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Abstract Severe fever with thrombocytopenia syndrome (SFTS) is caused by the SFTS virus (SFTSV) with high morbidity and mortality. The major immunodominant region of SFTSV surface glycoprotein (G) remains unclear. In this study, we constructed adenovirus type 5 (Ad5) vectored vaccine candidates expressing different regions of SFTSV G (Gn, Gc and Gn-Gc) and evaluated their immunogenicity and protective efficacy in mice. In wild-type mice, compared with Ad5-Gc or Ad5-Gn-Gc, Ad5-Gn recruited/activated more dendritic cells and B cells in lymph nodes or peripheral blood, causing Th1-/Th2-mediated responses in splenocytes and triggered a greater level of SFTSV-neutralizing antibodies. In IFNAR Ab-treated mice, immunization of Ad5-Gn exhibited better protection against SFTSV challenge than Ad5-Gc or Ad5-Gn-Gc. Furthermore, passive immunization revealed complete protective immunity of Gn-specific serum rather than Gc. Collectively, our data demonstrated that Gn is the immunodominant fragment of SFTSV G and could be a potential candidate for SFTSV vaccine development.

This review rigorously assesses the burgeoning research into the role of polyphenols in modulating mitophagy, an essential cellular mechanism for the targeted removal of impaired mitochondria. These natural compounds, known for their low toxicity, are underscored for their potential in therapeutic strategies against a diverse array of diseases, such as neurodegenerative, cardiovascular, and musculoskeletal disorders. The analysis penetrates deeply into the molecular mechanisms whereby polyphenols promote mitophagy, particularly by influencing crucial signaling pathways and transcriptional regulators, including the phosphatase and tensin homolog (PTEN) induced putative kinase 1 (PINK1)/parkin and forkhead box O3 (FOXO3a) pathways. Noteworthy discoveries include the neuroprotective properties of resveratrol and curcumin, which affect both autophagic pathways and mitochondrial dynamics, and the pioneering integration of polyphenols with other natural substances to amplify therapeutic effectiveness. Furthermore, the review confronts the issue of polyphenol bioavailability and emphasizes the imperative for clinical trials to corroborate their therapeutic viability. By delivering an exhaustive synthesis of contemporary insights and recent advancements in polyphenol and mitophagy research, this review endeavors to catalyze additional research and foster the creation of innovative therapeutic modalities that exploit the distinctive attributes of polyphenols to manage and prevent disease.

Street space is a crucial component of public space, serving as a site for a variety of human activities. However, prior studies have primarily focused on the traffic function of street space, neglecting other functional types, such as residential and commercial. To address this gap, this study proposes a classification method for street space by integrating taxi trajectory and street view imagery. The dynamic travel features of the residents are extracted from taxi trajectory data and the multi-level physical environment features of the streets are constructed based on street view imagery data. Then, the street space with same urban functions is assigned to the same cluster based on the dynamic travel features of the residents, the multi-level physical environment features of streets and K-means method. The proposed method is empirically applied to the Futian District of Shenzhen to demonstrate its validity, and the street space is successfully classified into five types, namely public, traffic, commercial, residential, and mixed commercial and residential. This work contributes to the evaluation of street space quality, facilitating scientific planning and efficient governance of street space.

To evaluate the effects of inactivated Lactobacillus plantarum (LPM) on carnivorous fish, juvenile largemouth bass [initial body mass: (5.62±0.13) g] were fed five isonitrogenous and isoenergetic diets containing 0 (M0), 200 (M1), 400 (M2), 600 (M3), and 800 (M4) mg/kg LPM for 8 weeks. The results showed that in terms of growth, weight gain rate (WGR) and specific growth rate (SGR) showed significant second-order polynomial trend with increasing LPM supplementation. WGR and SGR of the M3 group were significantly increased by 14.18% and 5.62%, respectively, compared with the M0 group. Significant linear and second order polynomial trends were observed for feed conversion ratio (FCR), protein efficiency ratio (PER) and protein deposition rate (PDR) with the increase in dietary LPM. FCR was significantly reduced by 8.9% in the M2 group compared to the M0 group. In terms of intestinal health, intestinal amylase, intestinal villi number, length and width showed significant second order polynomial trends with increasing LPM supplementation. Significant linear and second order polynomial trends were observed for intestinal lipase activity, liver crude fat content with increasing dietary LPM. Dietary LPM supplementation of 600 mg/kg significantly increased the abundance of beneficial bacteria. In terms of antioxidant capacity, with the increase of LPM supplemental level, superoxide dismutase activity, malondialdehyde content of serum and liver, and liver Cu/Zn-SOD, Keap-1 and TGF-β1 mRNA expression levels showed significant linear and second-order polynomial trend, and Nrf2 mRNA expression level showed a significant second-order polynomial trend. Overall, the appropriate level of LPM (400–600 mg/kg) promoted the growth performance and feed utilization of largemouth bass by increasing the abundance of intestinal bacteria, improving the activities of digestive enzymes and antioxidant enzymes, and up-regulating the mRNA expressions of genes related to antioxidants.

AbstractLandslides can result in extensive casualties and huge economic losses. The accurate discrimination of the main slip direction and deformation trajectory is an important prerequisite for studying landslide formation mechanisms and designing landslide control schemes. In the process of landslide evolution over time, the main slip direction also changes dynamically and provides a comprehensive reflection of the landslide displacement state. However, few studies on this topic have been published. In this paper, a new methodology for analyzing slope stability is proposed based on three techniques: interferometric synthetic aperture radar (InSAR), unmanned aerial vehicle (UAV), and ground-based interferometric synthetic aperture radar (GB-InSAR). The Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique is combined with an overall analysis of the study area to identify the regions of interest (ROIs) with large deformation and the starting target points, and the fusion results of radar deformation data (RDD) and digital surface model (DSM) data are used to fit the deformation surface field of the ROIs. The gradient descent approach is executed to obtain the running trajectory points of the target masses so that the main slip direction and displacement trajectory in the study area can be predicted at small scales. The measured data for the Hongshiyan landslide in Yunnan Province are used to verify the effectiveness of the method, and the predicted results are consistent with the actual landslide direction. The experimental results show that the method can exactly identify the deformation area, especially in the case of a fast-changing deformation trend. This approach can provide more accurate monitoring area results to support the rapid control and prevention of landslide hazards by analyzing the minimum pixel grid (i.e. points), as the smallest spatial unit at a time interval of minutes. The study shows that the method can efficiently combine space–Earth multisource monitoring data to clarify the main slide direction and improve the postslide trajectory prediction of the slope, which is beneficial for assessing disaster risks and improving landslide prevention and control effects, reflecting the engineering application value of the approach.

Cellular automata (CA) models have been extensively employed to predict and understand the spatiotemporal dynamics of land use. Driving factors play a significant role in shaping and driving land-use changes. Mining land-use transition rules from driving factors and quantifying the contribution of driving factors to land-use dynamics are fundamental aspects of CA simulation. However, existing CA models have limitations in obtaining accurate transition rules and reliable interpretations simultaneously for multiple land-use simulations. In this study, we constructed a CA model based on a tree-based deep learning algorithm, deep cascade forest (DCF), to improve multiple land-use simulations and driving factors analysis. The DCF algorithm was utilized to mine accurate multiple land-use transition rules without overfitting to improve CA simulation accuracy. Additionally, a novel ensemble mean decrease of impurity (MDI) factor importance analysis method (DCF-MDI), which leverages the cascade structure of the DCF model, was proposed to qualify the contribution of each driving factor to land-use dynamics stably and efficiently. To evaluate the effectiveness of the proposed DCF-CA, we applied the model to simulate land-use distributions and explore the driving mechanisms of land-use dynamics in the Pearl River Delta (PRD), China, from 2000 to 2010. Compared to existing models, the proposed DCF-CA model exhibits the highest accuracy (FoM = 23.79%, PA = 39.77%, UA = 36.35%, OA = 91.50%), which demonstrates its superiority in mining accurate transition rules for capturing multiple land-use dynamics. Factor importance analysis reveals that the proposed DCF-MDI method yields more stable ranking orders and lower standard deviation of contribution weights (

AbstractAs a landmark in China’s hydraulic engineering history, the Central Route of the South–North Water Diversion Project (SNWD-CR) plays a major role in alleviating severe water shortages in Northern China. Existing InSAR monitoring of the SNWD-CR based on one-dimensional viewing geometry and Sentinel-1 imaging is insufficient for a comprehensive assessment of deformation causes. Moreover, the poor geocoding accuracy hampers the previous interpretation of the estimated deformations. Here we carry out comprehensive two-dimensional deformation analyses over the head of the SNWD-CR by integrating the multitemporal PS/DS InSAR and the stress–strain model, where the well-designed topography and geocoded error correction can help to identify the causes of deformation. We apply our strategy to ascending and descending TerraSAR-X meter-resolution datasets acquired from June 2020 to February 2021. Geocoding errors in both ascending and descending orbits are corrected, and the relationship between topography error and horizontal positioning error is discussed. The multi-data fusion results reveal insignificant deformations in the east–west direction except for a makeshift rigid-frame bridge, whereas the vertical deformation field shows two uplifts and a subsidence area in the narrow canal with a maximum rate of 23 mm/y, which are likely to be related to rainfall and water diversion pressure. Furthermore, the combination of the external DEM and topographic residual offers the chance to reconstruct the high-accuracy DEM.

Abstract Background Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability. Methods We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs. Results Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer’s disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015). Conclusions The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.

This study aimed to investigate the effect of N-acetylcysteine (NAC) on indomethacin (IDMT)-induced intestinal injury in a piglet model and explore the underlying molecular mechanisms. Piglets were randomly divided into 3 treatment groups: (1) control group; (2) IDMT group; (3) NAC+IDMT group. The results showed that NAC administration significantly increased the average daily gain of piglets, attenuated the intestine hyperemia, and restored normal jejunal morphology. Further studies indicated that NAC administration significantly increased plasma citrulline concentration and jejunal villin expression, but decreased the content of proinflammatory cytokines in plasma and jejunum of IDMT-stimulated piglets. NAC administration selectively decreased the proportion of eosinophils but not neutrophils in plasma. Furthermore, NAC administration significantly increased the activities of superoxide dismutase and catalase in plasma but decreased the concentrations of hydrogen peroxide (plasma) and malondialdehyde (plasma and jejunum), as well as the activity of myeloperoxidase (jejunum) when comparing NAC+IDMT group with IDMT group. Gene Ontology analysis showed that the significantly enriched molecular function term was “ubiquitin-like protein ligase binding” for NAC+IDMT versus IDMT differentially regulated genes. In the biological process category, differentially regulated genes of NAC+IDMT versus IDMT were mainly enriched in immune-related terms. The major enrichments for differentially regulated proteins (DRPs) of NAC+IDMT versus IDMT were terms involved in lipid metabolism and immune response. KEGG pathway enrichment analysis showed that “arginine biosynthesis” was a significant enrichment term for the DRPs of NAC+IDMT versus IDMT. Further studies demonstrated that NAC administration up-regulated argininosuccinate synthase 1 mRNA expression and down-regulated arginase mRNA expression in the jejunum of IDMT-stimulated piglets. Moreover, the content of nitric oxide was restored to a normal level with the reduction of nitric oxide synthase activity. NAC administration ameliorated intestinal injury in IDMT-challenged piglets by enhancing antioxidant and anti-inflammatory functions and modulating arginine metabolism in the small intestine.

Airborne laser scanning (ALS) point cloud classification is a necessary step for understanding 3-D scenes and their applications in various industries. However, the classification accuracy and efficiency are low: 1) point cloud classification methods lack effective filtering of the large number of traditional features, 2) significant category imbalance and coordinate scale problems in ALS point cloud classification. To address these problems, this article proposes an airborne LiDAR point cloud classification method based on deep learning network with optimal feature fusion-based spectral information. This method involves the following steps: First, multiscale point cloud features are extracted, and random forest method is used to filter the features, while spectral information is fused to obtain a point cloud feature dataset with less but better data. Second, to adapt to the characteristics of the airborne point cloud, the improved RandLA-Net can simultaneously retain the advantages of random sampling and learn deeper semantic information by fusing the constructed point cloud features with the local feature aggregation module in the network. Third, four fusion models are constructed to verify the effectiveness of the optimal feature fusion-based spectral information network (OFFS-Net) model for airborne point cloud classification. Last, these models are trained and tested on Vaihingen 3-D dataset. The OFFS-Net achieves overall accuracy score of 84.9% and F1-score of 72.3%, which are better than the mainstream methods. This also validates that the proposed OFFS-Net point cloud classification method, based on the advantages of geometric feature and spectral information is excellent.

Rabies is a zoonotic infectious disease with a high fatality rate. It is caused by a virus in the genus Lyssavirus and is a global public health threat. The rabies virus invades and infects cells mainly via a glycoprotein, which may involve multiple receptors. Neutralizing antibodies against the rabies virus function by blocking the binding of the glycoprotein to a receptor or preventing the membrane fusion process. Vaccination combined with anti-rabies virus neutralizing antibodies is essential for postexposure prophylaxis for category III exposure to the rabies virus. In this review, we discussed the neutralizing epitopes of the rabies virus and the neutralization mechanism of monoclonal antibodies. The neutralizing antibodies that have been commercialized or are under development are also summarized. Our review would provide a basis for the further development of safe and effective broad-spectrum neutralizing antibodies to replace the rabies virus immunoglobulin in rabies post-exposure prophylaxis.

Three or four intramuscular doses of the inactivated human rabies virus vaccines are needed for pre- or post-exposure prophylaxis in humans. This procedure has made a great contribution to prevent human rabies deaths, which bring huge economic burdens in developing countries. Herein, a recombinant adeno-associated virus serotype 9, AAV9-RABVG, harbouring a RABV G gene, was generated to serve as a single dose rabies vaccine candidate. The RABV G protein was stably expressed in the 293T cells infected with AAV9-RABVG. A single dose of 2 × 1011 v.p. of AAV9-RABVG induced robust and long-term positive seroconversions in BALB/c mice with a 100% survival from a lethal RABV challenge. In Cynomolgus Macaques vaccinated with a single dose of 1 × 1013 v.p. of AAV9-RABVG, the titres of rabies VNAs increased remarkably from 2 weeks after immunity, and maintained over 31.525 IU/ml at 52 weeks. More DCs were activated significantly for efficient antigen presentations of RABV G protein, and more B cells were activated to be responsible for antibody responses. Significantly more RABV G specific IFN-γ-secreting CD4+ and CD8+ T cells, and IL-4-secreting CD4+ T cells were activated, and significantly higher levels of IL-2, IFN-γ, IL-4, and IL-10 were secreted to aid immune responses. Overall, the AAV9-RABVG was a single dose rabies vaccine candidate with great promising by inducing robust, long-term humoral responses and both Th1 and Th2 cell-mediated immune responses in mice and non-human primates.

Abstract A variety of power electronic equipment in AC/DC distribution networks causes oscillations with unknown origin. A self‐sustained low‐frequency oscillation appeared recently in a practical AC/DC distribution network in Tongli, Jiangsu Province, China. In this paper, the accident is introduced, and the oscillation phenomenon has been successfully reproduced by theoretical modelling. Based on the small signal model, the oscillation is well analysed and an additional controller is designed to suppress this low‐frequency oscillation. The reason of the oscillation is related to the power and control parameters of buck converter; the system is critically stable within the linear system stability theory, namely, the leading eigenvalues of the small‐signal system are on the imaginary axis. Meanwhile, a novel inhomogeneity phenomenon for oscillation amplitudes on different voltage‐level DC buses, which are not proportional to their steady‐state voltages, is found. This paper provides a new case study of self‐sustained oscillations coming from a practical AC/DC distribution network and is of great significance for our understanding of oscillations in electric power engineering.

Research on silicon (Si), an element considered beneficial for plant growth, has focused on abiotic and biotic stress mitigation. However, the effect of Si on tomato fruit quality under normal growth conditions remains unclear. This study investigated the effects of applying different levels of Si (0 mmol·L−1 [CK], 0.6 mmol·L−1 [T1], 1.2 mmol·L−1 [T2], and 1.8 mmol·L−1 [T3]) in foliar sprays on tomato fruit quality cultivated in substrates, and the most beneficial Si level was found. Compared to CK, exogenous Si treatments had a positive influence on the appearance and nutritional quality of tomato fruits at the mature green, breaker, and red ripening stages. Of these, T2 treatment significantly increased peel firmness and single-fruit weight in tomato fruits. The contents of soluble sugars, soluble solids, soluble proteins, and vitamin C were significantly higher, and the nitrate content was significantly lower in the T2 treatment than in the CK treatment. Cluster analysis showed that T2 produced results that were significantly different from those of the CK, T1, and T3 treatments. During the red ripening stage, the a* values of fruits in the T2 treatment tomato were significantly higher than those in the other three treatments. Moreover, the lycopene and lutein contents of the T2 treatment increased by 12.90% and 17.14%, respectively, compared to CK. T2 treatment significantly upregulated the relative gene expression levels of the phytoene desaturase gene (PDS), the lycopene ε-cyclase gene (LCY-E), and the zeaxanthin cyclooxygenase gene (ZEP) in the carotenoid key genes. The total amino acid content in tomato fruits in the T2 treatment was also significantly higher than that of CK. In summary, foliar spraying of 1.2 mmol·L−1 exogenous Si was effective in improving the appearance and nutritional quality of tomato fruits under normal growth conditions. This study provides new approaches to further elucidate the application of exogenous silicon to improve tomato fruit quality under normal conditions.

Abstract With the popularity of various portable mobile devices with positioning functions, a large amount of spatial‐temporal trajectory data has emerged. To effectively compress large‐scale vehicle trajectory data and serve intelligent transportation system, we propose a spatial‐temporal trajectory data compression algorithm based on vehicle motion pattern recognition. The algorithm recognizes vehicles' turning behaviour and variable speed behaviour during the driving process through the analysis of vehicle motion patterns, and extracts the trajectory's turning feature points and variable speed feature points, so as to achieve online compression of the vehicle trajectory. By means of large‐scale experimental datasets, the authors compare this algorithm with representative trajectory compression algorithms in various performance metrics. The experimental results indicate that as an online vehicle trajectory compression algorithm, the proposed algorithm is superior to representative compression algorithms in compression accuracy and computational efficiency, and the compression results can reflect semantic motion information such as turning patterns and variable speed patterns in the trajectory. Therefore, this algorithm has comprehensive advantages, which is foundational for trajectory data mining in intelligent transportation systems.

The soil moisture from the South-to-North Water Diversion Middle Route Project is assessed in this study. Complex and variable geological conditions complicate the prediction of soil moisture in the study area. To achieve this aim, we carried out research on soil moisture inversion methods for channel slopes in the study area using massive monitoring data from multiple GNSS observatories on channel slopes, incorporating GNSS-R techniques and deep learning algorithms. To address the issue of low accuracy in linear inversion when using a single satellite, this study proposes a multi-satellite and multi-frequency data fusion technique. Furthermore, three soil moisture inversion models, namely, the linear model, BP neural network model, and GA-BP neural network model, are established by incorporating deep learning techniques. In comparison with single-satellite data inversion, with the data fusion technique proposed in this study, the correlation is improved by 12.7%, the root mean square error is reduced by 0.217, the mean square error is decreased by 0.884, and the mean absolute error is decreased by 0.243 with the linear model. With the BP neural network model, the correlation is increased by 15.4%, the root mean square error is decreased by 0.395, the mean square error is decreased by 0.465, and the mean absolute error is reduced by 0.353. Moreover, with the GA-BP neural network model, the correlation is improved by 6.3%, the root mean square error is decreased by 1.207, the mean square error is decreased by 0.196, and the mean absolute error is reduced by 0.155. The results indicate that performing data fusion by using multiple satellites and multi-frequency bands is a feasible approach for improving the accuracy of soil moisture inversion. These research findings provide new technical means for the risk analysis of deformation disasters in the expansive soil channel slopes of the South-to-North Water Diversion Middle Route Project.

In this article, the time difference physics-informed neural network (TD-PINN) is considered to approximate the solution of fractional water wave models. Based on the TD-PINN framework, the spatial partial derivatives are approximated by applying the neural network’s automatic differential operation and the time direction is discretized by making use of the BDF2 with the L1formula for the Caputo fractional derivative. We provide the detailed algorithm process based on our models. Finally, by choosing two different types of examples, we carry out numerical experiments to verify the effectiveness and feasibility of the proposed TD-PINN.

The emergency response is crucial in preventing and mitigating urban floods. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring. However, current research lacks a thorough exploration of the application of social media data and remote sensing imagery in the urban flood emergency response. To address this issue, this paper, while extracting disaster information based on social media data, deeply mines the spatiotemporal distribution characteristics and dynamic spatial accessibility of rescue points. Furthermore, SAR imagery and social media data for monitoring urban flooding are compared. This study took the Zhengzhou 7.20 urban flood as a case study and created a methodological framework to quickly extract flood disaster information (flood, landslide, and rescue points) using these two types of data; spatiotemporal analysis and random forest classification were also conducted to mine valuable information. Temporally, the study revealed that disaster information did not increase proportionally with the amount of rainfall during the rainfall process. Spatially, specific regions with higher susceptibility to flooding, landslides, and rescue points were identified, such as the central region characterized by low drainage standards and high-density urban areas, as well as the eastern region with low-lying terrain. Moreover, this study examined the spatial accessibility of rescue resources in real flood scenarios and found that their service coverage varied throughout the day during and after the disaster. In addition, social media excelled in high-density urban areas’ flood point extraction, while SAR performed better in monitoring floods at the edges of low-density urban areas and large water bodies, allowing them to complement each other, to a certain extent. The findings of this study provide scientific reference value for the optimal selection of rescue paths and the allocation of resources in the emergency response to urban floods caused by extreme rainstorms.

Due to expansive soils and high slopes, the deep excavated channel section of the China South–North Water-Diversion Middle-Route Project has a certain risk of landslide disaster. Therefore, examining the deformation law and mechanism of the channel slope in the middle-route section of the project is an extreme necessity for safe operation. However, the outdated monitoring method limits research on the surface deformation law and mechanism of the entire deep excavation channel section. For these reasons, we introduced a novel approach that combines SBAS-InSAR and GNSS, enabling the surface domain monitoring of the study area at a regional scale as well as real-time monitoring of specific target regions. By using SBAS-InSAR technology and leveraging 11-view high-resolution TerraSAR-X data, we revealed the spatiotemporal evolution law of surface deformations in the channel slopes within the study area. The results demonstrate that the predominant deformation in the study area was uplifted, with limited evidence of subsidence deformation. Moreover, there is a distinct region of significant uplift deformation, with the highest annual uplift rate reaching 19 mm/y. Incorporating GNSS and soil-moisture-monitoring timeseries data, we conducted a study on the correlation between soil moisture and the three-dimensional deformation of the ground surface, revealing a positive correlation between the soil moisture content and vertical displacement of the channel slope. Furthermore, combining field investigations on surface uplift deformation characteristics, we identified that the main cause of surface deformation in the study area was attributed to the expansion of the soil due to water absorption in expansive soils. The research results not only revealed the spatiotemporal evolution law and mechanism of the channel slope deformation in the studied section of the deep excavation channel but also provide successful guidance for the prevention and control of channel slope-deformation disasters in the study area. Furthermore, they offer effective technical means for the safe monitoring of the entire South–North Water-Diversion Middle-Route Project and similar long-distance water-conveyance canal projects.

Spatial equality of medical services refers to equal access to medical services in all regions. Currently, research on medical facility planning focuses mainly on efficiency, and less on methods for achieving medical facility access equality. In this study, we propose a medical service equality optimization method considering facility grade and Gaode actual travel time data. First, we use the maximum coverage location problem (MCLP) model to locate new medical facilities. Then, we incorporate a service capacity weight matrix reflecting medical facility grade into the quadratic programming (QP) model, with the objective of optimizing the bed configuration of each facility to maximize the spatial equality of medical accessibility. By measuring and optimizing medical accessibility in Guangzhou under different travel time thresholds, we analyzed the optimization results of central, peripheral, and edge areas. The results show that (1) the model significantly improves the spatial equality of medical accessibility. After optimization, fewer locations have very low (or low) and very high (or high) accessibility, while more locations have moderate accessibility. When the travel time threshold is 22 min, the number of locations with medium accessibility level increases by about 18.86%. (2) The higher the travel time threshold, the greater is the overall optimization effect. (3) Different regions have different optimization effects and a larger travel time threshold can improve the optimization effect of the peripheral areas more significantly. It is recommended that new medical facilities be built in the peripheral and edge areas, along with improvements to the transport system.

As a typical application of geodesy, the GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation technique was developed and has been applied for decades. For the integrated systems with multiple sensors, data fusion is one of the key problems. As a well-known data fusion algorithm, the Kalman filter can provide optimal estimates with known parameters of the models and noises. In the literature, however, the data fusion algorithm of the GNSS/INS integrated navigation and positioning systems is performed under a certain norm, and performance of the conventional filtering algorithms are improved only under this fixed and limited frame. The mixed norm-based data fusion algorithm is rarely discussed. In this paper, a mixed norm-based data fusion algorithm is proposed, and the hypothesis test statistics are constructed and adopted based on the chi-square distribution. Using the land vehicle data collected through the multi-GNSS and the IMU (Inertial Measurement Unit), the proposed algorithm is tested and compared with the conventional filtering algorithms. Results show that the influences of the outlying measurements and the uncertain noises are weakened with the proposed data fusion algorithm, and the precision of the estimates is further improved. Meanwhile, the proposed algorithm provides an open issue for geodetic applications with mixed norms.

The deep learning method has achieved great success in hyperspectral image classification, but the lack of labeled training samples still restricts the development and application of deep learning methods. In order to deal with the problem of small samples in hyperspectral image classification, a novel small sample classification method based on rotation-invariant uniform local binary pattern (RULBP) features and a graph-based masked autoencoder is proposed in this paper. Firstly, the RULBP features of hyperspectral images are extracted, and then the k-nearest neighbor method is utilized to construct the graph. Furthermore, self-supervised learning is conducted on the constructed graph so that the model can learn to extract features more suitable for small sample classification. Since the self-supervised training mainly adopts the masked autoencoder method, only unlabeled samples are needed to complete the training. After training, only a small number of samples are used to fine-tune the graph convolutional network, so as to complete the classification of all nodes in the graph. A large number of classification experiments on three commonly used hyperspectral image datasets show that the proposed method could achieve higher classification accuracy with fewer labeled samples.

In arid areas, the disordered accumulation of Na+ in cotton fields under mulched drip irrigation seriously affects the normal growth and development of cotton. To reveal the process and mechanism of Na+ accumulation, in situ field tests were conducted in typical cotton planting bases in the south of Xinjiang, China. Eight experimental scenarios were set up to use brackish and fresh water for drip irrigation under mulch during the whole growth period of cotton according to the drip irrigation system. By monitoring the weather, groundwater level, and soil moisture before and after irrigation, and testing the soil salinity and Na+ concentration, the temporal and spatial distribution and transport characteristics of Na+ were compared and studied. The results showed that: (1) owing to the strong spatial variation of soil permeability, soil water and salt dynamics exhibited significant differences after irrigation in all experimental scenarios in the study area, especially at a depth of 0–60 cm. (2) The Na+ profile exhibited a “thin waist”, whose depth depended on the climate and lithology of the soil; however, this phenomenon was not observed in the salt profile. (3) The accumulation of Na+ in cotton fields is an extremely complex dynamic process that is influenced by both natural and human activities. This process is controlled by the dynamic characteristics of soil water and salt, but it is different from the distribution of soil salt in time and space. This study provides necessary technical support for the formulation of drip irrigation systems under brackish water film, and provides a scientific basis for fine agricultural planting and water resources management in arid areas.

The current researches trend to adopt a low-resolution hot spot map to restore the original high-resolution representation to save computing cost, resulting in unsatisfactory detection performance, especially in human body recognition with a highly complex background. Aiming at this problem, we proposed a model of parallel connection of multiple sub-networks with different resolution levels on a high-resolution main network. It can maintain the network structure of a high-resolution hot spot map in the whole operation process. By using this structure in the human key point vector field network, the accuracy of human posture recognition has been improved with high-speed operation. To validate the proposed model’s effectiveness, two common benchmark data sets of COCO key point data set and MPII human posture data set are used for evaluation. Experimental results show that our network achieves the accuracy of 72.3% AP and 92.2% AP in the two data sets, respectively, which is 3%-4% higher than those of the existing mainstream researches. In our test, only the accuracy of backbone’s SimpleBaseline with ResNet-152 is close to ours, yet our network realized a much lower computing cost.

Underground mining has caused drastic disturbances to regional ecosystems and soil nutrients. Understanding the three-dimensional (3D) spatial distribution of soil nutrients in mining area farmland is crucial for agricultural production and environmental management. However, few studies have reported the 3D spatial distribution of soil organic matter (SOM) in coal mining subsidence area. In our study, a sequential Gaussian simulation (SGS) algorithm was used to analyse the spatial distribution of SOM based on observations of 180 soil samples in the Zhaogu mine in China. The results showed that the SOM content had considerable variation in spatial distribution at different soil depths (0–20, 20–40, 40–60 cm) and decreased with the increase in soil depth. The spatial variability of surface organic matter was the largest, and the coefficient of variation was 29.38%, which was moderately mutated. The spatial distribution of SOM also varied among slope locations. The SOM content was higher upslope and downslope than on the middle slope. In addition, given a threshold, SGS can be used to calculate the probability that the organic matter content at any position is lower or higher than the given value. The research results provide a reference for land reclamation and precision agriculture.

BackgroundThe clinical value and application of preoperative ultrasound contrast in the diagnosis of cervical lymph node metastasis in thyroid papillary carcinoma is investigated.MethodsIn total, 126 cases of thyroid papillary carcinoma were selected, the sensitivity and accuracy of color ultrasound and ultrasound contrast were analyzed by comparing preoperative gray-scale ultrasound, color ultrasound, and ultrasound contrast.ResultsThe accuracies of preoperative color ultrasound and ultrasound contrast in detecting lymph node metastasis were 74 and 82%, respectively, and their sensitivities were 80 and 94%, respectively. Lymph node metastasis was significantly more severe when the tumor diameter was >4 cm. The lymphatic metastatic rate of the patients with multifocal papillary carcinoma was 96.4%, whereas the lymphatic metastatic rate of the patients with thyroid gland lesions was 87.7%. The central foci of cervical lymph node metastasis included the following pathological subtypes: diffuse sclerosis type (89.3%, 25/28), high-cell type (72.2%, 8/11), and papillary type (40.0%, 4/10).ConclusionUltrasound contrast is more sensitive than color ultrasound in the diagnosis of cervical lymph node metastasis. Primary lesions ≥4 cm, lesion involvement, outer membrane, and high-risk pathologic subtypes and lesions were considered as the criteria for ultrasound contrast application.

Network-constrained spatial flows are usually used to describe movements between two spatial places on a road network. The analysis of the spatial associations between different types of network-constrained spatial flows plays a key role in understanding the spatial relationships among different movements. However, existing studies usually do not consider the effect of distance decay, which may reduce the effectiveness of the detected bivariate spatial flow patterns. Moreover, most existing studies are based on the planar space assumption, which is not suitable for network-constrained spatial flows. To overcome these problems, this study proposed a new statistical method, the Geographically Weighted Network Flow Cross K-function, which improves the Flow Cross K-Function method by taking the distance decay effect and the constraints of road networks into account. Both global and local versions are extended in this study: the global version measures the overall spatial association and the local version identifies the exact locations where a spatial association occurs. The experiments on simulated datasets show that the proposed method can identify predefined bivariate flow patterns. In a case study, the proposed method is also applied to flow data comprising Xiamen taxi and ride-hailing datasets. The results demonstrate that the proposed method effectively identifies the competitive relationships between taxi and ride-hailing services.

Hyperspectral image (HSI) classification is an important but challenging topic in the field of remote sensing and earth observation. By coupling the advantages of convolutional neural network (CNN) and Transformer model, the CNN–Transformer hybrid model can extract local and global features simultaneously and has achieved outstanding performance in HSI classification. However, most of the existing CNN–Transformer hybrid models use artificially specified hybrid strategies, which have poor generalization ability and are difficult to meet the requirements of recognizing fine-grained objects in HSI of complex scenes. To overcome this problem, we proposed a convolution–Transformer adaptive fusion network (CTAFNet) for pixel-wise HSI classification. A local–global fusion feature extraction unit, called the convolution–Transformer adaptive fusion kernel, was designed and integrated into the CTAFNet. The kernel captures the local high-frequency features using a convolution module and extracts the global and sequential low-frequency information using a Transformer module. We developed an adaptive feature fusion strategy to fuse the local high-frequency and global low-frequency features to obtain a robust and discriminative representation of the HSI data. An encoder–decoder structure was adopted in the CTAFNet to improve the flow of fused local–global information between different stages, thus ensuring the generalization ability of the model. Experimental results conducted on three large-scale and challenging HSI datasets demonstrate that the proposed network is superior to nine state-of-the-art approaches. We highlighted the effectiveness of adaptive CNN–Transformer hybrid strategy in HSI classification.

In point-cloud scenes, semantic segmentation is the basis for achieving an understanding of a 3D scene. The disorderly and irregular nature of 3D point clouds makes it impossible for traditional convolutional neural networks to be applied directly, and most deep learning point-cloud models often suffer from an inadequate utilization of spatial information and of other related point-cloud features. Therefore, to facilitate the capture of spatial point neighborhood information and obtain better performance in point-cloud analysis for point-cloud semantic segmentation, a multiscale, multi-feature PointNet (MSMF-PointNet) deep learning point-cloud model is proposed in this paper. MSMF-PointNet is based on the classical point-cloud model PointNet, and two small feature-extraction networks called Mini-PointNets are added to operate in parallel with the modified PointNet; these additional networks extract multiscale, multi-neighborhood features for classification. In this paper, we use the spherical neighborhood method to obtain the local neighborhood features of the point cloud, and then we adjust the radius of the spherical neighborhood to obtain the multiscale point-cloud features. The obtained multiscale neighborhood feature point set is used as the input of the network. In this paper, a cross-sectional comparison analysis is conducted on the Vaihingen urban test dataset from the single-scale and single-feature perspectives.

The Chinese South–North Water Transfer Project is an important project to improve the freshwater supply environment in the Chinese interior and greatly alleviates the water shortage in the Chinese North China Plain; its sustainable, healthy, and safe operation guarantees ecological protection and economic development. However, due to the special expansive soil and deep excavation structure, the first section of the South–North Water Transfer Project canal faces serious disease risk directly manifested by cracks in the slope of the canal. Currently, relying on manual inspection not only consumes a lot of human resources but also unnecessarily repeats and misses many inspection areas. In this paper, a monitoring method combining depth learning and Uncrewed Aerial Vehicle (UAV) high-definition remote sensing is proposed, which can detect the cracks of the channel slope in time and accurately and can be used for long-term health inspection of the South–North Water Transfer Project. The main contributions are as follows: (1) aiming at the need to identify small cracks in reinforced channels, a ground-imitating UAV that can obtain super-clear resolution remote-sensing images is introduced to identify small cracks on a complex slope background; (2) to identify fine cracks in massive images, a channel crack image dataset is constructed, and deep-learning methods are introduced for the intelligent batch identification of massive image data; (3) to provide the geolocation of crack-extraction results, a fast field positioning method for non-modeled data combined with navigation information is investigated. The experimental results show that the method can achieve a 92.68% recall rate and a 97.58% accuracy rate for detecting cracks in the Chinese South–North Water Transfer Project channel slopes. The maximum positioning accuracy of the method is 0.6 m, and the root mean square error is 0.21 m. It provides a new technical means for geological risk identification and health assessment of the South–North Water Transfer Central Project.

In this paper, we report the study of the process of fabricating a multi-layer metal micro-structure using UV-LIGA overlay technology, including mask fabrication, substrate treatment, and UV-LIGA overlay processes. To solve the process problems in the masking procedure, the swelling problem of the first layer of SU-8 thick photoresist was studied experimentally. The 5 μm line-width compensation and closed 20 μm and 30 μm isolation strips were designed and fabricated around the micro-structure pattern. The pore problem in the Ni micro-electroforming layer was analyzed and the electroforming parameters were improved. The pH value of the electroforming solution should be controlled between 3.8 and 4.4 and the current density should be below 3 A/dm2. To solve the problems of high inner stress and incomplete development of the micro-cylinder hole array with a diameter of 30 μm, the lithography process was optimized. The pre-baking temperature was increased via gradient heating and rose every 5 °C from 65 °C to 85 °C and then remained at 85 °C for 50 min – 1 h. In addition, the full contact exposure was used. Finally, a multi-layer metal micro-structure with high precision and good quality of micro-electroforming layer was fabricated using UV-LIGA overlay technology. Keywords: UV-LIGA overlay technology, SU-8 photoresist, Micro-electroforming technology, Multi-layer metal micro-structure

This paper proposes an efficient and noise-insensitive end-to-end lightweight deep learning method. The method synthesizes the characteristics of a frequency domain transform and a deep convolutional neural network. The former can extract multiscale information in vibration signal processing and the latter has a good classification performance, data-driven, and high transfer-learning ability. A vibration signal is decomposed into a pyramidal wavelet packet, and each sub-band coefficient is used as an input of a channel in the deep network. A deep residual convolutional network based on a separable convolution and concatenated rectified linear unit (CReLU) lightweight convolution technology is used for fault diagnosis. The proposed algorithm is compared with related deep learning algorithms using two bearing datasets produced by Case Western Reserve University (CWRU) and the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati. Compared with the existing algorithms, the experimental results show that the comprehensive performance of the algorithm proposed in this paper is “small, light, and fast,” and satisfactory diagnostic results are obtained in the fault diagnosis of rotating machinery.

Detecting dynamic community structure in vehicle movements is helpful for revealing urban structures and human mobility patterns. Despite the fruitful research outcomes of community detection, the discovery of irregular-shaped and statistically significant dynamic communities in vehicle movements is still challenging. To overcome this challenge, we developed an evolutionary ant colony optimization (EACO) method for detecting dynamic communities in vehicle movements. Firstly, a weighted, spatially embedded graph was constructed at each time snapshot. Then, an ant-colony-optimization-based spatial scan statistic was upgraded to identify statistically significant communities at each snapshot by considering the effects of the communities discovered at the previous snapshot. Finally, different rules defined based on the Jaccard coefficient were used to identify the evolution of the communities. Experimental results on both simulated and real-world vehicle movement datasets showed that EACO performs better than three representative dynamic community detection methods: FacetNet (a framework for analyzing communities and evolutions in dynamic networks), DYNMOGA (dynamic multi-objective genetic algorithm), and RWLA (random-walk-based Leiden algorithm). The dynamic communities identified by EACO may be useful for understanding the dynamic organization of urban structures.

The Yellow River Basin (YRB) has been facing severe water shortages; hence, the long-term dynamic monitoring of its surface water area (SWA) is essential for the efficient utilization of its water resources and sustainable socioeconomic development. In order to detect the changing trajectory of the SWA of the YRB and its influencing factors, we used available Landsat images from 1986 through to 2019 and a water and vegetation indices-based method to analyze the spatial–temporal variability of four types of SWAs (permanent, seasonal, maximum and average extents), and their relationship with precipitation (Pre), temperature (Temp), leaf area index (LAI) and surface soil moisture (SM).The multi-year average permanent surface water area (SWA) and seasonal SWA accounted for 46.48% and 53.52% in the Yellow River Basin (YRB), respectively. The permanent and seasonal water bodies were dominantly distributed in the upper reaches, accounting for 70.22% and 48.79% of these types, respectively. The rate of increase of the permanent SWA was 49.82 km2/a, of which the lower reaches contributed the most (34.34%), and the rate of decrease of the seasonal SWA was 79.18 km2/a, of which the contribution of the source region was the highest (25.99%). The seasonal SWA only exhibited decreasing trends in 13 sub-basins, accounting for 15% of all of the sub-basins, which indicates that the decrease in the seasonal SWA was dominantly caused by the change in the SWA in the main river channel region. The conversions from seasonal water to non-water bodies, and from seasonal to permanent water bodies were the dominant trends from 1986 to 2019 in the YRB. The SWA was positively correlated with precipitation, and was negatively correlated with the temperature. Because the permanent and seasonal water bodies were dominantly distributed in the river channel region and sub-basins, respectively, the change in the permanent SWA was significantly affected by the regulation of the major reservoirs, whereas the change in the seasonal SWA was more closely related to climate change. The increase in the soil moisture was helpful in the formation of the permanent water bodies. The increased evapotranspiration induced by vegetation greening played a significant positive role in the SWA increase via the local cooling and humidifying effects, which offset the accelerated water surface evaporation caused by the atmospheric warming.

In recent years, object detection in remote sensing images has become a popular topic in computer vision research. However, there are various problems in remote sensing object detection, such as complex scenes, small objects in large fields of view, and multi-scale object in different categories. To address these issues, we propose DFPN-YOLO, a dense feature pyramid network for remote sensing object detection. To address difficulties in detecting small objects in large scenes, we add a larger detection layer on top of the three detection layers of YOLOv3, and we propose Dense-FPN, a dense feature pyramid network structure that enables all four detection layers to combine semantic information before sampling and after sampling to improve the performance of object detection at different scales. In addition, we add an attention module in the residual blocks of the backbone to allow the network to quickly extract key feature information in complex scenes. The results show that the mean average precision (mAP) of our method on the RSOD datasets reached 92%, which is 8% higher than the mAP of YOLOv3, and the mAP increased from 62.41% on YOLOv3 to 69.33% with our method on the DIOR datasets, outperforming even YOLOv4.

Abstract Many viral proteins are related to suppressing apoptosis in target cells and are hence beneficial to viral replication. The V protein of Newcastle disease virus (NDV) is one such protein that plays an important role in inhibiting apoptosis in a species-specific manner. However, to date, there have been no reports clarifying the antiapoptotic mechanisms of the V protein. The present study was undertaken to determine the apoptotic potential of the V protein in a chicken embryo fibroblast cell line (DF-1 cell) and to elucidate its molecular mechanisms of action. Here, a yeast two-hybrid system was used to screen the host proteins that interact with the V protein and identified thioredoxin-like protein 1 (TXNL1) as a potential binding partner. Immuno-colocalization of V protein and TXNL1 protein in DF-1 cells further verified the interaction of the two proteins. Through the overexpression of TXNL1 protein and knockdown of TXNL1 protein in DF-1 cells, the effects of NDV replication and cell apoptosis were examined. Cell apoptosis was detected by flow cytometry. The mRNA and protein expression levels of Bax, Bcl-2 and Caspase-3 were detected by quantitative real-time PCR (Q-PCR) and Western blotting. NDV expression was detected by Q-PCR and plaque assay. The results revealed that the TXNL1 protein induced apoptosis and inhibited NDV replication in DF-1 cells. Furthermore, the Western blot and Q-PCR results suggested that TXNL1 induced cell apoptosis through a pathway involving Bcl-2\Bax and Caspase-3. Finally, this work provides insight into the mechanism by which the V protein inhibits apoptosis.

In this study, a 50-pixel linear array of 4H–SiC ultraviolet avalanche photodiodes operating in Geiger mode is reported for the first time. The 50 pixels within the linear array are all capable of detecting single photons but with a small number of them suffering from extra leakage current near avalanche breakdown. The enhanced leakage is likely correlated with structural defects existing within the epitaxial structure, which would lead to higher dark count rate (DCR) at the same single photon detection efficiency (SPDE) in Geiger mode. For a low-leakage avalanche photodiode device within the linear array, a room temperature SPDE of 15.4% at 280 nm is obtained with a corresponding DCR of 2.5 Hz/μm2.

The interface passivation of the HfO2/Si0.7Ge0.3 stack is systematically investigated based on low-temperature ozone oxidation and Si-cap methods. Compared with the Al2O3/Si0.7Ge0.3 stack, the dispersive feature and interface state density (Dit) of the HfO2/Si0.7Ge0.3 stack MOS (Metal-Oxide-Semiconductor) capacitor under ozone direct oxidation (pre-O sample) increases obviously. This is because the tiny amounts of GeOx in the formed interlayer (IL) oxide layer are more likely to diffuse into HfO2 and cause the HfO2/Si0.7Ge0.3 interface to deteriorate. Moreover, a post-HfO2-deposition (post-O) ozone indirect oxidation is proposed for the HfO2/Si0.7Ge0.3 stack; it is found that compared with pre-O sample, the Dit of the post-O sample decreases by about 50% due to less GeOx available in the IL layer. This is because the amount of oxygen atoms reaching the interface of HfO2/Si0.7Ge0.3 decreases and the thickness of IL in the post-O sample also decreases. To further reduce the Dit of the HfO2/Si0.7Ge0.3 interface, a Si-cap passivation with the optimal thickness of 1 nm is developed and an excellent HfO2/Si0.7Ge0.3 interface with Dit of 1.53 × 1011 eV−1cm−2 @ E−Ev = 0.36 eV is attained. After detailed analysis of the chemical structure of the HfO2/IL/Si-cap/Si0.7Ge0.3 using X-ray photoelectron spectroscopy (XPS), it is confirmed that the excellent HfO2/Si0.7Ge0.3 interface is realized by preventing the formation of Hf-silicate/Hf-germanate and Si oxide originating from the reaction between HfO2 and Si0.7Ge0.3 substrate.

The effect of As ion implantation on the stability of SiGe/Si multilayer was systematically studied. The atomic percentage of Ge in as-grown SiGe layer was 30% in this work. A thermally stable Si _0.7 Ge _0.3 /Si multilayer with As ion implantation was attained when the rapid thermal annealing (RTA) treatment temperature did not exceed 850 ^o C. Significant Ge diffusion was observed for the SiGe/Si multilayer with As ion implantation when the RTA temperature was 900 °C or above. However, minor Ge diffusion was attained for the SiGe/Si multilayer without As ion implantation when the RTA treatment temperature was 900 °C. Therefore, , compared with samples without As ion implantation, the stability window of the SiGe/Si multilayer with As ion implantation should be further reduced to 850 °C. As ion implantation plays a critical role in the stability of SiGe/Si multilayer, as it promotes the diffusion of Ge. Consequently, based on the stability of the SiGe/Si multilayer, the highest RTA treatment temperature of 850 °C is proposed for the gate-all-around (GAA) device fabrication process.

Fault diagnosis is critical to ensuring the safety and reliable operation of rotating machinery systems. Long short-term memory networks (LSTM) have received a great deal of attention in this field. Most of the LSTM-based fault diagnosis methods have too many parameters and calculation, resulting in large memory occupancy and high calculation delay. Thus, this paper proposes a low-delay lightweight recurrent neural network (LLRNN) model for mechanical fault diagnosis, based on a special LSTM cell structure with a forget gate. The input vibration signal is segmented into several shorter sub-signals in order to shorten the length of the time sequence. Then, these sub-signals are sent into the network directly and converted into the final diagnostic results without any manual participation. Compared with some existing methods, our experiments illustrate that the proposed method has less memory space occupancy and lower computational delay while maintaining the same level of accuracy.

To improve the fault diagnosis performance for rotating machinery, an efficient, noise-resistant end-to-end deep learning (DL) algorithm is proposed based on the advantages of the wavelet packet transform in vibration signal processing (the capability to extract multiscale information and more spectral distribution features) and deep convolutional neural networks (good classification performance, data-driven design and high transfer-learning ability). First, a vibration signal is subjected to pyramid wavelet packet decomposition, and each sub-band coefficient is used as the input for each channel of a deep convolutional network (DCN). Then, based on the lightweight modeling requirements and techniques, a new DCN structure is designed for the fault diagnosis. The proposed algorithm is compared with the support vector machine algorithm and the published DL algorithms based on a bearing dataset produced by Case Western Reserve University. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of accuracy, memory space, computational complexity, noise resistance, and transfer performance, producing good results.

This study describes H2O2-activated photosensitizer nanoparticles (ICyHD NPs), which inhibit histone deacetylase via binding Zn2+ to induce ferroptosis and upregulate the intracellular O2, thus resulting in enhanced photodynamic therapeutic effect. ICyHD NPs exert strong antitumor effects on mice and have improved the therapeutic precision via observing the increase in cellular fluorescence. [ABSTRACT FROM AUTHOR]