dynamic element matching control logic. The prototype is fabricated by a 0.18-μm CMOS technology, and it exhibits 170.47 dB figure of merit Schreier (FoMs), 15.04 bits effective number of bits (ENOB) and 119.50 dB spurs free dynamic range (SFDR). The peak differential nonlinearity (DNL) and integral nonlinearity (INL) are −0.422/0.536 LSB and −0.721/0.758 LSB, respectively. • A 16-bit 1 Msps SAR ADC is manufactured in 0.18 μm CMOS process with 170.47 dB FoMs. • A split-ADC digital calibration scheme based on dynamic element matching can improve the SFDR effectively. • A segmented capacitor array of 4 sub-capacitor arrays decreases the total capacitance of the high-resolution SAR ADC. • A dual-mode automatic zero comparator is utilised to reduce power consumption. [ABSTRACT FROM AUTHOR]

Echelle grating provides high spectral resolving power and diffraction efficiency in a broadband wavelength range by the Littrow mode. The spectrometer with the cross-dispersed echelle scheme has seen remarkable growth in recent decades. Rather than the conventional approach with common blazed grating, the cross-dispersed echelle scheme achieves the two-dimensional spatial distribution of the spectrum by one exposure without scanning in the broadband spectral range. It is the fastest and most sensitive spectroscopic technology as of now, and it has been extensively applied in commercial and astronomical spectrometers. In this review, we first highlight the characteristics of the echelle and then present the optical layout, detection approach, and method of calibration. Finally, we discuss the state-of-the-art implementations and applications of commercial and astronomical instruments. [ABSTRACT FROM AUTHOR]

This paper addresses the objectives in a complex context: the polarization mathematical and physical model of the basic components of the target/background is constructed by simulation modeling. The image fusion experiment is carried out based on a two-dimensional wavelet transformation. A high-resolution polarization imaging instrument was developed, and static experiments were carried out with polarization, infrared, and visible cameras. The result shows that using polarization imaging detection technology to detect physical evidence targets in complex backgrounds can effectively improve target contrast. It can enrich the target information, improve image quality, and improve target detection accuracy. [ABSTRACT FROM AUTHOR]

Phased structures for lossless ion manipulation offer significant improvements over the scanning second gate method for coupling with ion trap mass analyzers. With an experimental run time of under 1 min for select conditions and an average run time of less than 4 min, this approach significantly reduces experimental time while enhancing the temporal duty cycle. The outlined SLIM system connects to an ion trap mass analyzer via a PCB stacked ring ion guide, which replaces the commercial ion optics and capillary inlet. By applying a discrete and repeating injection pulse and solving a series of algebraic equations, the system reconstructs an arrival time distribution with a minimal degree of error with enhanced ion throughput. To demonstrate the feasibility of this approach, the 3.4-m SLIM system resolves gas-phase conformers for various small peptides and proteins. This system and methodology also enable direct implementation between SLIM and ion trap mass analyzers traditionally interfaced with front separation systems such as liquid chromatography.

Purpose: Digital breast tomosynthesis (DBT) has been introduced more than a decade ago. Studies have shown higher breast cancer detection rates and lower recall rates, and it has become an established imaging method in diagnostic settings. However, full-field digital mammography (FFDM) remains the most common imaging modality for screening in many countries, as it delivers high-resolution planar images of the breast. To combine the advantages of DBT with the faster acquisition and the unique in-plane resolution capabilities known from FFDM, a system concept was developed for application in screening and diagnosis., Approach: The concept comprises an X-ray tube with adaptive focal spot position based on the flying focal spot (FFS) technology and optimized X-ray spectra. This is combined with innovative algorithmic concepts for tomosynthesis reconstruction and synthetic mammograms (SMs)., Results: An X-ray tube with FFS was incorporated into a DBT system that performs 50-deg wide tomosynthesis scans with 25 projections in 4.85 s. Laboratory evaluations demonstrated significant improvements in the effective modular transfer function (eMTF). The improved eMTF as well as the effectiveness of the algorithmic concepts is shown in images from a clinical evaluation study., Conclusions: The DBT system concept enables high spatial resolution at short acquisition times. This leads to improved microcalcification visibility, reduced risk of motion artifacts, and shorter breast compression times. It shifts the in-plane resolution of DBT into the high-resolution range of FFDM. The presented technology leap might be a key contributor to facilitating the paradigm shift of replacing FFDM with DBT plus SM., (© 2024 The Authors.)

Obtaining the high-resolution distribution characteristics of urban air pollutants is crucial for effective pollution control and public health. In order to fulfill it, mobile monitoring offers a novel and practical approach compared to traditional fixed monitoring methods. However, the sparsity of mobile monitoring data still makes it a challenge to recover the high-resolution pollutant concentration across an entire area. To tackle the sparsity issue and fulfill a prediction of the spatiotemporal distribution of PM 2.5 , a high-resolution urban PM 2.5 prediction method was proposed based on mobile monitoring data in this study. This method enables prediction with a spatial resolution of 500m × 500m and a temporal resolution of 1 h. First, a Light Gradient Boosting Machine (LightGBM) was trained using mobile monitoring of PM 2.5 concentration and exogenous features to obtain complete spatiotemporal PM 2.5 concentration. Second, a model consisting of Convolutional Neural Network and Transformer (CNN-Transformer) with a customised loss function was established to predict high-resolution PM 2.5 concentration based on complete spatiotemporal data. The method was validated using real-world data collected from Cangzhou, China. The numerical results from cross-validation showed an R 2 of 0.925 for imputation and 0.887 for prediction, demonstrating this method is suitable for high-resolution spatiotemporal prediction of PM 2.5 concentration based on mobile monitoring data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)