Pixel Resolution Research Articles

Characterization of Rigid and Flexible Digital Detector Arrays Using ASTM E2597/E2597M-22

ASTM E2597/E2597M-22 is a standard that summarizes a standard practice for characterization of digital detector arrays (DDAs) by manufacturers. The standard consists of a series of tests intended to deliver quantitative results for a given model of detector’s characteristics as they should perform upon sale. It was written for use in characterizing rigid detectors, but the tests are applicable to bendable DDAs, utilized for single viewing. Bendable DDAs, unlike their rigid counterparts, are made to conform around parts being inspected, such as pipes, in a similar application to film and computed radiography for imaging curved objects. As bendable DDAs are made using the same scintillators as flat panel DDAs and have similar pixel pitch and resolution capabilities, it is expected that the results from most characterization tests should be similar. Conformable DDAs produce similar imaging results as rigid DDAs but have the flexibility of use for curved surfaces. They offer a valuable tool for customers currently using film and CR who are looking to expedite their inspections or move away from use of consumable products. We performed these characterization procedures on conformable DDA models and rigid DDA models utilizing proprietary INDUSTREX software developed by Carestream Health, Inc. The experimental results are presented as part of this paper.

7‐3: Analysis of a 1.2‐in. 4k2k LCOS Display Phase Modulator for Holographic Display Applications

This developed 1.2‐inch 4K2K LCOS with 76.0 lp/mm spatial resolution can still achieve high phase accuracy and obviously enlarge field‐of‐viewing (FOV) angle compared with previous 0.55‐inch 2K1K LCOS. The holographic image quality within phase‐only LCOS not only can be improved by using higher phase accuracy and compensation technologies, but also can realize the real 3D imaging into near‐eye displays to avoid the VAC phenomena. In addition to, the holographic 4K2K LCOS (26.6mm x 15.8mm) optical design can achieve lighter optical engine and reconstructed square (26.6mm x 26.6mm) image per eye, so that it has advantages of more feasible driving condition and cost competition. As high‐speed technologies of display electronics, liquid crystal, and optical algorithm were gradually mature, the above 1.2" phase‐only LCOS with both high pixel resolution and smaller pixel pitch will be another choice into the VR & MR headsets utilized video see‐through (VST) method. Meanwhile, the 1.2” 4K2K LCOS with enhanced FOV can be alternative scheme for AR glasses using optical see‐through (OST) method before the trade‐off between volume, brightness, and FOV angle are resolved.

TQU-HG dataset and comparative study for hand gesture recognition of RGB-based images using deep learning

Hand gesture recognition has great applications in human-computer interaction (HCI), human-robot interaction (HRI), and supporting the deaf and mute. To build a hand gesture recognition model using deep learning (DL) with high results then needs to be trained on many data and in many different conditions and contexts. In this paper, we publish the TQU-HG dataset of large RGB images with low resolution (640×480) pixels, low light conditions, and fast speed (16 fps). TQU-HG dataset includes 60,000 images collected from 20 people (10 male, 10 female) with 15 gestures of both left and right hands. A comparative study with two branches: i) based on Mediapipe TML and ii) Based on convolutional neural networks (CNNs) (you only look once (YOLO); YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLO-Nas, single shot multiBox detector (SSD) VGG16, residual network (ResNet)18, ResNext50, ResNet152, ResNext50, MobileNet V3 small, and MobileNet V3 large), the architecture and operation of CNNs models are also introduced in detail. We especially fine-tune the model and evaluate it on TQU-HG and HaGRID datasets. The quantitative results of the training and testing are presented (F1-score of YOLOv8, YOLO-Nas, MobileNet V3 small, ResNet50 is 98.99%, 98.98%, 99.27%, 99.36%, respectively on the TQU-HG dataset and is 99.21%, 99.37%, 99.36%, 86.4%, 98.3%, respectively on the HaGRID dataset). The computation time of YOLOv8 is 6.19 fps on the CPU and 18.28 fps on the GPU.

64‐5: Virtual ESD Failure Detection Methodology for Oxide TFT Based OLED Panels

As the screen size and pixel resolution increase, electrostatic discharging (ESD) problems have become the most detrimental issue for the next‐level OLED devices such as IT‐OLED, and QD‐OLED. Electrostatic discharging (ESD) is a well‐known phenomenon in the semiconductor and display industry. For its catastrophic failure, lots of effort has been made over the last three decades to predict ESD failures systematically in display panels, but proper methodologies for ESD hotspot prediction have not developed due to its complicated physical behaviors. Especially for the oxide TFT‐based OLED devices, the Bottom Metal Layer (BML) is introduced to shield the electric field from the external light‐induced charging and enhance the output characteristics of oxide TFT. However, this large area of the BML layer can cause serious electrostatic charging (ESD) issues during the fabrication process. In this work, we analyzed five different types of temporal charging injection mechanisms and three types of failure phenomena. To simulate the multi‐scale time‐dependent full panel ESD models, we developed simplified OLED panel design verification models by using advanced numerical techniques such as perfect boundary approximation and thin sheet metal approximation. Based on the newly developed simulation methodology, robust OLED panel designs are suggested and experimentally verified..

Microsphere-assisted hyperspectral imaging: super-resolution, non-destructive metrology for semiconductor devices

As semiconductor devices shrink and their manufacturing processes advance, accurately measuring in-cell critical dimensions (CD) becomes increasingly crucial. Traditional test element group (TEG) measurements are becoming inadequate for representing the fine, repetitive patterns in cell blocks. Conventional non-destructive metrology technologies like optical critical dimension (OCD) are limited due to their large spot diameter of approximately 25 μm, which impedes their efficacy for detailed in-cell structural analysis. Consequently, there is a pressing need for small-spot and non-destructive metrology methods. To address this limitation, we demonstrate a microsphere-assisted hyperspectral imaging (MAHSI) system, specifically designed for small spot optical metrology with super-resolution. Utilizing microsphere-assisted super-resolution imaging, this system achieves an optical resolution of 66 nm within a field of view of 5.6 μm × 5.6 μm. This approach effectively breaks the diffraction limit, significantly enhancing the magnification of the system. The MAHSI system incorporating hyperspectral imaging with a wavelength range of 400–790 nm, enables the capture of the reflection spectrum at each camera pixel. The achieved pixel resolution, which is equivalent to the measuring spot size, is 14.4 nm/pixel and the magnification is 450X. The MAHSI system enables measurement of local uniformity in critical areas like corners and edges of DRAM cell blocks, areas previously challenging to inspect with conventional OCD methods. To our knowledge, this approach represents the first global implementation of microsphere-assisted hyperspectral imaging to address the metrology challenges in complex 3D structures of semiconductor devices.

Quantum dot-enabled infrared hyperspectral imaging with single-pixel detection.

Near-infrared (NIR) hyperspectral imaging is a powerful technique that enables the capture of three-dimensional (3D) spectra-spatial information within the NIR spectral range, offering a wide array of applications. However, the high cost associated with InGaAs focal plane array (FPA) has impeded the widespread adoption of NIR hyperspectral imaging. Addressing this challenge, in this study, we adopt an alternative approach-single-pixel detection for NIR hyperspectral imaging. Our investigation reveals that single-pixel detection outperforms conventional FPA, delivering a superior signal-to-noise ratio (SNR) for both spectral and imaging reconstruction. To implement this strategy, we leverage self-assembled colloidal quantum dots (CQDs) and a digital micromirror device (DMD) for NIR spectral and spatial information multiplexing, complemented by single-pixel detection for simultaneous spectral and image reconstruction. Our experimental results demonstrate successful NIR hyperspectral imaging with a detection window about 600 nm and an average spectral resolution of 8.6 nm with a pixel resolution of 128 × 128. The resulting spectral and spatial data align well with reference instruments, which validates the effectiveness of our approach. By circumventing the need for expensive and bulky FPA and wavelength selection components, our solution shows promise in advancing affordable and accessible NIR hyperspectral imaging technologies, thereby expanding the range of potential applications.

Modeling and Locating the Wind Erosion at the Dry Bottom of the Aral Sea Based on an InSAR Temporal Decorrelation Decomposition Model

The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators reported in this current study can accurately describe and measure wind erosion intensity. A novel wind erosion intensity (WEI) of a pixel resolution unit was defined in this paper based on deformation due to the wind erosion in this pixel resolution unit. We also derived the relationship between WEI and soil InSAR temporal decorrelation (ITD). ITD is usually caused by the surface change over time, which is very suitable for describing wind erosion. However, within a pixel resolution unit, the ITD signal usually includes soil and vegetation contributions, and extant studies concerning this issue are considerably limited. Therefore, we proposed an ITD decomposition model (ITDDM) to decompose the ITD signal of a pixel resolution unit. The least-square method (LSM) based on singular value decomposition (SVD) is used to estimate the ITD of soil (SITD) within a pixel resolution unit. We verified the results qualitatively by the landscape photos, which can reflect the actual conditions of the soil. At last, the WEI of the Aral Sea from 23 June 2020, to 5 July 2020 was mapped. The results confirmed that (1) based on the ITDDM model, the SITD can be accurately estimated by the LSM; (2) the Aral Sea is experiencing severe wind erosion; and (3) the middle, northeast, and southeast bare areas of the South Aral Sea are where salt dust storms may occur.

Hadamard ghost imaging with a small amount of mask plates based on a spread spectrum.

Ghost imaging techniques using low-cost bucket detectors have unrivaled advantages for some wavebands where plane array detectors are not available or where focusing is difficult. In these bands, fine mask plates are the key to implementing high-resolution and quality ghost imaging. However, manufacturing a large number of mask plates is necessary but undoubtedly expensive in traditional Hadamard ghost imaging (HGI). Inspired by the spread spectrum technology, Hadamard ghost imaging based on spread spectrum (HGI-SS) is proposed, in which only two sets of a small number of mask plates are needed to accomplish Nyquist sampling for the object. Their numbers are equal to the lateral pixel resolution and the vertical pixel resolution of the object, respectively. Optical experiments verify the effectiveness of the scheme. For ghost imaging with a resolution requirement of 128 × 128 pixels, HGI-SS needs to prepare only 256 mask plates, while the traditional HGI needs to prepare 16,384 mask plates. HGI-SS may be helpful to expand the pixel resolution of imaging at a relatively low cost of mask plates.

Vulnerability Assessment of Groundwater Influenced Ecosystems in the Northeastern United States

Groundwater-influenced ecosystems (GIEs) are increasingly vulnerable due to groundwater extraction, land-use practices, and climate change. These ecosystems receive groundwater inflow as a portion of their baseflow or water budget, which can maintain water levels, water temperature, and chemistry necessary to sustain the biodiversity that they support. In some systems (e.g., springs, seeps, fens), this connection with groundwater is central to the system’s integrity and persistence. Groundwater management decisions for human use often do not consider the ecological effects of those actions on GIEs. This disparity can be attributed, in part, to a lack of information regarding the physical relationships these systems have with the surrounding landscape and climate, which may influence the environmental conditions and associated biodiversity. We estimate the vulnerability of areas predicted to be highly suitable for the presence of GIEs based on watershed (U.S. Geological Survey Hydrologic Unit Code 12 watersheds: 24–100 km2) and pixel (30 m × 30 m pixels) resolution in the Atlantic Highlands and Mixed Wood Plains EPA Level II Ecoregions in the northeastern United States. We represent vulnerability with variables describing adaptive capacity (topographic wetness index, hydric soil, physiographic diversity), exposure (climatic niche), and sensitivity (aquatic barriers, proportion urbanized or agriculture). Vulnerability scores indicate that ~26% of GIEs were within 30 m of areas with moderate vulnerability. Within these GIEs, climate exposure is an important contributor to vulnerability of 40% of the areas, followed by land use (19%, agriculture or urbanized). There are few areas predicted to be suitable for GIEs that are also predicted to be highly vulnerable, and of those, climate exposure is the most important contributor to their vulnerability. Persistence of GIEs in the northeastern United States may be challenged as changes in the amount and timing of precipitation and increasing air temperatures attributed to climate change affect the groundwater that sustains these systems.

Method for identifying the position of crack tip by displacement distribution based on digital image correlation

Determining the position of crack tip is crucial for rock fracture characteristic analysis. This study aims to establish a quantitative standard for determining crack tip positions in crystalline rocks based on digital image correlation (DIC). Semi-circular bending (SCB) tests were conducted on four types of rocks under three-point bending. Optical microscopy and DIC were used to investigate characteristics of the horizontal displacement fields around the crack tips. A significant disparity in the displacement gradient was observed across the crack tips, with the internal gradient being approximately 3–5.1 times higher than the external gradient. A critical value ranging from 0.41%–0.64% was determined for the high internal displacement gradient (∂u/∂x) at a 20 pixel resolution equivalent to 1 mm. Furthermore, a novel DIC-based method was proposed that used three patterns of opening displacement gradients to classify SCB specimens into non-cohesive, cohesive, and elastic zones, providing a quantitative determination of Type I crack tip positions. This study offers valuable insights and a crack tip determination method based on DIC gradients, applicable for assessing parameters such as the crack extension length and fracture process zone length.

FPGA-based stereo matching for crop height measurement using monocular camera

We have proposed a hardware-accelerated drone to analyze the condition of farmland right then and there; as a first step, we report that the proposed system can take crop height measurements with high accuracy using a monocular camera. The proposed three-dimensional farmland is generated using stereo matching, where a drone with a monocular camera can extend the parallax distance as the length between two positions when taking a ground image. This means that our approach can improve the accuracy of a reconstructed 3D farmland. In addition, toward real-time computation and low power consumption, the proposed hardware design accelerates image processing efficiently. Thus, to achieve this, we propose a strategy that combines the semi-global matching (SGM) with single path direction and a sum of absolute difference (SAD) with reduced disparity searching length. For example, a semi-global matching (SGM) was employed to smooth the disparity map result before checking the consistency, where the scan line was performed in one direction, from left to right, to speed up the computation time. The experimental result shows that the computation time performed by Xilinx Zynq ZCU102 FPGA achieves 0.77 s for the stereo data set images with 1536 × 1024 pixels resolution. To meet the real-time application and reduce the FPGA resources toward lower power consumption, the experiment discusses reducing the disparity searching length for the SAD computation. In our experiment, the execution time is less than 40 milliseconds, and the circuit volume is around 9,500 LUTs, equivalent to a small-size FPGA. Finally, we also estimated the object's height; a value of 0.43 m was estimated for the object with a physical height of 0.45 m. Meanwhile, for the object with a physical height of 0.65 m, a value of 0.63 m was estimated.

Three-Dimensional Ultrastructure of Arabidopsis Cotyledons Infected with Colletotrichum higginsianum.

We used serial block-face scanning electron microscopy (SBF-SEM) to study the host-pathogen interface between Arabidopsis cotyledons and the hemibiotrophic fungus Colletotrichum higginsianum. By combining high-pressure freezing and freeze-substitution with SBF-SEM, followed by segmentation and reconstruction of the imaging volume using the freely accessible software IMOD, we created 3D models of the series of cytological events that occur during the Colletotrichum-Arabidopsis susceptible interaction. We found that the host cell membranes underwent massive expansion to accommodate the rapidly growing intracellular hypha. As the fungal infection proceeded from the biotrophic to the necrotrophic stage, the host cell membranes went through increasing levels of disintegration culminating in host cell death. Intriguingly, we documented autophagosomes in proximity to biotrophic hyphae using transmission electron microscopy (TEM) and a concurrent increase in autophagic flux between early to mid/late biotrophic phase of the infection process. Occasionally, we observed osmiophilic bodies in the vicinity of biotrophic hyphae using TEM only and near necrotrophic hyphae under both TEM and SBF-SEM. Overall, we established a method for obtaining serial SBF-SEM images, each with a lateral (x-y) pixel resolution of 10 nm and an axial (z) resolution of 40 nm, that can be reconstructed into interactive 3D models using the IMOD. Application of this method to the Colletotrichum-Arabidopsis pathosystem allowed us to more fully understand the spatial arrangement and morphological architecture of the fungal hyphae after they penetrate epidermal cells of Arabidopsis cotyledons and the cytological changes the host cell undergoes as the infection progresses toward necrotrophy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Easily deployable real-time detection method for small traffic signs

Traffic sign detection technology plays an important role in driver assistance systems and automated driving systems. This paper proposes DeployEase-YOLO, a real-time high-precision detection scheme based on an adaptive scaling channel pruning strategy, to facilitate the deployment of detectors on edge devices. More specifically, based on the characteristics of small traffic signs and complex background, this paper first of all adds a small target detection layer to the basic architecture of YOLOv5 in order to improve the detection accuracy of small traffic signs.Then, when capturing specific scenes with large fields of view, higher resolution and richer pixel information are preserved instead of directly scaling the image size. Finally, the network structure is pruned and compressed using an adaptive scaling channel pruning strategy, and the pruned network is subjected to a secondary sparse pruning operation. The number of parameters and computations is greatly reduced without increasing the depth of the network structure or the influence of the input image size, thus compressing the model to the minimum within the compressible range. Experimental results show that the model trained by Experimental results show that the model trained by DeployEase-YOLO achieves higher accuracy and a smaller size on TT100k, a challenging traffic sign detection dataset. Compared to existing methods, DeployEase-YOLO achieves an average accuracy of 93.3%, representing a 1.3% improvement over the state-of-the-art YOLOv7 network, while reducing the number of parameters and computations to 41.69% and 59.98% of the original, respectively, with a compressed volume of 53.22% of the previous one. This proves that the DeployEase-YOLO has a great deal of potential for use in the area of small traffic sign detection. The algorithm outperforms existing methods in terms of accuracy and speed, and has the advantage of a compressed network structure that facilitates deployment of the model on resource-limited devices.

Recovering the gas properties of protoplanetary disks through parametric visibility modeling: MHO 6

The composition and distribution of the gas in a protoplanetary disk plays a key role in shaping the outcome of the planet formation process. Observationally, the recovery of information such as the emission height and brightness temperature from interferometric data is often limited by the imaging processes. To overcome the limitations of image-reconstruction when analyzing gas emission from interferometric observations, we have introduced a parametric model to fit the main observable properties of the gaseous disk component in the visibility plane. This approach is also known as parametric visibility modeling. We applied our parametric visibility modeling to the gas brightness distribution of the molecular line emission from 12CO J=3-2 and 13CO J=3-2 in the disk around MHO\,6, a very-low-mass star in the Taurus star-forming Region. To improve the flux fidelity of our parametric models, we combined models with different pixel resolution before the computation of their visibilities, referred to as ``nesting images.'' When we apply our parametric visibility modeling to MHO\,6, with independent fits to the emission from its CO isopotologues, the models return the same consistent results for the stellar mass, disk geometry, and central velocity. The surface height and brightness temperature distribution are also recovered. When compared to other disks, MHO\,6 surface height is among the most elevated surfaces, consistent with the predictions for disks around very-low-mass stars. This work demonstrates the feasibility of running rapidly iterable parametric visibility models in moderate resolution and sensitivity interferometric observations. More importantly, this methodology opens the analysis of disk's gas morphology to observations where image-based techniques are unable to robustly operate, as in the case of the compact disk around MHO\,6.

Fast high quality computational ghost imaging based on saliency variable sampling detection

Fast computational ghost imaging with high quality and ultra-high-definition resolution reconstructed images has important application potential in target tracking, biological imaging and other fields. However, as far as we know, the resolution (pixels) of the reconstructed image is related to the number of measurements. And the limited resolution of reconstructed images at low measurement times hinders the application of computational ghost imaging. Therefore, in this work, a new computational ghost imaging method based on saliency variable sampling detection is proposed to achieve high-quality imaging at low measurement times. This method physically variable samples the salient features and realizes compressed detection of computational ghost imaging based on the salient periodic features of the bucket detection signal. Numerical simulation and experimental results show that the reconstructed image quality of our method is similar to the compressed sensing method at low measurement times. Even at 500 (sampling rate 0.76%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.76\\%$$\\end{document}) measurement times, the reconstructed image of the method still has the target features. Moreover, the 2160×4096\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2160\ imes 4096$$\\end{document} (4K) pixels ultra-high-definition resolution reconstructed images can be obtained at only a sampling rate of 0.11%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.11\\%$$\\end{document}. This method has great potential value in real-time detection and tracking, biological imaging and other fields.

Dynamic land cover evapotranspiration model algorithm: DyLEMa

This study presents the “Dynamic Land Cover Evapotranspiration Model Algorithm: DyLEMa” for continuous spatiotemporal evapotranspiration (ET) estimates across diverse land uses. DyLEMa employs a coupled Random Forest model with a novel dynamic recalibrating strategy to improve pre-optimized seasonal hyperparameters following satellite acquisition alongside land cover classes. An analysis of feature importance indicated the significant variability in ET processes across different land cover classes and seasons. Hence, DyLEMa was applied to 20 years of daily 30x30 m pixel resolution Landsat-derived ET data in Illinois to address spatial and temporal discontinuities due to cloud contamination and sensor failures. DyLEMa performance was evaluated on Eddy Covariance measurements to find out that DyLEMa predictions reduced the average PBIAS error from + 31 % to −7% compared to existing US Geological Survey ET products. Spatially, DyLEMa underscores the value of a land cover-aware approach in ET estimation under varied cloud cover rates and their ability to preserve landscape features. However, the performance of DyLEMa was affected by the quality of land cover classification, suggesting the need for a refined region-specific land cover classification. DyLEMa’s flexibility and performance suggest its applicability to other regions and satellite datasets, offering a promising reduction in uncertainty of ET estimates with impacts on environmental and water resources assessments on regional scales.

Single-shot synthetic wavelength imaging: Sub-mm precision ToF sensing with conventional CMOS sensors

We present a novel single-shot interferometric ToF camera targeted for precise 3D measurements of dynamic objects. The camera concept is based on Synthetic Wavelength Interferometry, a technique that allows retrieval of depth maps of objects with optically rough surfaces at submillimeter depth precision. In contrast to conventional ToF cameras, our device uses only off-the-shelf CCD/CMOS detectors and works at their native chip resolution (as of today, theoretically up to 20 Mp and beyond). Moreover, we can obtain a full 3D model of the object in single-shot, meaning that no temporal sequence of exposures or temporal illumination modulation (such as amplitude or frequency modulation) is necessary, which makes our camera robust against object motion.In this paper, we introduce the novel camera concept and show the first measurements that demonstrate the capabilities of our system. We present 3D measurements of small (cm-sized) objects with > 2 Mp point cloud resolution (the native pixel resolution of our used detector) and up to sub-mm depth precision. We also report a “single-shot 3D video” acquisition and a first single-shot “Non-Line-of-Sight” measurement. Our technique has great potential for high-precision applications with dynamic object movement, e.g., in AR/VR, industrial inspection, medical imaging, and imaging through scattering media like fog or human tissue.

Heterogeneity and influencing factors of algal blooms in the reservoir-impacted tributary: Evidence from remote sensing and physical-based model

The construction of reservoirs often caused algae blooms but there is controversy over pattern and attribution of blooms in reservoir-impacted tributaries due to lacking of detailed records. Herein, the remote sensing data with resolution of 10-meter pixels and physics-based model were combined to explore spatiotemporal distribution of algae blooms in key tributaries of Three Gorges Reservoir, China. Self-organizing maps were used for identifying the drivers of blooms. The results revealed that algae blooms were more serious during dry years and there was little difference between dry or wet years for multi-stage gate river with coverage remaining at 42.90%. The open waters with slow currents were identified as high-risk zones. Hotspots of algae blooms changed during the year, which were close to estuary in spring, upstream in autumn, and dispersed in backwater region in summer. Water level was major measure for managing algae blooms, while meteorological conditions were identified as the key factor (40.25%) for river with multi-stage dams and characteristics of shallow lakes. For the nutrients-rich river, blooms were driven by both water level (13.98%) and climate factors (23.34%). The blooms of natural backwater river were stimulated by combination of water level, climate and waterhed nutrients. These results highlighted necessity of region water quality management in reservoir-impacted regions.

High-resolution short-term prediction of the COVID-19 epidemic based on spatial-temporal model modified by historical meteorological data

In the global challenge of Coronavirus disease 2019 (COVID-19) pandemic, accurate prediction of daily new cases is crucial for epidemic prevention and socioeconomic planning. In contrast to traditional local, one-dimensional time-series data-based infection models, the study introduces an innovative approach by formulating the short-term prediction problem of new cases in a region as multidimensional, gridded time series for both input and prediction targets. A spatial-temporal depth prediction model for COVID-19 (ConvLSTM) is presented, and further ConvLSTM by integrating historical meteorological factors (Meteor-ConvLSTM) is refined, considering the influence of meteorological factors on the propagation of COVID-19. The correlation between 10 meteorological factors and the dynamic progression of COVID-19 was evaluated, employing spatial analysis techniques (spatial autocorrelation analysis, trend surface analysis, etc.) to describe the spatial and temporal characteristics of the epidemic. Leveraging the original ConvLSTM, an artificial neural network layer is introduced to learn how meteorological factors impact the infection spread, providing a 5-day forecast at a 0.01° × 0.01° pixel resolution. Simulation results using real dataset from the 3.15 outbreak in Shanghai demonstrate the efficacy of Meteor-ConvLSTM, with reduced RMSE of 0.110 and increased R2 of 0.125 (original ConvLSTM: RMSE = 0.702, R2 = 0.567; Meteor-ConvLSTM: RMSE = 0.592, R2 = 0.692), showcasing its utility for investigating the epidemiological characteristics, transmission dynamics, and epidemic development.

Preliminary Discussion on the Current Sheet

The current sheet is a characteristic structure of magnetic energy dissipation during the magnetic reconnection process. So far, the width and depth of the current sheet are still indefinite. Here we investigate 64 current sheets observed by four telescopes from 1999 to 2022, and all of them have been well identified in the previous literature. In each current sheet, three width values are obtained at the quartering points. Based on these investigated cases, we obtain 192 values, which are in a wide range from hundreds to tens of thousands of kilometers. By calculating the pixel width (PW: the ratio of the current sheet width to the pixel resolution of corresponding observed data) of these current sheets, we find that more than 80% of the PW values concentrate on 2–4 pixels, indicating that the widths of the current sheets are dependent strongly on the instrument resolutions and all the sheets have no observable three-dimensional information. To interpret this result, we suggest that there are two probabilities. One is that the width of the current sheet is smaller than the instrument resolution, and the other is that the detected current sheet is only a small segment of the real one. Furthermore, there is another possible scenario. The so-called current sheet is just an emission-enhanced region.