Metal-Free C60-Doped Mesoporous Carbon Nitride Drives Red-Light Photocatalysis.

The pursuit of novel strategies for synthesizing high-performance nanostructures of graphitic carbon nitride (g-C3N4) has garnered increasing scholarly attention in the field of photocatalysis. Herein, we have successfully designed a metal-free photocatalyst by integrating mesoporous carbon nitride (mpg-C3N4) and C60 through a straightforward and innovative method, marking the first instance of such an achievement. Under red light, the C60/mpg-C3N4 composite exhibited a significantly accelerated rhodamine B (RhB) photodecomposition rate, surpassing bulk g-C3N4 by more than 25.8 times and outperforming pure mpg-C3N4 by 7.8 times. The synergistic effect of C60 and the mesoporous structure significantly enhanced the photocatalytic performance of g-C3N4 by adjusting its electronic structure, broadening the light absorption range, increasing the active sites, and reducing the recombination of photogenerated carriers. This work presents a promising avenue for harnessing a metal-free, stable, efficient photocatalyst driven by red light, with potential for enhancing solar energy utilization in environmental remediation.

GBDT Method Integrating Feature-Enhancement and Active-Learning Strategies-Sea Ice Thickness Inversion in Beaufort Sea.

Sea ice, as an important component of the Earth's ecosystem, has a profound impact on global climate and human activities due to its thickness. Therefore, the inversion of sea ice thickness has important research significance. Due to environmental and equipment-related limitations, the number of samples available for remote sensing inversion is currently insufficient. At high spatial resolutions, remote sensing data contain limited information and noise interference, which seriously affect the accuracy of sea ice thickness inversion. In response to the above issues, we conducted experiments using ice draft data from the Beaufort Sea and designed an improved GBDT method that integrates feature-enhancement and active-learning strategies (IFEAL-GBDT). In this method, the incident angle and time series are used to perform spatiotemporal correction of the data, reducing both temporal and spatial impacts. Meanwhile, based on the original polarization information, effective multi-attribute features are generated to expand the information content and improve the separability of sea ice with different thicknesses. Taking into account the growth cycle and age of sea ice, attributes were added for month and seawater temperature. In addition, we studied an active learning strategy based on the maximum standard deviation to select more informative and representative samples and improve the model's generalization ability. The improved GBDT model was used for training and prediction, offering advantages in dealing with nonlinear, high-dimensional data, and data noise problems, further expanding the effectiveness of feature-enhancement and active-learning strategies. Compared with other methods, the method proposed in this paper achieves the best inversion accuracy, with an average absolute error of 8 cm and a root mean square error of 13.7 cm for IFEAL-GBDT and a correlation coefficient of 0.912. This research proves the effectiveness of our method, which is suitable for the high-precision inversion of sea ice thickness determined using Sentinel-1 data.

Underwater Fish Segmentation Algorithm Based on Improved PSPNet Network.

With the sustainable development of intelligent fisheries, accurate underwater fish segmentation is a key step toward intelligently obtaining fish morphology data. However, the blurred, distorted and low-contrast features of fish images in underwater scenes affect the improvement in fish segmentation accuracy. To solve these problems, this paper proposes a method of underwater fish segmentation based on an improved PSPNet network (IST-PSPNet). First, in the feature extraction stage, to fully perceive features and context information of different scales, we propose an iterative attention feature fusion mechanism, which realizes the depth mining of fish features of different scales and the full perception of context information. Then, a SoftPool pooling method based on fast index weighted activation is used to reduce the numbers of parameters and computations while retaining more feature information, which improves segmentation accuracy and efficiency. Finally, a triad attention mechanism module, triplet attention (TA), is added to the different scale features in the golden tower pool module so that the space attention can focus more on the specific position of the fish body features in the channel through cross-dimensional interaction to suppress the fuzzy distortion caused by background interference in underwater scenes. Additionally, the parameter-sharing strategy is used in this process to make different scale features share the same learning weight parameters and further reduce the numbers of parameters and calculations. The experimental results show that the method presented in this paper yielded better results for the DeepFish underwater fish image dataset than other methods, with 91.56% for the Miou, 46.68 M for Params and 40.27 G for GFLOPS. In the underwater fish segmentation task, the method improved the segmentation accuracy of fish with similar colors and water quality backgrounds, improved fuzziness and small size and made the edge location of fish clearer.

Red Tide Detection Method Based on Improved U-Net Model-Taking GOCI Data in East China Sea as an Example.

In the coastal areas of China, the eutrophication of seawater leads to the continuous occurrence of red tide, which has caused great damage to Marine fisheries and aquatic resources. Therefore, the detection and prediction of red tide have important research significance. The rapid development of optical remote sensing technology and deep-learning technology provides technical means for realizing large-scale and high-precision red tide detection. However, the difficulty of the accurate detection of red tide edges with complex boundaries limits the further improvement of red tide detection accuracy. In view of the above problems, this paper takes GOCI data in the East China Sea as an example and proposes an improved U-Net red tide detection method. In the improved U-Net method, NDVI was introduced to enhance the characteristic information of the red tide to improve the separability between the red tide and seawater. At the same time, the ECA channel attention mechanism was introduced to give different weights according to the influence of different bands on red tide detection, and the spectral characteristics of different channels were fully mined to further extract red tide characteristics. A shallow feature extraction module based on Atrous Spatial Pyramid Convolution (ASPC) was designed to improve the U-Net model. The red tide feature information in a multi-scale context was fused under multiple sampling rates to enhance the model's ability to extract features at different scales. The problem of limited accuracy improvement in red tide edge detection with complex boundaries is solved via the fusion of deep and shallow features and multi-scale spatial features. Compared with other methods, the method proposed in this paper achieves better results and can detect red tide edges with complex boundaries, and the accuracy, precision, recall, and F1-score are 95.90%, 97.15%, 91.53%, and 0.94, respectively. In addition, the red tide detection experiments in other regions with relatively concentrated distribution also prove that the method has good applicability.

Clinical Progress and Optimization of Information Processing in Artificial Visual Prostheses.

Visual prostheses, used to assist in restoring functional vision to the visually impaired, convert captured external images into corresponding electrical stimulation patterns that are stimulated by implanted microelectrodes to induce phosphenes and eventually visual perception. Detecting and providing useful visual information to the prosthesis wearer under limited artificial vision has been an important concern in the field of visual prosthesis. Along with the development of prosthetic device design and stimulus encoding methods, researchers have explored the possibility of the application of computer vision by simulating visual perception under prosthetic vision. Effective image processing in computer vision is performed to optimize artificial visual information and improve the ability to restore various important visual functions in implant recipients, allowing them to better achieve their daily demands. This paper first reviews the recent clinical implantation of different types of visual prostheses, summarizes the artificial visual perception of implant recipients, and especially focuses on its irregularities, such as dropout and distorted phosphenes. Then, the important aspects of computer vision in the optimization of visual information processing are reviewed, and the possibilities and shortcomings of these solutions are discussed. Ultimately, the development direction and emphasis issues for improving the performance of visual prosthesis devices are summarized.

Underwater Holothurian Target-Detection Algorithm Based on Improved CenterNet and Scene Feature Fusion.

Aiming at the common problems, such as noise pollution, low contrast, and color distortion in underwater images, and the characteristics of holothurian recognition, such as morphological ambiguity, high similarity with the background, and coexistence of special ecological scenes, this paper proposes an underwater holothurian target-detection algorithm (FA-CenterNet), based on improved CenterNet and scene feature fusion. First, to reduce the model's occupancy of embedded device resources, we use EfficientNet-B3 as the backbone network to reduce the model's Params and FLOPs. At the same time, EfficientNet-B3 increases the depth and width of the model, which improves the accuracy of the model. Then, we design an effective FPT (feature pyramid transformer) combination module to fully focus and mine the information on holothurian ecological scenarios of different scales and spaces (e.g., holothurian spines, reefs, and waterweeds are often present in the same scenario as holothurians). The co-existing scene information can be used as auxiliary features to detect holothurians, which can improve the detection ability of fuzzy and small-sized holothurians. Finally, we add the AFF module to realize the deep fusion of the shallow-detail and high-level semantic features of holothurians. The results show that the method presented in this paper yields better results on the 2020 CURPC underwater target-detection image dataset with an AP50 of 83.43%, Params of 15.90 M, and FLOPs of 25.12 G compared to other methods. In the underwater holothurian-detection task, this method improves the accuracy of detecting holothurians with fuzzy features, a small size, and dense scene. It also achieves a good balance between detection accuracy, Params, and FLOPs, and is suitable for underwater holothurian detection in most situations.

Classification of Building Damage Using a Novel Convolutional Neural Network Based on Post-Disaster Aerial Images.

The accurate and timely identification of the degree of building damage is critical for disaster emergency response and loss assessment. Although many methods have been proposed, most of them divide damaged buildings into two categories-intact and damaged-which is insufficient to meet practical needs. To address this issue, we present a novel convolutional neural network-namely, the earthquake building damage classification net (EBDC-Net)-for assessment of building damage based on post-disaster aerial images. The proposed network comprises two components: a feature extraction encoder module, and a damage classification module. The feature extraction encoder module is employed to extract semantic information on building damage and enhance the ability to distinguish between different damage levels, while the classification module improves accuracy by combining global and contextual features. The performance of EBDC-Net was evaluated using a public dataset, and a large-scale damage assessment was performed using a dataset of post-earthquake unmanned aerial vehicle (UAV) images. The results of the experiments indicate that this approach can accurately classify buildings with different damage levels. The overall classification accuracy was 94.44%, 85.53%, and 77.49% when the damage to the buildings was divided into two, three, and four categories, respectively.

FTY720 Reduces Endothelial Cell Apoptosis and Remodels Neurovascular Unit after Experimental Traumatic Brain Injury.

Traumatic brain injury (TBI) is a major cause of death and disability worldwide. A sequence of pathological processes occurred when there is TBI. Previous studies showed that sphingosine-1-phosphate receptor 1 (S1PR1) played a critical role in inflammatory response in the brain after TBI. Thus, the present study was designed to evaluate the effects of the S1PR1 modulator FTY720 on neurovascular unit (NVU) after experimental TBI in mice. The weight-drop TBI method was used to induce TBI. Western blot (WB) was performed to determine the levels of SIPR1, claudin-5 and occludin at different time points. FTY720 was intraperitoneally administered to mice after TBI was induced. The terminal deoxynucleotidyl transferase-dUTP nick end labeling (TUNEL) assay was used to assess endothelial cell apoptosis. Immunofluorescence and WB were performed to measure the expression of tight junction proteins: claudin-5 and occludin. Evans blue (EB) permeability assay and brain water content were applied to evaluate the blood-brain barrier (BBB) permeability and brain edema. Immunohistochemistry was performed to assess the activation of astrocytes and microglia. The results showed that FTY720 administration reduced endothelial cell apoptosis and improved BBB permeability. FTY720 also attenuated astrocytes and microglia activation. Furthermore, treatment with FTY720 not only improved neurological function, but also increased the survival rate of mice significantly. These findings suggest that FTY720 administration restored the structure of the NVU after experimental TBI by decreasing endothelial cell apoptosis and attenuating the activation of astrocytes. Moreover, FTY720 might reduce inflammation in the brain by reducing the activation of microglia in TBI mice.

The application of computer vision to visual prosthesis.

A visual prosthesis is an auxiliary device for patients with blinding diseases that cannot be treated with conventional surgery or drugs. It converts captured images into corresponding electrical stimulation patterns, according to which phosphenes are generated through the action of internal electrodes on the visual pathway to form visual perception. However, due to some restrictions such as the few implantable electrodes that the biological tissue can accommodate, the induced perception is far from ideal. Therefore, an important issue in visual prosthesis research is how to detect and present useful information in low-resolution prosthetic vision to improve the visual function of the wearer. In recent years, with the development and broad application of computer vision methods, researchers have investigated the possibility of their utilization in visual prostheses by simulating prosthetic visual percepts. Through the optimization of visual perception by image processing, the efficiency of visual prosthesis devices can be further improved to better meet the needs of prosthesis wearers. In this article, recent works on prosthetic vision centering on implementing computer vision methods are reviewed. Differences, strengths, and weaknesses of the mentioned methods are discussed. The development directions of optimizing prosthetic vision and improving methods of visual perception are analyzed.

Deep Supervised Residual Dense Network for Underwater Image Enhancement.

Underwater images are important carriers and forms of underwater information, playing a vital role in exploring and utilizing marine resources. However, underwater images have characteristics of low contrast and blurred details because of the absorption and scattering of light. In recent years, deep learning has been widely used in underwater image enhancement and restoration because of its powerful feature learning capabilities, but there are still shortcomings in detailed enhancement. To address the problem, this paper proposes a deep supervised residual dense network (DS_RD_Net), which is used to better learn the mapping relationship between clear in-air images and synthetic underwater degraded images. DS_RD_Net first uses residual dense blocks to extract features to enhance feature utilization; then, it adds residual path blocks between the encoder and decoder to reduce the semantic differences between the low-level features and high-level features; finally, it employs a deep supervision mechanism to guide network training to improve gradient propagation. Experiments results (PSNR was 36.2, SSIM was 96.5%, and UCIQE was 0.53) demonstrated that the proposed method can fully retain the local details of the image while performing color restoration and defogging compared with other image enhancement methods, achieving good qualitative and quantitative effects.

Podoplanin influences the inflammatory phenotypes and mobility of microglia in traumatic brain injury.

Traumatic brain injury (TBI) represents a major cause of death and disability worldwide. Exacerbated neuroinflammation following TBI causes secondary injury. Podoplanin (PDPN) is a small transmembrane mucin-like glycoprotein that promotes the inflammatory response in different tissues and cells. However, the contribution of PDPN to neuroinflammation and microglial activation is unknown. Here, we found that PDPN was correlated with microglial activation after TBI in mice. Meanwhile, PDPN expression could be induced by trauma-related stimuli, such as lipopolysaccharide (LPS), ATP, H2O2 and hemoglobin (Hb), in primary microglia. Furthermore, with Hb treatment in vitro, knockdown of PDPN could decrease the proportion of M1-like microglia and increase the proportion of M2-like microglia via reduced secretion of IL-1ß and TNF-α and increased secretion of IL-10 and TGF-ß compared to the control microglia. Immunofluorescence also showed that CD86-positive microglia were decreased and CD206-positive microglia were elevated in the PDPN-KD group. Additionally, PDPN knockdown impaired microglial mobility and phagocytosis and decreased the expression of matrix metalloproteinases (mainly MMP2 and MMP9). In summary, PDPN plays an important role in microglia-mediated inflammation and may serve as a potential target for TBI treatment.

Continuous-wavelet-transform-based automatic curve fitting method for laser-induced breakdown spectroscopy.

In this work, an automatic curve fitting method based on a continuous-wavelet transform (CWT) is proposed to resolve overlapped peaks and to adaptively extract the major peaks in laser-induced breakdown spectroscopy (LIBS). From the local minimum of the second derivative of the LIBS spectrum calculated with CWT, the number of individual peaks is determined, and corresponding peak positions are estimated. The full width at half-maximums (FWHMs) of individual peaks are estimated from the separation of two maxima siding the minimum. A threshold is introduced to eliminate the small peaks and therefore reduce the number of fitting parameters and adaptively extract the major peaks with different spectral intensities. The Trust-Region algorithm is used for parameter optimization. The proposed method is used to analyze both simulated LIBS spectra and experimental overlapped peaks. Both simulated and experimental results show that the proposed method can resolve overlapped peaks even with a low separation degree, although the minimum resolvable separation degree depends on the FWHM ratio and strength ratio of individual peaks and the wavelet scale. In a LIBS calibration experiment of N2/SF6 gasses mixture, after resolving the overlapped peaks with the proposed method, better linear correlations between the concentration and intensity of F (with an adjusted R-squared value 0.9972), as well as between the concentration ratio and intensity ratio of nitrogen to fluorine (with adjusted R-squared values >0.98 and 0.99) are obtained.

Simultaneous mapping of reflectance, transmittance and optical loss of highly reflective and anti-reflective coatings with two-channel cavity ring-down technique.

We demonstrate the use of a two-channel cavity ring-down (CRD) technique for simultaneously measuring/mapping the reflectance R, transmittance T and optical loss L (absorption plus scattering losses) of highly reflective (HR) and anti-reflective (AR) laser components. High reflectance/transmittance of HR/AR components is measured with the ring-down time of CRD signals, while the low residual transmittance/ reflectance of HR/AR components is determined by the amplitude ratio of two CRD signals, and the optical loss is then determined via L = 1-R-T. Experiments are performed to measure and map R, T, and L of HR mirrors with different transmittance levels from below 1ppm to about 70 ppm (part-per-million) and of one AR window at 635nm. For a 4 ppm-transmittance HR mirror, the measured R, T, and L at one position are 99.99821 ± 0.00004%, 4.042 ± 0.008 ppm and 13.9 ± 0.4 ppm, respectively. For the AR sample, the measured T, R, and L at one position are 99.99279 ± 0.00004%, 50.0 ± 0.7 ppm and 22.0 ± 0.4 ppm, respectively. The sub-ppm standard deviations for R, T, and L indicate the high accuracy of the two-channel CRD technique for the simultaneous measurements of reflectivity, transmittance and optical loss of HR and AR components. High-resolution mappings of R, T, and L of both HR and AR samples are demonstrated. The simultaneous measurements/mappings of reflectance, transmittance, and optical loss with sub-ppm accuracy are of great importance to the preparation of high-performance laser optics for applications such as gravitational-wave detection and laser gyroscopes.

Reflectivity mapping of large-aperture mirrors with cavity ringdown technique.

A high-precision reflectivity measurement device based on an optical-feedback cavity ringdown technique is developed for mapping reflectivity distributions of large-aperture highly reflective (HR) mirrors with a diameter up to 300 mm. Reflectivity maps are obtained by two-dimensionally raster scanning the large-aperture mirrors and measuring the reflectivity as a function of position. By employing a laser source with a beam diameter of approximately 0.4 mm, reflectivity maps with sub-millimeter spatial resolution are achieved. The reflectivity non-uniformity of HR mirrors is investigated by statistically analyzing the experimental reflectivity distributions. The measurement repeatability of the device is also experimentally investigated, with a standard deviation of approximately 0.0001% for reflectivity higher than 99.99%.

Sea Ice Detection Based on an Improved Similarity Measurement Method Using Hyperspectral Data.

Hyperspectral remote sensing technology can acquire nearly continuous spectrum information and rich sea ice image information, thus providing an important means of sea ice detection. However, the correlation and redundancy among hyperspectral bands reduce the accuracy of traditional sea ice detection methods. Based on the spectral characteristics of sea ice, this study presents an improved similarity measurement method based on linear prediction (ISMLP) to detect sea ice. First, the first original band with a large amount of information is determined based on mutual information theory. Subsequently, a second original band with the least similarity is chosen by the spectral correlation measuring method. Finally, subsequent bands are selected through the linear prediction method, and a support vector machine classifier model is applied to classify sea ice. In experiments performed on images of Baffin Bay and Bohai Bay, comparative analyses were conducted to compare the proposed method and traditional sea ice detection methods. Our proposed ISMLP method achieved the highest classification accuracies (91.18% and 94.22%) in both experiments. From these results the ISMLP method exhibits better performance overall than other methods and can be effectively applied to hyperspectral sea ice detection.

Extinction measurement with open-path cavity ring-down technique of variable cavity length.

Open-path cavity ring down (OPCRD) technique with variable cavity length was developed to measure optical extinction including scattering and absorption of air in laboratory environment at 635 nm wavelength. By moving the rear cavity mirror of the ring-down cavity to change cavity length, ring-down time with different cavity lengths was experimentally obtained and the dependence of total cavity loss on cavity length was determined. The extinction coefficient of air was determined by the slope of linear dependence of total cavity loss on cavity length. The extinction coefficients of air with different particle concentrations at 635 nm wavelength were measured to be from 10.46 to 84.19 Mm-1 (ppm/m) in a normal laboratory environment. This variable-cavity-length OPCRD technique can be used for absolute extinction measurement and real-time environmental monitoring without closed-path sample cells and background measurements.

[Trace Moisture Measurement with 5.2 µm Quantum Cascade Laser Based Continuous-Wave Cavity Ring-Down Spectroscopy].

Trace moisture concentration in high-purity gases is an important parameter in semiconductor manufacturing because many manufacturing processes are sensitive to moisture even on the level of parts per billion by volume (ppbv). Detection of trace moisture in mid-infrared spectral region is beneficial due to more abundant and stronger spectral lines in this region. Recently, Quantum cascade lasers (QCLs) with high output power, narrow line-width, and high reliability have been developing rapidly and have become promising light sources for sensitive spectroscopic measurements. By employing a 5.2 µm external-cavity tunable quantum cascade laser, a continuous-wave cavity ring-down spectroscopy (CRDS) experimental setup is established and applied to detect trace moisture in high-purity nitrogen gas. In the experiment, the CRDS signal is averaged to improve the detection sensitivity, and the optimal averaging number is determined by Allan variance calculation to be 602. For trace moisture detection, the absorption cross-section of H(2)O in the spectral range between 1 905 and 1 925 cm(-1) is simulated according to the HITRAN database and the optimal detection spectral line is chosen. Detected at 1 918 cm(-1) absorption line at 296 K temperature and 1 atm pressure, the measured moisture concentration is in good agreement with the nominal value, and the minimum detectable moisture concentration of 24.8 ppbv is achieved when cavity mirrors with reflectance of 99.93% are used. The experimental results show that mid-infrared cavity ring-down spectroscopy technique has great potential in a wide variety of applications, such as industrial production control, environmental monitoring and health diagnosis, etc.

Simultaneous determination of optical loss, residual reflectance and transmittance of highly anti-reflective coatings with cavity ring down technique.

Cavity ring down (CRD) technique was employed to measure optical losses (absorption and scattering losses), residual reflectance and transmittance of anti-reflectively (AR) coated laser components with transmittance higher than 99.9%. By inserting the AR coated laser component with parallel optical surfaces into the ring-down cavity and measuring the ring-down time versus the angle of incidence with respect to the surface normal, the optical loss and residual reflectance of the laser component were determined respectively at normal and out-of-normal incidences with repeatability of part-per-million level. The transmittance was also determined simultaneously. Experimental results demonstrated that CRD is a simple, inexpensive and fast technique for highly accurate measurements of optical loss, residual reflectance, and transmittance of AR coated laser components widely used in high-power laser systems.

Revelation of the discrepancy of volatile compounds in fig (Ficus carica) via gas chromatography ion-mobility spectrometry.

The volatile compounds of fig (Ficus carica) are influenced by various factors. To explore the composition and difference of volatile compounds among figs, gas chromatography ion mobility spectrometry (GC-IMS) was used to study the volatiles of figs from various regions, diverse cultivars, and after treatment with different drying methods. Aldehydes were the main volatile compounds in Bojihong from Shandong, while esters, ketones, and alcohols were the main volatile compounds in Bojihong from Sichuan and Guangdong. The volatiles of Branswick and Banane were similar, but differed significantly from those of Bojihong. Drying had the most significant effect on fig volatiles, which greatly reduced the content of benzaldehyde, (E)-2-hexenal, 2-methylbutanal aldehydes, lost the content of esters such as isoamyl acetate, butyl acetate, ethyl butyrate, and generated some ketones and ethers. The results showed that Bojihong from Shandong was more suitable for the processing of subsequent fig drying products.