Imaging Mapping Research Articles

Recognizing and reducing effects of moisture-salt coexistence on soil organic matter spectral prediction：From laboratory to satellite

Soil organic matter (SOM) mapping in salinized areas is crucial for scientific guidance on soil salinization. However, accurately mapping SOM is challenging due to the intricate interplay between soil moisture content (SMC) and soil salt content (SSC), which significantly influences soil spectra. Unlike prior research that has separately examined the impacts of moisture or salinity, this study delves into the combined effects of these factors on SOM spectra. The objective of this study is to develop and validate several spectral optimization algorithms at both the laboratory and satellite levels. In October 2020, a study was conducted using 291 ground-truth data to examine the impact of various moisture-salt stages (seven moisture stages, five salt stages) on hyperspectral data. Spectrum mechanism responsive for soil moisture and salinity were analyzed through spectral curves, correlation, and analysis of variance (Anova, AOV), and spectrum mechanism responsive for soil moisture and salinity model were built. Following that, the spectra were optimized using piecewise direct normalization (PDS)-AOV, non-negative matrix factorization (NMF)-AOV, and orthogonal signal correction (OSC)-AOV. The SOM prediction models were then built by integrating these optimized spectra with Stacking ensemble machine learning algorithms (RF, GBM, ANN). Eventually, the lab-optimized spectra were merged with satellite multispectral images to create new image (named REC) for SOM mapping. The results indicated varying impacts of SMC and SSC on spectra, particularly between 1400 nm to 2000 nm, revealing the influence of moisture-salt interaction; the best optimization algorithm (OSC-AOV) with Stacking mitigated the effect of moisture-salt coexistence on spectra (the R2 and RPD of the best models elevated by 0.005–0.267, 0.020–0.374 respectively, RMSE reduced by 0.137–1.817 g/kg); implementing this algorithm on REC significantly improved the accuracy of SOM mapping (R2 elevated by 0.185–0.259, RMSE reduced by 2.615–3.203 g/kg). This study extensively investigated the effects of moisture and salinity on spectra, spanning from laboratory to satellite, offering a novel approach to understanding and addressing the complexities in SOM mapping in salinized environments.

The impact of Artificial Intelligence in detecting esophageal squamous cell carcinoma: advances and challenges

This review explores the transformative role of artificial intelligence (AI), especially through deep learning (DL) and convolutional neural networks (CNNs), in the early detection of esophageal squamous cell carcinoma (ESCC). ESCC presents significant diagnostic challenges due to its aggressive nature and high mortality, often diagnosed at advanced stages. Traditional endoscopic methods, while essential, suffer from limitations like interobserver variability, especially for subtle early lesions. AI-based systems have demonstrated high sensitivity in identifying these early neoplasms, reducing diagnostic discrepancies and enhancing precision. Through a bibliographic review, the article highlights recent AI advancements, particularly in utilizing CNNs to identify early-stage ESCC. These tools have shown the potential to improve diagnostic accuracy even among less experienced endoscopists and provide critical support in clinical decision-making, including biopsy guidance and lesion mapping in chromoendoscopy and narrow-band imaging (NBI). Despite promising results, challenges persist in AI’s practical application, such as the need for extensive data for training, clinical validation, and standardization across different endoscopic equipment. The study underscores the need for large-scale multicenter studies and standardization to solidify AI’s role in ESCC diagnosis. It envisions a future where AI not only serves as a diagnostic support tool but becomes integral to endoscopic practices, requiring continued technological development, ethical considerations, and regulatory frameworks to maximize clinical benefit.

A multi modal fusion coal gangue recognition method based on IBWO-CNN-LSTM

Accurate identification of coal and gangue is a crucial guarantee for efficient and safe mining of top coal caving face. This article proposes a coal-gangue recognition method based on an improved beluga whale optimization algorithm (IBWO), convolutional neural network, and long short-term memory network (CNN-LSTM) multi-modal fusion model. First, the mutation and memory library mechanisms are introduced into the beluga whale optimization to explore the solution space fully, prevent falling into local optimum, and accelerate the convergence process. Subsequently, the image mapping of the audio signal and vibration signal is performed to extract Mel-Frequency Cepstral Coefficients (MFCC) features, generating rich sample data for CNN-LSTM. Then the multi-head attention mechanism is introduced into CNN-LSTM to speed up the training speed and improve the classification accuracy. Finally, the IBWO-CNN-LSTM coal-gangue recognition model is constructed by the optimal hyperparameter combination obtained by IBWO to realize the automatic recognition of coal-gangue. The benchmark function proves that IBWO is superior to other optimization algorithms. By building an experimental platform for the impact of coal and gangue falling on the tail beam of hydraulic support, multiple experimental data collection is carried out. The experimental results show that the proposed coal-gangue recognition model has better performance than other recognition models, and the accuracy rate reaches 95.238%. The multi-modal fusion strategy helps to improve the accuracy and robustness of coal-gangue recognition.

CLFNet: a multi-modal data fusion network for traffic sign extraction

Abstract When using image data for signage extraction, poor visibility conditions such as insufficient light, rainy days, and low light intensity are encountered, leading to low accuracy and poor boundary segmentation in vision-based detection methods. To address this problem, we propose a cross-modal latent feature fusion network for signage detection, which obtains rich boundary information by combining images with light detection and ranging depth images, thus compensating for the pseudo-boundary phenomenon that may occur when using a single RGB image segmentation. First, HRNet is utilized as the backbone network to extract the boundary information of the point cloud depth map and RGB image by introducing the boundary extraction module; Second, the sensitivity to the boundary is enhanced by applying the feature aggregation module to deeply fuse the extracted boundary information with the image features; Finally, boundary Intersection over Union (IOU) is introduced as an evaluation index. The results show that the method performs more superiorly compared to the mainstream RGBD network, with an improvement of 5.5% and 6.1% in IOU and boundary IOU, and an accuracy of 98.3% and 96.2%, respectively, relative to the baseline network.

SpaGAN: A spatially-aware generative adversarial network for building generalization in image maps

Building generalization is an essential task in generating multi-scale topographic maps. The progress of deep learning offers a new paradigm to overcome the coordination challenges faced by conventional building generalization algorithms. Some studies have confirmed the feasibility of several original semantic segmentation networks, such as U-Net and its variants and the conditional generative adversarial network (cGAN), for building generalization in image maps. However, they suffer from critical deformation effects, especially for large and geometrically complex buildings. Since learning building generalization essentially means modeling the subtle transformation of building footprints across scales, we argue that the spatial awareness of a neural network, for instance, regarding building size and shape, is crucial to effective learning. Thus, we propose a spatially-aware generative adversarial network, SpaGAN. It takes a representative cGAN, pix2pix, as the backbone, and modifies two modules: In the U-Net-based generator, an atrous spatial pyramid pooling (ASPP) module replaces the conventional convolutional module to extract multi-scale features of buildings of varying sizes and shapes; in the PatchGAN-based discriminator, a signed distance map (SDM) module is used to capture the fine-grained shape difference for discrimination. The proposed network was comprehensively evaluated with a synthetic and a real-world dataset. The results demonstrate that SpaGAN outperforms existing baseline models (U-Net, ResU-Net, pix2pix) for building generalization, particularly in the real-world dataset. The new model can achieve more reasonable aggregation, simplification, and squaring generalization operators.

XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-Ray Security Inspection Images

XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-Ray Security Inspection Images

MnOOH nanorods decorated with CeO2 nanoparticles as advanced electrode for high-performance supercapacitor

The development of novel composite electrode materials is essential to fabricating supercapacitors with high specific capacitance and good stability. In this study, MnOOH nanorods adorned with CeO2 (CeMn composites) have been satisfactorily synthesized through in-situ growth of tiny CeO2 nanoparticles using hydrothermal treatment. SEM images revealed that the granular CeO2 particles are adhered to the surfaces of nanorod-shaped MnOOH. XRD analysis confirmed the CeMn composites maintain the crystal structure of MnOOH and CeO2 with high purity. The EDS elemental mapping images demonstrated that Mn, O, and Ce elements are homogenously dispersion distributed in the CeMn composites. The supercapacitive performance of the MnOOH and CeMn composites pasted onto the Ni foam was evaluated determined through electrochemical measurements. The Ce0.05Mn1 (Ce/Mn molar ratio of 0.05/1) as a supercapacitor electrode exhibited an excellent specific capacitance of 857.62 F/g at 1 A/g, which is higher than the values for the MnOOH. Moreover, the prepared Ce0.05Mn1 still could retain good cycling stability over 3000 charge/discharge cycles. This study presents a feasible route to develop high-performing supercapacitor electrode materials.

Design of MnO2/g-C3N4 heterojunction composite photocatalysts for augmented charge separation and photocatalytic degradation performance with superior antibacterial activity

The development of advanced photocatalysts is critical for addressing environmental pollution and enhancing water purification processes. Our study has effectively developed a novel MnO2/g-C3N4 (MGC) heterojunction composite photocatalyst exhibiting superior photo-degradation under visible-light (VL) conditions and also established antibacterial activities. Comprehensive characterization was acquired using XRD, FT-IR, FE-SEM with EDX-associated mapping images, TEM, UV–Vis DRS and PL investigations, indicating the successful formation of well-dispersed MnO2 nanoparticles (NPs) over the g-C3N4 catalyst. The MGC composite heterojunction photocatalyst demonstrated increased photocatalytic degradation activity of 77.2 % in aqueous crystal violet (CV) under VL within 120 min, significantly outperforming pure g-C3N4 and MnO2 by 3.02 and 2.37 times, respectively. The MGC composite demonstrates remarkable stability and reusability, retaining 73.7 % of its efficiency after five consecutive cycles. Additionally, the as-synthesised composite endows potent antibacterial action against various pathogenic bacteria including K. pneumonia, S. aureus, E. coli and B. cereus. The active species analysis indicates that the composite photocatalyst facilitates charge transfer, while effectively preventing the recombination of photo-produced carriers via an effective Z-scheme mechanism, and the synergistic things among MnO2 and g-C3N4 are accredited to the boosted photocatalytic and antibacterial activities. This research describes a photocatalytic approach for efficiently eliminating various contaminants from water bodies, which has significant implications for the future development of photocatalytic technology for wastewater handling.

First Hit the Best Hit for Advanced Differentiated Papillary Thyroid Carcinoma?

Optimal upfront surgery with precise and objective image mapping is perhaps the way to go to enhance treatment outcomes and mitigate the use of post-operative radioiodine therapy. With the advent of targeted agents and precision medicine, it is now possible to undertake surgery for very advanced or inoperable differentiated thyroid cancers, followed by appropriate adjuvant treatment.

DeepUrbanMapper - Satellite Image to Google Maps Translation

Abstract: The research presents DeepUrbanMapper, an innovative framework developed for the translation of satellite imagery into Google Maps-style renderings utilizing advanced image-to-image translation techniques. It is based on Generative Adversarial Networks (GANs), widely known in machine learning for their ability to yield high-quality images. To train it, the process involves a carefully curated and labeled dataset that consists of paired satellite and map images, enabling DeepUrbanMapper to learn the complex mappings between these two distinct visual domains. The GAN architecture used in DeepUrbanMapper is carefully designed and optimized to preserve spatial coherence and improve visual quality of the translated image. This helps in producing output maps that are close to the ground truth in both visual and geographical aspects. The proposed method includes few novel strategies to stabilize the GAN training and to overcome the notorious mode collapse problem and ensure a consistent output. DeepUrbanMapper has been extensively evaluated quantitatively and qualitatively for its performance. The evaluation shows that the new framework significantly outperforms the existing methods in terms of visual realism of the translated image and its ability to retain detailed features of the input satellite image which opens up many possibilities urban planning, car navigation systems, GIS.

Attributes Classification for Elaborating the Information of Digital and Imaging Mapping

The rapid development of information has made it possible for everyone to obtain the latest information, complete and accurate, in real time, anytime, anywhere, all over the world. Any information is fine catching up by updated with the latest news and even detailed information on local conditions. With the same analogy, detailed information regarding land utilization, land containing, landscape provision, earth surface contour, etc., are required to inform and elaborate any appropriate decision needed. The Geographic information systems (GIS) is a recent technology commonly used by research in earth science to facilitate many layered detail information by one way to get up-to-date, detailed information. In this research, the GIS utilizes several types of imaging data such remote sensing images and digitize images. As the name suggests, this system captures detailed geographic information about a location or region. By inputting classified images of remote sensing results into a GIS database at regular intervals (adjusted as necessary, such as every year, every two years, every three years, etc.), the number of information sources that can be obtained increases. There are several reasons for that. First, remote sensing images are images that cover the entire surface of the Earth. Next, remote sensing images are images that contain information about the state of the earth's surface. Third, a variety of information can be obtained by performing appropriate image processing. Furthermore, this research could be elaborated by implementing an artificial intelligent algorithm to create a robust outcome.

Deep Learning Models Used for Crop Analysis: A Review

The development and improvement to map agricultural land cover are major challenges for researchers. For the sustainable development of agronomics spatial information about agricultural practices plays a vital role. Remote sensing satellite imagery is a valuable aid in providing and understanding this spatial distribution of agricultural practices. The aim of this paper is to provide a better understanding of capabilities of satellite images for agricultural land cover mapping through the use of deep learning techniques. The global coverage , rich spectral and spatial information and repetitive nature of remote sensing(RS) data have made them effective tools for mapping crop extents and yield prediction. This paper explores wide ranging review of research papers and articles on deep learning algorithms for image processing and predictions in the field of agriculture. The DL algorithms has attained remarkable success in different fields of RS and its use in crop monitoring. This review systematically identified 40 research papers from peer reviewed scientific publications related to sensors, platforms, input features, training data, spatial distribution of study sites. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly.

Bilateral enhancement network with signal-to-noise ratio fusion for lightweight generalizable low-light image enhancement

Low-light image enhancement aims to enhance the visibility and contrast of low-light images while eliminating complex degradation issues such as noise, artifacts, and color distortions. Most existing low-light image enhancement methods either focus on quality while neglecting computational efficiency or have limited learning and generalization capabilities. To address these issues, we propose a Bilateral Enhancement Network with signal-to-noise ratio fusion, called BiEnNet, for lightweight and generalizable low-light image enhancement. Specifically, we design a lightweight Bilateral enhancement module with SNR (Signal-to-Noise Ratio) Fusion (BSF), which serves the SNR map of the input low-light image as the interpolation weights to dynamically fuse global brightness features and local detail features extracted from a bilateral network and achieve differentiated enhancement across different regions. To improve the network’s generalization ability, we propose a Luminance Normalization (LNM) module for preprocessing and a Dual-Exposure Processing (DEP) module for post-processing. LNM divides the channels of input features into luminance-related channels and luminance independent channels, and reduces the inconsistency of the degradation distribution of input low-light images by only normalizing the luminance-related channels. DEP learns overexposure and underexposure corrections simultaneously by employing the ReLU activation function, inverting operation, and residual network, which can improve the robustness of enhancement effects under different exposure conditions while reducing network parameters. Experiments on the LOL-V1 dataset shows BiEnNet significantly increased PSNR by 8.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} and SSIM by 3.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} compared to FLW-Net, reduced parameters by 98.78\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, and improved computational speed by 52.64\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} compared to the classical KIND.

Advancing Sugarcane Farm Management Through NDVI-Based Color Mapping and Drone Imaging

Precision agriculture has emerged as a key strategy for boosting crop productivity and optimizing resource use. This study leverages advanced imaging and machine learning to enhance the management of sugarcane farms. Using drones, high-resolution RGB images of sugarcane fields are captured and transformed into multispectral images through a Generative Adversarial Network (GAN), revealing critical spectral data for plant health assessment. The Normalized Difference Vegetation Index (NDVI) is derived from these multispectral images and serves as a vital measure of vegetation health. This NDVI data, combined with farmer-reported yield information, creates a comprehensive dataset linking NDVI values to actual crop yields. To predict sugarcane yield from NDVI values, we trained a feedforward neural network on this integrated dataset. The proposed method not only enhances prediction accuracy but also provides valuable insights into the connection between NDVI metrics and crop performance. The model was validated using individual field images, enabling precise yield predictions for different field sections. This study highlights the effectiveness of integrating drone imagery, machine learning, and remote sensing in precision agriculture. The combination of NDVI data with yield information provides a robust tool for optimizing sugarcane production, improving farm management decisions, and advancing agricultural sustainability.

State-to-State Spin-Orbit Changing Collision Dynamics of Vibrationally Excited NO at Collision Energies from 1.4 eV to the Cold Regime.

State-to-state spin-orbit changing collisions of vibrationally excited nitric oxide (NO) with argon (Ar) were studied across a wide collision energy range from 3.5 to 11,200 cm-1 (0.43 meV to 1.4 eV) using two molecular beam geometries. Stimulated emission pumping (SEP) for precise initial state preparation and velocity map imaging (VMI) for detailed scattering image capture were employed. These methods enable the study of quantum-state-resolved differential cross sections (DCSs) and provide comprehensive insight into the collision dynamics over both quantum and classical regimes. Theoretical predictions using quantum mechanical close-coupling (QMCC) calculations based on high-level coupled cluster (CCSD(T)) and multireference configuration interaction (MRCI) potential energy surfaces (PESs) are compared with experimental results enabling the testing of both repulsive and attractive parts of the PESs. This study highlights the challenges in accurately modeling spin-orbit changing collisions and underscores the importance of precise experimental data for validating theoretical models, thereby advancing our understanding of nonadiabatic collision dynamics.

Impact of uterine displacement on T2-weighted image quality in the female pelvic MRI.

Despite the importance of T2-weighted image in clinical practice, artifacts can significantly degrade image quality and affect diagnosis. This study quantitatively analyzed uterine displacement and surveyed the relationship between the image quality of fast-spin-echo-T2-weighted image of the female pelvis and quantitative value of uterine displacement. Overall, 147 women (mean age, 46.0 ± 12.8years; age range, 22-84years) who had undergone pelvic magnetic resonance imaging examination using a 3T- magnetic resonance imaging scanner were included. Two radiologists performed a visual assessment of the fast-spin-echo-T2-weighted image in the sagittal plane in terms of ghosts and motion blur, and classified the image quality into the following three groups: poor, moderate, and excellent. Uterine displacement on half-Fourier acquisition single-shot turbo spin-echo-cine images was calculated, and the maximum amplitude of uterine displacement and summation of uterine displacement were calculated from the displacement map images. The Kruskal-Wallis and Steel-Dwass tests were performed to compare the maximum amplitude of uterine displacement and summation of uterine displacement among the three groups. Poor, moderate, and excellent image qualities were observed in 48, 71, and 28 patients, respectively. The quality of fast-spin-echo-T2-weighted images degraded statistically significantly with P < 0.01 as the maximum amplitude of uterine displacement increased. The summation of uterine displacement in the poor and moderate groups had greater statistical significance with P < 0.01 than that in the excellent group.

Coexistent pleural effusion is found to be associated with aggravated subclinical myocardial injury in systemic lupus erythematous using cardiovascular magnetic resonance imaging.

Pleural effusion (PE) is a common pulmonary manifestation in patients with systemic lupus erythematosus (SLE), and is associated with disease activity. However, little is known regarding the additive effects of PE on cardiac function. Therefore, this study aimed to investigate multi-parameter cardiovascular magnetic resonance imaging (CMR) findings in SLE patients with PE and to explore whether cardiac involvement is associated with PE. Patients with SLE and age-matched/sex-matched healthy controls were included in this study. Patients with SLE were diagnosed according to the 2019 European League Against Rheumatism/American College of Rheumatology classification criteria. Moreover, the PE diagnosis was based on computed tomography, and the height of the effusion was > 5mm. All enrolled individuals underwent CMR imaging, including cine and late gadolinium enhancement (LGE), T1, and T2 mapping imaging. The left and right ventricular function, LGE, T1, extracellular volume (ECV), and T2 values were evaluated. A total of 111 patients with SLE were enrolled, of whom 26 (23.42%) had PE. White cell count, hemoglobin, CRP, ESR, and lactate dehydrogenase levels were higher in SLE patients with PE than in SLE patients without PE (P<0.05). LGE was more prevalent in SLE patients with PE compared with those without PE (P<0.001). In addition, Native T1 (1348 ± 65 ms vs. 1284 ± 67 ms vs. 1261 ± 41 ms; P<0.001), ECV (31.92 ± 4.16% vs. 28.61 ± 3.60% vs. 26.54 ± 2.94%; P<0.001), and T2 (44.76 ± 3.68 ms vs. 41.96 ± 3.62 ms vs. 39.21 ± 2.85 ms; P<0.001) values were high in SLE patients with PE, intermediate in SLE patients without PE, and the lowest in the control group. Linear regression analysis demonstrated that PE was independently associated with LGE (β=0.329; P<0.05), T1 (β=0.346; P<0.05), ECV (β=0.353; P<0.05), and T2 (β=0.201; P<0.05). SLE patients with PE have a higher prevalence of LGE and more diffuse myocardial fibrosis and edema than SLE patients without PE. Moreover, PE is associated with increased diffuse interstitial fibrosis and edema.

Development of a nomogram based on whole-tumor multiparametric MRI histogram analysis to predict deep myometrial invasion in stage I endometrioid endometrial carcinoma preoperatively.

The depth of myometrial invasion determines whether International Federation of Gynecology and Obstetrics stage I endometrioid endometrial carcinoma (EEC) patients undergo lymph node dissection. However, subjective evaluation results relying on magnetic resonance imaging (MRI) are not always satisfactory. To develop a nomogram based on whole-volume tumor MRI histogram parameters to preoperatively predict deep myometrial invasion (DMI) in patients with stage I EEC. This retrospective analysis included 131 EEC patients and a training/validation cohort of 92/39 patients at a 7:3 ratio. The histogram parameters were obtained from multiple sequences (ADC mapping and T2-weighted imaging) within volumes of interest. Univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression were used for feature selection. The performance of clinical model, histogram model, and histogram nomogram was evaluated by calculating the area under the receiver operating characteristic curve (AUC). Age and two morphological features (maximum anteroposterior tumor diameter on sagittal T2-weighted images [APsag] and the tumor area ratio [TAR]) were selected to construct the clinical model. Five histogram parameters were selected for the creation of the histogram model. The nomogram, which combines the histogram parameters, age, APsag, and TAR, achieved the highest AUCs in both the training and validation cohorts (nomogram vs. histogram vs. clinical model: 0.973 vs. 0.871 vs. 0.934 [training] and 0.972 vs. 0.870 vs. 0.928 [validation]). The MR histogram nomogram can help predict the DMI of patients with stage I EEC preoperatively, assisting physicians in the development of personalized treatment strategies.

Sustainable dual-cathode photoelectro-Fenton-like system at a wide pH range for rapid degradation of emerging pollutants

In-situ Fenton like process was highly promising and energy-efficient for treating emerging organic contaminants. However, the challenge in the production and activation of H2O2 faces its practical application. Herein, a photoelectro-Fenton-like (PEF) system was successfully fabricated with BiVO4 thin-film, graphite (C) and PhC2Cu as photoanode, cathode and photocathode, respectively. The ternary system was applied for visible-light degradation of Tetracycline (TC) at a low potential of 0.5 V. H2O2 was generated at the graphite cathode and then activated to produce •OH by Cu(I) from PhC2Cu photocathode. The photo-generated electrons accelerated the Cu(I)/Cu(II) cycle to achieve sustainable PEF reactions. After 150 min of illumination, 97 % TC was degraded in BiVO4|C|PCu system, superior to BiVO4|C and BiVO4|PCu systems. The outstanding stability and reusability of this PEF system was proved by 10 cycles. Moreover, the vulnerable atomic sites of TC molecule were predicated by the Fukui function. Both inhibition zone tests of bacteria and wheat seeds cultivation experiments proved that the constructed PEF system displayed outstanding performance in hampering TC eco-toxicity. For example, the Fv/Fm fluorescence image map demonstrated that the constructed PEF system not only degraded organic pollutants but also lowered their toxicity. Finally, the mechanism of in-situ generation and activation of H2O2 was proposed based on the results of quenching experiments, PL spectra and ESR technique. Overall, this study provides a promising pathway to remediate environmental pollution through constructing a PEF system based on dual cathodes.

Imaging geometric simulation and vibration influence analysis of rotary scanning remote sensing satellites

In response to the lack of in-orbit image and vibration impact measurement methods for new rotary scanning satellites, this paper proposed a satellite imaging geometric simulation method based on 3D rendering engine. Simulated images that reflect strict object-image mapping and real-time dynamic image motion compensation process of TDI cameras are generated. On this basis, the consistency of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) in measuring vibration influence is analyzed, and a theoretical approximate solution for the sensitive frequency of sinusoidal vibration is obtained, which is inversely proportional to the number of rows of the image to be evaluated and the camera integration time. Finally, the correctness of the image motion compensation model is demonstrated through simulations under different working conditions, and the relative deviation between the experimental values of sensitive frequencies and theoretical results is within −15% to 20%.