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Abstract The Eastern Mausoleum of Qin State is a significant component of the Qin Dynasty's royal tombs, reflecting the social development level during the Warring States period (475 BC ~ 221 BC) in China. At the onset of our investigation, we mapped the site's boundaries utilizing Corona satellite imagery and employed the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Moisture Index (NDMI) to identify archaeological features. Additionally, this study is the first to propose the use of thermal infrared band data from the SDGSAT-1 satellite to explore thermal archaeological traces, demonstrating their viability for archaeological site analysis. Subsequently, electromagnetic (EM) prospection was utilized to validate the presence of an ancient burial chamber passage. Landscape monitoring and analysis of the No.1 Mausoleum were performed using Corona and Google Earth images, revealing the accuracy of the Geomancy Theory of Chinese mausoleums through dynamic remote sensing of surface changes. Furthermore, our employment of space-to-ground observational modalities and resultant Digital Elevation Models (DEM) have been used to provided new insights into the application of Remote Sensing (RS) and Geomancy in archaeology, thereby emphasizing the pivotal role of site selection in heritage preservation. This research underscores the promise of synergistic space-ground observations in both exploiting the archaeological riches of cultural heritage sites and ensuring the enduring conservation of these irreplaceable patrimonial assets.

Abstract Weiyang Palace, the royal palace of the Western Han Dynasty, is a part of the Silk Roads: the Routes Network of Chang’an-Tianshan Corridor on the World Heritage list. The south palace wall of Weiyang Palace is a well-preserved earthen heritage site within the palace, but it is undergoing continuous deterioration due to the influence of vegetation and external environmental factors. This study pioneers the integration of high-resolution three-dimensional LiDAR scanning with multi-source data analysis, including unprecedented on-site botanical surveys, to explore the subtle effects of different vegetation types on the structural integrity of the south palace wall. Through contour line analysis and facade grid analysis, we extracted the deterioration locations of typical sections of the earthen heritage sites. And we classified the overlying vegetation types on the wall using an object-oriented classification algorithm. Our findings reveal a complex interaction between vegetation and earthen structures: paper mulberry exhibits protective qualities against erosion, while Ziziphus jujuba significantly exacerbates structural vulnerabilities. The methodologies applied in this study for extracting deterioration at earthen heritage sites and integrating multi-source spatial data can serve as a technical application model for monitoring and analyzing the driving forces of earthen heritage sites along the entire Silk Road network, thereby better guiding the conservation of earthen heritage sites.

As an effective water prevention and control technology to block or reduce groundwater infiltration, water cut-off curtain has been continuously applied to underground and open pit coal mines in recent years. Influenced by quality defects such as joints and interlayer, the cut-off curtain generally needs to evaluate the anti-seepage performance by calculating the permeability coefficient, which can directly determine the anti-seepage quality of the cut-off curtain. Taking Zhanihe Open-pit Coal Mine and Yuanbaoshan Open-pit Coal Mine and Zhuxianzhuang Coal Mine as examples, the seepage coefficient of the water cut-off curtain is calculated by five methods, including the well enclosure pumping test, the complete well pumping test, the water injection test of the precipitation head, the water pressure test of the borehole and the core permeability test. The results show that there are differences in the results obtained by various calculation methods, but they can be mutually verified; the pumping test, water injection test and core permeability test are mostly used to test the permeability coefficient of the cut-off curtain in open pit coal mines, while the borehole water pressure test is more suitable for testing the seepage resistance of the grouting curtain in underground coal mines. The five calculation methods can respectively calculate the permeability coefficient of the water cut-off curtain from four different angles of point, line, surface and body, and comprehensive inspection of curtain wall seams and pouring quality. The combination of multiple methods can more comprehensively verify the permeability coefficient of the cut-off curtain.

Abstract The freshness of aquatic products is directly related to the safety and health of the people. Traditional methods of detecting the freshness of Conger myriaster rely on manual operations, which are labor‐intensive, inefficient, and highly subjective. This paper combines computer vision and the DWG‐YOLOv8 network model to establish an intelligent classification method for C. myriaster freshness. Through image augmentation, 484 C. myriaster samples were expanded to 2904 samples. The YOLOv8n model was improved by simplifying the network backbone, introducing Ghost convolution and the new DW‐GhostConv, thereby reducing the number of parameters and computational load. Test results show that the recognition accuracy of the DWG‐YOLOv8 model reached 98.958%, outperforming models such as ResNet18, Mobilenetv3 small, and Swin transformer v2 tiny. The model's parameter count is 16.609 K, the inference time is 57.80 ms, and the model size is only 102 KB. The research provides a reliable method for online intelligent and nondestructive detection of C. myriaster freshness.

Abstract Background C-type lectins (CTLs) are a large family of proteins with sugar-binding activity. CTLs contain an evolutionarily conserved C-type lectin domain (CTLD) that binds microbial carbohydrates in a calcium-dependent manner, thereby playing a key role in both microbial pathogenesis and innate immune responses. Aedes albopictus is an important vector for transmitting dengue virus (DENV) worldwide. Currently, the molecular characteristics and functions of CTLs in Ae. albopictus are largely unknown. Methods Transcripts encoding CTL proteins in the Ae. albopictus genome assembly were analyzed via sequence blast. Phylogenetic analysis and molecular characterization were performed to identify the functional domains of the CTLs. Quantitative analysis was performed to determine the gene expression features of CTLs during mosquito development and in different tissues of female adults after blood feeding. In addition, the functional role of CTLs in response to DENV infection was investigated in Ae. albopictus mosquito cells. Results We identified 39 transcripts encoding CTL proteins in the Ae. albopictus transcriptome. Aedes albopictus CTLs are classified into three groups based on the number of CTLDs and the domain architecture. These included 29 CTL-Ss (single-CTLDs), 1 immulectins (dual-CTLD) and 9 CTL-Xs (CTLDs with other domains). Phylogenetic analysis and structural modeling indicated that CTLs in Ae. albopictus are highly conserved with the homologous CTLs in Aedes aegypti. The expression profile assay revealed differential expression patterns of CTLs in both developmental stages and in adult female tissues. Knockdown and overexpression of three CTLs (CTL-S12, S17 and S19) confirmed that they can promote dengue virus infection in Ae. albopictus cells. Conclusions The CTL genes in Ae. albopictus mosquito and other mosquito species are evolutionarily conserved and exhibit different developmental and tissue expression features. The functional assay indicated that three CTLs in Ae. albopictus mosquitoes are involved in promoting dengue virus infection. Our study revealed that CTLs play important roles in both the physiological processes and viral infection in mosquito vectors. Graphical Abstract

Streptomyces offer a wealth of naturally occurring compounds with diverse structures, many of which possess significant pharmaceutical values. However, new product exploration and increased yield of specific compounds in Streptomyces have been technically challenging due to their slow growth rate, complex culture conditions and intricate genetic backgrounds. In this study, we screened dozens of Streptomyces strains inhabiting in a plant rhizosphere for fast-growing candidates, and further employed CRISPR/Cas-based engineering techniques for stepwise refinement of a particular strain, Streptomyces sp. A-14 that harbors a 7.47 Mb genome. After strategic removal of nonessential genomic regions and most gene clusters, we reduced its genome size to 6.13 Mb, while preserving its growth rate to the greatest extent. We further demonstrated that cleaner metabolic background of this engineered strain was well suited for the expression and characterization of heterologous gene clusters, including the biosynthetic pathways of actinorhodin and polycyclic tetramate macrolactams. Moreover, this streamlined genome is anticipated to facilitate directing the metabolic flux towards the production of desired compounds and increasing their yields.

Abstract Wireless sensing of the wave propagation direction from radio sources lays the foundation for communication, radar, navigation, etc. However, the existing signal processing paradigm for the direction of arrival estimation requires the radio frequency electronic circuit to demodulate and sample the multichannel baseband signals followed by a complicated computing process, which places the fundamental limit on its sensing speed and energy efficiency. Here, we propose the super-resolution diffractive neural networks (S-DNN) to process electromagnetic (EM) waves directly for the DOA estimation at the speed of light. The multilayer meta-structures of S-DNN generate super-oscillatory angular responses in local angular regions that can perform the all-optical DOA estimation with angular resolutions beyond the diffraction limit. The spatial-temporal multiplexing of passive and reconfigurable S-DNNs is utilized to achieve high-resolution DOA estimation over a wide field of view. The S-DNN is validated for the DOA estimation of multiple radio sources over 5 GHz frequency bandwidth with estimation latency over two to four orders of magnitude lower than the state-of-the-art commercial devices in principle. The results achieve the angular resolution over an order of magnitude, experimentally demonstrated with four times, higher than diffraction-limited resolution. We also apply S-DNN’s edge computing capability, assisted by reconfigurable intelligent surfaces, for extremely low-latency integrated sensing and communication with low power consumption. Our work is a significant step towards utilizing photonic computing processors to facilitate various wireless sensing and communication tasks with advantages in both computing paradigms and performance over electronic computing.

Abstract In recent years, clear aligner can enhance individual appearance with dental defects, so it used more and more widely. However, in manufacturing process, there are still some problems, such as low degree of automation and high equipment cost. The problem of coordinate system mismatch between gingival curve point cloud and dental CAD model is faced to. The PCA-ICP registration algorithm is proposed, which includes coarse match algorithm and improve-ICP registration algorithm. The principal component analysis (PCA) based method can roughly find the posture relationship between the two point clouds. Using z-level dynamic hierarchical, the ICP registration can accurately find the posture between these two clouds. The final registration maximum distance error is 0.03 mm, which is smaller than robot machining error. Secondly, the clear aligner machining process is conducted to verify the registration effectiveness. Before machining, the path is generated based on the well registered gingival curve. After full registration, the tool path is calculated by establishing a local coordinate system between the workpiece and the tool to avoid interference. This path is calculated and generated as an executable program for ABB industrial robots. Finally, the robot was used for flexible cutting of clear aligners and was able to extract products, ensuring the effectiveness of the proposed research. This method can effectively solve the limitations of traditional milling path planning under such complex conditions.

The pH and firmness index (FI) of red globe grapes determine the taste and post-harvest quality of the fruit. In this paper, a method for the detection of pH and firmness of red globe grapes based on transmission spectroscopy technology was proposed. Spectral datas were first collected from 360 full-growth-cycle red globe grape samples, which were pre-processed and modeled by different spectral pre-processing methods as a way to determine the best spectral pre-processing method. Then competitive adaptive reweighted sampling (CARS), successive projection algorithm (SPA), uniformative variable elimination (UVE) and CARS-SPA, UVE-SPA composite data dimensionality reduction methods were used respectively for extracting feature variables from spectra. Finally, partial least squares regression (PLSR) detection models for pH and firmness of red globe grapes were established, respectively. The optimal prediction models for pH and firmness of red globe grape samples were moving-average method (MA)-CARS-SPA-PLSR and MA-UVE-SPA-PLSR. The correlation coefficient of prediction (RP) of the prediction sets of the two models were 0.9882 and 0.9588, and the residual predictive deviation (RPD) were 6.5857 and 3.5167, respectively. The results showed that transmission spectroscopy could be applied to the detection of pH and firmness of red globe grapes, which provided a new idea and a new method for the detection of pH and firmness of red globe grapes in the whole growth cycle.

Background: Guselkumab (human monoclonal antibody) selectively inhibits the interleukin (IL)-23p19 subunit. Objectives: Assess the longer-term pharmacodynamic effects of guselkumab and explore associations between such effects and clinical responses in patients with active psoriatic arthritis (PsA). Design: DISCOVER-2 randomized 739 biologic-naïve patients with active PsA (swollen/tender joint counts each ⩾5, C-reactive protein (CRP) ⩾0.6 mg/dL) to guselkumab (100 mg every 4 weeks (Q4W) or at Weeks 0, 4, and then Q8W) or placebo. Guselkumab-randomized participants with available serum biomarker data (randomly selected to reflect demographic and disease characteristics of the DISCOVER-2 population) comprised inflammatory ( N = 100) and collagen ( N = 178) biomarker cohorts. Methods: Pharmacodynamic effects of guselkumab through 2 years on inflammatory and collagen biomarker levels (general linear model) and associations between biomarkers and improvements in composite measures of joint, skin, and overall disease activity (Spearman linear regression) through 2 years were assessed. The relationship between the pharmacodynamic effects of guselkumab and achieving ⩾50% improvement in the American College of Rheumatology response criteria (ACR50) was assessed using a general linear model. Results: With guselkumab, pharmacodynamic effects on inflammatory (CRP, IL-6, serum amyloid A (SAA), IL-17A, IL-17F, IL-22, and beta-defensin 2 (BD-2)) and collagen (matrix metalloproteinase-degradation type I, III, IV, and VI collagen (C1M, C3M, C4M, and C6M)) biomarker levels were sustained or enhanced through Week 100. Throughout follow-up timepoints (Week 24/52/100), decreases in CRP, IL-6, C1M, and C6M levels correlated ( r = 0.26–0.30; p

To solve the problems of localization and identification of fish in the complex fishway environment, improving the accuracy of fish detection, this paper proposes an object detection algorithm YOLORG, and a fishway fish detection dataset (FFDD). The FFDD contains 4,591 images from the web and lab shots and labeled with the LabelIMG tool, covering fish in a wide range of complex scenarios. The YOLORG algorithm, based on YOLOv8, improves the traditional FPN–PAN network into a C2f Multi-scale feature fusion network with a Gather-and-Distribute mechanism, which solves the problem of information loss accompanied by the network in the fusion of feature maps of different sizes. Also, we propose a C2D Structural Re-parameterization module with a rich gradient flow and good performance to further improve the detection accuracy of the algorithm. The experimental results show that the YOLORG algorithm improves the mAP50 and mAP50-95 by 1.2 and 1.8% compared to the original network under the joint VOC dataset, and also performs very well in terms of accuracy compared to other state-of-the-art object detection algorithms, and is able to detect fish in very turbid environments after training on the FFDD. HIGHLIGHTS We propose an FFDD fish detection dataset.; We propose a Structural Re-parameterization convolution module C2D.; We propose a C2f Multi-scale feature fusion network to solve the problem of information loss in the YOLOv8 network.; We propose a YOLORG-series model constructed by C2D Structural Re-parameterization module and C2f Multi-scale feature fusion network.; The proposed method has fewer parameters.;

Background: Reactive oxygen species (ROS), predominantly generated by mitochondria, play a crucial role in the pathogenesis of intervertebral disc degeneration (IVDD). Reduction of ROS levels may be an effective strategy to delay IVDD. In this study, we assessed whether umbilical cord mesenchymal stem cell-exosomes (UCMSC-exos) can be used to treat IVDD by suppressing ROS production caused by mitochondrial dysfunction. Materials and methods: Human UCMSC-exos were isolated and identified. Nucleus pulposus cells (NPCs) were stimulated with H2O2 in the presence or absence of exosomes. Then, 4D label free quantitative (4D-LFQ) proteomics were used to analyze the differentially expressed (DE) proteins. Mitochondrial membrane potential (MMP), mitochondrial ROS and protein levels were determined via immunofluorescence staining, flow cytometry and western blotting respectively. Additionally, high-throughput sequencing was performed to identify the DE miRNAs in NPCs. Finally, therapeutic effects of UCMSC-exos were investigated in a puncture-induced IVDD rat model. Degenerative grades of rat IVDs were assessed using magnetic resonance imaging and histochemical staining. Results: UCMSC-exos effectively improved the viability of NPCs and restored the expression of the extracellular matrix (ECM) proteins, collagen type II alpha-1 (COL2A1) and matrix metalloproteinase-13 induced by H2O2. Additionally, UCMSC-exos not only reduced the total intracellular ROS and mitochondrial superoxide levels, but also increased MMP in pathological NPCs. 4D-LFQ proteomics and western blotting further revealed that UCMSC-exos up-regulated the levels of the mitochondrial protein, mitochondrial transcription factor A (TFAM), in H2O2-induced NPCs. High-throughput sequencing and qRT-PCR uncovered that UCMSC-exos down-regulated the levels of miR-194-5p, a potential negative regulator of TFAM, induced by H2O2. Finally, in vivo results showed that UCMSC-exos injection improved the histopathological structure and enhanced the expression levels of COL2A1 and TFAM in the rat IVDD model. Conclusions: Our findings suggest that UCMSC-exos promote ECM synthesis, relieve mitochondrial oxidative stress, and attenuate mitochondrial dysfunction in vitro and in vivo, thereby effectively treating IVDD. The translational potential of this article: This study provides solid experimental data support for the therapeutic effects of UCMSC-exos on IVDD, suggesting that UCMSC-exos will be a promising nanotherapy for IVDD.

In this study, the cloud macro-physical characteristics in Xiangyang of Hubei Province are statistically analyzed from the aspects of cloud occurrence frequency, number of cloud layers, and cloud base height by using the observation data of ceilometer from 2019 to 2021. The results are as follows. (1) The cloud coverage in Xiangyang is relatively high throughout the year, with an average cloud occurrence frequency of 61.3% in the three years. The frequency of cloud occurrence shows a seasonal variation of higher in summer but lower in winter, and a diurnal variation of higher level during nighttime but lower during daytime. (2) The cloud layer is dominated by single-layer clouds (78.3%), especially in winter (80.1%). In the multi-layer cloud system, two-layer clouds account for the largest proportion (18.5%). The occurrence frequency of multi-layer clouds decreases with the increase in the number of cloud layers, and it is usually high in summer but low in winter, which is mainly related to the relatively sufficient water vapor in summer and the strong convection caused by high temperatures. (3) For clouds with a cloud base height below 1.0 km, the occurrence frequency is 26% in January and 40% in July, indicating that the cloud distribution is relatively dispersed in January but relatively concentrated in July. However, for clouds with a cloud base height between 1.0 and 3.5 km, the occurrence frequency is 57% in January and 24% in July, that is, the cloud distribution is relatively concentrated in January, while relatively dispersed in July. (4) Convective precipitation clouds mainly occur from June to August, accounting for 83.3% of all convective precipitation clouds, and the probability of convective precipitation around the evening is the highest. In terms of cloud base height distribution, the average cloud base height of convective precipitation clouds is lower than that of all precipitation clouds, with a large occurrence frequency of low clouds.

Objective Hypertension refers to the elevated blood pressure (BP) in arteries, with a BP reading of 140/90 mm Hg or higher in adults. Over 40% of >25-year-old population have suffered from hypertension. Thus, this study aimed to find novel diagnostic biomarkers for hypertension.Methods All hypertension-related mRNA and methylation datasets were downloaded from the GEO database. Liner model method was used to identify differentially expressed genes (DEGs) between hypertension and control groups. Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes enrichment analysis was employed to obtain functional information. CpG sites and the corresponding genes associated with hypertension were screened using epigenome-wide association study (EWAS) analysis.Results There were 37 DEGs between the hypertension group and control group, which were significantly enriched in 84 Biological Process terms, 31 Cellular Component terms, 18 Molecular Function terms and 9 signalling pathways. EWAS results indicated that 1072 CpG sites were associated with hypertension occurrence, corresponding to 1029 genes. After cross-analysis, complement factor D (CFD) and OTU deubiquitinase, ubiquitin aldehyde binding 2 (OTUB2) with methylation modification were identified as diagnostic markers for hypertension.Conclusion In conclusion, CFD and OTUB2 were potential biomarkers of hypertension occurrence. Our results will provide more information for hypertension diagnosis and would be more reliable combined with multiple biomarkers.

Soluble solids content (SSC) and pH of red globe grapes are crucial measures of quality. In this paper, we used hyperspectral imaging technology to achieve nondestructive detection and distribution visualization of SSC and pH of red globe grapes. First, the hyperspectral images of samples were collected. Then, CARS, SPA, GA, IRIV were used to extract feature variables from raw spectral (RAW) information. The PLSR prediction models of samples were developed. By comparing the different prediction models, RAW-IRIV-PLSR was selected as the optimal model. Finally, the SSC and pH of the samples were calculated to obtain a grayscale image and perform a pseudo-color transformation to visualize the distribution of SSC and pH. By studying the classification of the maturity of samples, it was concluded that the best discriminant classification model of maturity was RAW-IRIV-ELM. Hyperspectral also provided a new method for maturity stage classification of red globe grapes.

Background: Despite the known reproductive toxicity induced by triptolide (TP) exposure, the regulatory mechanism underlying testicular vacuolization injury caused by TP remains largely obscure. Methods: Male mice were subjected to TP at doses of 15, 30, and 60 μg/kg for 35 consecutive days. Primary Sertoli cells were isolated from 20-day-old rat testes and exposed to TP at concentrations of 0, 40, 80, 160, 320, and 640 nM. A Biotin tracer assay was conducted to assess the integrity of the blood–testis barrier (BTB). Transepithelial electrical resistance (TER) assays were employed to investigate BTB function in primary Sertoli cells. Histological structures of the testes and epididymides were stained with hematoxylin and eosin (H&E). The expression and localization of relevant proteins or pathways were assessed through Western blotting or immunofluorescence staining. Results: TP exposure led to dose-dependent testicular injuries, characterized by a decreased organ coefficient, reduced sperm concentration, and the formation of vacuolization damage. Furthermore, TP exposure disrupted BTB integrity by reducing the expression levels of tight junction (TJ) proteins in the testes without affecting basal ectoplasmic specialization (basal ES) proteins. Through the TER assay, we identified that a TP concentration of 160 nM was optimal for elucidating BTB function in primary Sertoli cells, correlating with reductions in TJ protein expression. Moreover, TP exposure induced changes in the distribution of the BTB and cytoskeleton-associated proteins in primary Sertoli cells. By activating the AKT/mTOR signaling pathway, TP exposure disturbed the balance between mTORC1 and mTORC2, ultimately compromising BTB integrity in Sertoli cells. Conclusion: This investigation sheds light on the impacts of TP exposure on testes, elucidating the mechanism by which TP exposure leads to testicular vacuolization injury and offering valuable insights into comprehending the toxic effects of TP exposure on testes.

Water injection is a commonly used development method in oilfields. Water injection systems are large and complex, with horizontally and vertically interconnected pipeline networks often buried underground. Faults in pipeline networks cannot be detected and handled in time, thereby posing significant safety hazards to production. This study focuses on fault diagnosis and decision-making within a water injection system. We established a fault tree for an oilfield water injection system and proposed an optimized BP neural network with a Self-Adaptive Differential Evolution Algorithm for the first time. This method constructs a two-layer fault diagnosis model for a water injection system. The model diagnosed fault positions and types based on parameters such as the fault point flow, pressure, and pipeline flow. Compared with the traditional BP algorithm, this algorithm has better diagnostic accuracy and faster convergence speed. Simultaneously, the decision tree CART method was employed to classify decision types based on multiple parameter indicators of the fault points and generated decisions. We designed and implemented a fault diagnosis and decision platform for an oilfield water injection system. Finally we built an experimental pipeline network model with EPANET to simulate the system fault conditions. The diagnostic performance of the proposed algorithm was tested. The results showed that the proposed method achieved a 99% accuracy rate in diagnosing faults in a water injection system. This method significantly improves the scientific management of water injection systems, holding great potential for broad application and value in achieving smart oilfields.

[Introduction] With the extensive construction of wind farms, the combination of researches on different machine learning algorithms and meteorological forecasting modes has received widespread attention. [Method] This paper was based on the spatial distribution characteristics of wind energy resources in Hubei Province, and utilized representative stations in combination with experimental data analysis to conduct in-depth discussions on the results. [Result] The wind farms in operation and under construction in Hubei Province are all located in the "Three Zones and One Area", including the north-south wind zone from Jingmen to Jingzhou in the central part of Hubei Province, the east-west wind zone from Zaoyang to Yingshan in the north of Hubei Province, certain lake islands and zones along the lake, as well as some high mountainous areas in the southwest and southeast of Hubei Province. This research uses four different numerical forecasting products, namely CMA-WSP, CMA-GD, WHMM, and EC, to compare with the measured wind speeds and investigated the applicable range of these four numerical modes. [Conclusion] By analyzing the performance of five ensemble forecasting methods based on machine learning and the mean method, we identified suitable algorithm and forecasting model combinations, providing references for improving the accuracy of ensemble forecasting.

[Introduction] The volatility and intermittency of wind energy pose significant challenges for large-scale wind power integration. An effective approach to address this issue is to provide accurate wind power forecasting. [Method] In response to this challenge, this study proposed a wind power forecasting model for deep learning neural networks based on an improved LSTM (Long Short-Term Memory) architecture. The model incorporated a wind power forecasting approach that included independently developed data anomaly detection and processing, wind speed feature extraction and hyperparameter tuning. To enhance the neural network model's ability to accurately learn the impact of wind speed features on wind power, a feature engineering method combining feature screening and feature augmentation was also defined. [Result] The simulation results demonstrate that the proposed data cleaning and data augmentation algorithm can enhance the accuracy of various machine learning algorithms by approximately 5%. Furthermore, the proposed improved LSTM neural network model, after data cleaning, outperforms traditional algorithms and state-of-the-art neural network algorithms in the industry, achieving a 2.5% increase in accuracy. [Conclusion] The improved approach not only exhibits robust capability in cleaning noisy data but also consistently outperforms other algorithms in terms of forecasting accuracy across all experiments. This model provides valuable guidance for practical applications in the field of wind power forecasting.

Ocean weather prediction is crucial for various applications, such as global climate prediction, marine environmental protection, and offshore production. However, current data-based marine weather prediction methods have limitations when predicting multiple variables in a particular area, failing to meet the efficiency and accuracy requirements of practical applications. In the realm of ocean weather variations, the presence of highly interconnected spatial and temporal continuations, coupled with the mutual influence of individual variables, underscores the utmost importance of effectively capturing dynamic correlations encompassing space, time, and variables to accurately predict ocean weather. To address this, we developed a novel approach called StHCFormer, which is a multivariate spatiotemporal hybrid convolutional attention network. The first key component of StHCFormer is the spatiotemporal hybrid convolutional attention (StHCA) module, which leverages a hybrid convolutional attention mechanism to explore both global spatial representations and local features. Additionally, the module incorporates temporal attention to capture the temporal dependence of weather records and effectively captures the dynamic correlations among multiple variables through channel deflation and weighted residuals. To ensure balanced variable losses, we introduced the concept of homoscedasticity uncertainty loss to dynamically adjust the multitask weights. This guarantees a global optimal solution and leads to more accurate multivariate ocean weather prediction. Finally, we conducted a comprehensive evaluation and comparison of the StHCFormer model with other state-of-the-art algorithms using the ERA5 dataset in the Philippine Sea. The results demonstrated that StHCFormer outperforms existing methods in marine multivariate field weather prediction.

Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices.

Objectives To investigate the efficacy, safety, pharmacokinetics and pharmacodynamics of nipocalimab in participants with moderate to severe active rheumatoid arthritis (RA) and inadequate response or intolerance to ≥1 antitumour necrosis factor agent.Methods In this phase 2a study, participants with RA seropositive for anticitrullinated protein antibodies (ACPA) or rheumatoid factors were randomised 3:2 to nipocalimab (15 mg/kg intravenously every 2 weeks) or placebo from Weeks 0 to 10. Efficacy endpoints (primary endpoint: change from baseline in Disease Activity Score 28 using C reactive protein (DAS28-CRP) at Week 12) and patient-reported outcomes (PROs) were assessed through Week 12. Safety, pharmacokinetics and pharmacodynamics were assessed through Week 18.Results 53 participants were enrolled (nipocalimab/placebo, n=33/20). Although the primary endpoint did not reach statistical significance for nipocalimab versus placebo, a numerically higher change from baseline in DAS28-CRP at Week 12 was observed (least squares mean (95% CI): –1.03 (–1.66 to –0.40) vs –0.58 (–1.24 to 0.07)), with numerically higher improvements in all secondary efficacy outcomes and PROs. Serious adverse events were reported in three participants (burn infection, infusion-related reaction and deep vein thrombosis). Nipocalimab significantly and reversibly reduced serum immunoglobulin G, ACPA and circulating immune complex levels but not serum inflammatory markers, including CRP. ACPA reduction was associated with DAS28-CRP remission and 50% response rate in American College of Rheumatology (ACR) criteria; participants with a higher baseline ACPA had greater clinical improvement.Conclusions Despite not achieving statistical significance in the primary endpoint, nipocalimab showed consistent, numerical efficacy benefits in participants with moderate to severe active RA, with greater benefit observed for participants with a higher baseline ACPA.Trial registration number NCT04991753.

Total acid is an important indicator of the internal quality of red globe grapes. This paper proposed a non-destructive method for the determination of total acid of red globe grapes based on hyperspectral fusion technique. A non-linear LSSVM prediction model based on spectral information, image information and the fusion of the two was built respectively for total acid. The results showed that the MSC-CARS-SPA-LSSVM model of samples about total acid built by fusing the spectra after feature wavelength extraction using the MSC-CARS-SPA algorithm and the image information after dimensionality reduction by the PCA algorithm using the graph fusion technique worked best. The correlation coefficients of the prediction sets of the optimal LSSVM model was 0.9907, which improved the accuracy over the unilateral models based on spectral or image information. A new method for nondestructive detection of total acid of red globe grapes by map fusion technique was discovered.

Abstract The integrated photonic processor, co-packaged with electronic peripherals, is proposed for blind source separation of microwave signals, which separates signal-of-interest from dynamic interference with real-time adaptability.

BackgroundThe safety of drinking water is closely related to people's health. In recent years, relevant studies have identified some health related problems with drinking water in Inner Mongolia Autonomous Region. The complex and diverse natural environment embraced by the vast jurisdiction of the region may lead to uneven drinking water quality across the region. ObjectiveTo evaluate eight chemicals including arsenic, cadmium, chromium (hexavalent), lead, mercury, fluoride, trichloromethane, and carbon tetrachloride in urban drinking water in Inner Mongolia Autonomous Region in 2021, and to provide reference for optimizing urban water supply system and ideas for further developing strategies to promote population health. MethodsA total of 1228 monitoring sites were set up in urban areas of Inner Mongolia, and water samples were collected once in dry season (May) and once in wet season (August−September). Eight chemicals of interest in drinking water were detected according to the Standard examination methods for drinking water, and assessed for health risks using the health risk assessment model recommended by the United States Environmental Protection Agency (USEPA) and following the Technical guide for environmental health risk assessment of chemical exposure. Mann-Whitney U test was used to compare the concentrations of eight chemicals in urban drinking water by water seasons and water sample types. ResultsIn 2021, a total of 2381 samples of urban drinking water were tested in the Inner Mongolia Autonomous Region, including 1195 samples in wet season and 1186 samples in dry season; 389 samples of finished water and 1992 samples of tap water. The positive rates of arsenic and fluoride were 26.25% and 96.77%, respectively. The positive rates of cadmium, chromium (hexavalent), lead, mercury, trichloromethane, and carbon tetrachloride were 6.22%, 16.63%, 6.09%, 16.67%, 18.98%, and 8.36%, respectively. The exceeding standard rate of fluoride was 4.87%. Trichloromethane and carbon tetrachloride were qualified in all samples. There were statistical differences in the concentrations of arsenic, cadmium, chromium (hexavalent), lead, and carbon tetrachloride in urban drinking water between water seasons (Z=−3.847, P10−4, which suggested carcinogenic risks, while the carcinogenic risks of dermal explore to chemicals were all less than 10−6. ConclusionIn 2021, urban drinking water in Inner Mongolia Autonomous Region is generally safe, but arsenic, cadmium, chromium (hexavalent), lead, mercury, and fluoride still exceed the national limits, posing certain health risks.

Objective To evaluate gene expression in blood of patients with psoriatic arthritis (PsA) versus healthy controls and identify changes associated with guselkumab treatment. Methods Whole blood transcriptome profiling via paired‐end RNA sequencing was conducted using samples from DISCOVER‐1 and DISCOVER‐2 at baseline (n = 673) and at weeks 4 and 24 from a representative subgroup that received placebo or guselkumab (n = 227 [longitudinal PsA cohort]). Baseline samples were compared with demographically matched healthy controls (n = 21). Guselkumab‐mediated changes in gene expression were assessed in participants from the longitudinal PsA cohort who did versus did not achieve at least 20% improvement in American College of Rheumatology response criteria (ACR20) or at least 75% improvement in Psoriasis Area and Severity Index (PASI75). Differential gene expression was analyzed using edgeR. Results At baseline, 355 upregulated and 314 downregulated genes (PsA‐associated genes) were identified in patients with PsA versus healthy controls. Upregulated genes were related to neutrophil, mononuclear cell, and CD11b+ gene sets. No cell type–specific gene sets were identified among downregulated genes. Most PsA‐associated genes were modulated by guselkumab treatment. At week 24, genes downregulated by guselkumab were enriched with neutrophil, monocyte, eosinophil, and macrophage gene sets; genes upregulated by guselkumab were enriched with B cell, T cell, and natural killer cell gene sets. Reductions in expression of upregulated PsA‐associated gene sets were more pronounced in ACR20 and PASI75 responders than in nonresponders. Conclusion These findings suggest a dysregulation of immune cell profiles in blood from patients in the baseline PsA cohort that approached levels in healthy controls after guselkumab treatment.

In recent years, square hollow section (SHS) joints with concrete-filled square hollow section (CFSHS) chords have increasingly been used in truss bridges where the fatigue life reliability is a critical issue. In this paper, a finite element analysis was performed to investigate the SCFs in SHS-CFSHS T-joints under in-plane bending in the brace, axial force in the chord and in-plane bending in the chord. The finite element models were developed and validated with experimental results. Then a parametric study was conducted with a reasonable range of three key non-dimensional parameters, i.e., β (width ratio between brace and chord), 2γ (width–to–wall thickness ratio of the chord) and τ (wall thickness ratio between brace and chord). Consequently, the stress concentration factor formulae for the fatigue design of SHS-CFSHS T-joints were proposed through multiple regression analysis. For in-plane bending in the brace, the maximum stress concentration factors were found at lines B and C for thick-walled chords (2γ = 25.0), while the stress concentration factors at all the lines need to be checked for thin-walled chords (2γ < 16.0). Under axial force in the chord and in-plane bending in the chord, only stress concentration factors at lines C and D needed to be considered. A comparison of stress concentration factors between SHS-SHS and SHS-CFSHS joints showed reductions of 10~26% and 14~31% in stress concentration factors in SHS-CFSHS joints under axial force in the brace and in-plane bending in the brace, respectively. In addition, it showed a general increase in stress concentration factors in SHS-CFSHS joints under axial force and in-plane bending in the chord. This reduction is attributed to the reduction in chord face deformation benefiting from the in-filled concrete. Meanwhile, the stress concentration factors caused by loads in the chord are much lower than those caused by loads in the brace. This work complements earlier studies on SHS-CFSHS T-joints under axial force.

Abstract Background Guselkumab is a selective interleukin (IL)-23 inhibitor targeting the IL-23p19 subunit. In the phase 3b COSMOS trial, guselkumab demonstrated efficacy in treating participants with active psoriatic arthritis (PsA) and inadequate response (IR; lack of efficacy or intolerance) to tumor necrosis factor inhibitors (TNFi). Methods Adults with active PsA (≥ 3 swollen joints, ≥ 3 tender joints) and IR to one or two TNFi (TNFi-IR) were randomized 2:1 to guselkumab at Weeks 0, 4, then every 8 weeks (Q8W) or placebo➔guselkumab Q8W at Week 24 with possible early escape at Week 16. Levels of serum cytokines, including interferon γ (IFNγ), IL-10, and tumor necrosis factor α (TNFα); T helper 17 (Th17) effector cytokines IL-17A, IL-17F, and IL-22; and acute phase proteins C-reactive protein (CRP), IL-6, and serum amyloid A (SAA), were assessed and compared with demographically matched healthy controls; guselkumab pharmacodynamics through Week 24 were also assessed. Associations between baseline biomarker levels and 1) baseline disease activity (28-joint disease activity score using CRP [DAS28-CRP], psoriasis area and severity index [PASI], and % body surface area [BSA] affected by psoriasis) and 2) clinical response (including ≥ 20% improvement in American College of Rheumatology criteria [ACR20] response) at Week 24 were assessed. Results Baseline serum levels of IL-6, IL-10, IL-17A, IL-17F, IL-22, TNFα, and IFNγ were significantly higher in COSMOS TNFi-IR participants than in healthy controls. Baseline IL-6, CRP, and SAA levels were associated with baseline DAS28-CRP. IL-17A and IL-17F levels were associated with baseline PASI score and psoriasis BSA. Baseline swollen or tender joint counts did not associate with baseline biomarker levels. At Week 24, significant decreases from baseline in CRP, SAA, IL-17A, IL-17F, and IL-22 levels were seen in guselkumab-, but not placebo-, treated participants. IL-17F and IL-22 levels at Week 24 in guselkumab-treated participants did not significantly differ from those of healthy controls. Guselkumab-treated participants achieving ACR20 response at Week 24 exhibited higher baseline IL-22 and IFNγ levels versus nonresponders. Conclusions Results from COSMOS participants with active, TNFi-IR PsA suggest guselkumab reduces levels of effector cytokines associated with the IL-23/IL-17 pathway, including those associated with baseline arthritis and skin disease activity. Trial registration ClinicalTrials.gov: NCT03796858.

The malicious attacks of Byzantine nodes in federated learning was studied over the non-independent and identically distributed dataset , and a privacy protection robust gradient aggregation algorithm was proposed.A reference gradient was designed to identify “poor quality” shared gradients in model training, and the influence of heterogeneity data on Byzantine node recognition was reduced by reputation evaluation.Meanwhile, the combination of homomorphic encryption and random noise obfuscation technology was introduced to protect user privacy in the process of model training and Byzantine node recognition.Finally, through the evaluation over the real-world datasets, the simulation results show that the proposed algorithm can accurately and efficiently identify Byzantine attack nodes while protecting user privacy and has good convergence and robustness.

Purpose. To evaluate the efficacy of spironolactone in the treatment of chronic central serous chorioretinopathy (CSC) and identify imaging characteristics that can predict the benefit of spironolactone treatment. Methods. Patients with chronic CSC were treated with spironolactone (20 mg/tid) and followed for 6 months. The primary outcome measure was complete resolution of the subretinal fluid (SRF), and the best-corrected visual acuity (BCVA), the SRF area, the central macular thickness (CMT), the subfoveal choroidal thickness (SFCT), and the density of the choriocapillaris vessel and adverse events were secondary outcome measures. Patients who presented complete resolution of SRF were included in the responder group and the other patients who had moderate or no resolution were included in the nonresponder group. Imaging characteristic comparisons between the responder and nonresponder groups were performed with regression analyses to identify factors that are predictive of a good response to treatment. Results. Forty-two eyes of 42 patients with a mean age of 46.06 ± 6.66 years were included. A total of 57.1% of the patients achieved a complete resolution of SRF. The mean SRF area, CMT, and SFCT decreased significantly (all P0.05). The vascular density of the choriocapillaris of the fellow eyes did not vary significantly during treatment. Logistic regression analysis revealed that SFCT (P=0.002) and the intact ellipsoid zone (P=0.001) were correlated with disease resolution. A relatively higher baseline SFCT was a predictive factor associated with a good response to treatment according to multivariate analysis. Conclusions. This study suggested that oral spironolactone could be an effective and safe therapy for chronic CSC patients. Eyes with a higher baseline SFCT and intact ellipsoid zone could have a good response. These parameters are an important prognostic marker.

ABSTRACTMicrobial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.

ABSTRACTDysbiosis of gut microbial community is associated with the pathogenesis of CD and may serve as a promising noninvasive diagnostic tool. We aimed to compare the performances of the microbial markers of different biological levels by conducting a multidimensional analysis on the microbial metagenomes of CD. We collected fecal metagenomic datasets generated from eight cohorts that altogether include 870 CD patients and 548 healthy controls. Microbial alterations in CD patients were assessed at multidimensional levels including species, gene, and SNV level, and then diagnostic models were constructed using artificial intelligence algorithm. A total of 227 species, 1047 microbial genes, and 21,877 microbial SNVs were identified that differed between CD and controls. The species, gene, and SNV models achieved an average AUC of 0.97, 0.95, and 0.77, respectively. Notably, the gene model exhibited superior diagnostic capability, achieving an average AUC of 0.89 and 0.91 for internal and external validations, respectively. Moreover, the gene model was specific for CD against other microbiome-related diseases. Furthermore, we found that phosphotransferase system (PTS) contributed substantially to the diagnostic capability of the gene model. The outstanding performance of PTS was mainly explained by genes celB and manY, which demonstrated high predictabilities for CD with metagenomic datasets and was validated in an independent cohort by qRT-PCR analysis. Our global metagenomic analysis unravels the multidimensional alterations of the microbial communities in CD and identifies microbial genes as robust diagnostic biomarkers across geographically and culturally distinct cohorts.

Type II deiodinase (DIO2) is thought to provide triiodothyronine (T3) to the nucleus to meet intracellular needs by deiodinating the prohormone thyroxine. DIO2 is expressed widely in many tissues and plays an important role in a variety of physiological processes, such as controlling T3 content in developing tissues (e.g., bone, muscles, and skin) and the adult brain, and regulating adaptive thermogenesis in brown adipose tissue (BAT). However, the identification and cloning of DIO2 have been challenging. In recent years, several clinical investigations have focused on the Thr92Ala polymorphism, which is closely correlated with clinical syndromes such as type 2 diabetes, obesity, hypertension, and osteoarthritis. Thr92Ala-DIO2 was also found to be related to bone and neurodegenerative diseases and tumors. However, relatively few reviews have synthesized research on individual deiodinases, especially DIO2, in the past 5 years. This review summarizes current knowledge regarding the physiological functions of DIO2 in thyroid hormone signaling and adaptive thermogenesis in BAT and the brain, as well as the associations between Thr92Ala-DIO2 and bone and neurodegenerative diseases and tumors. This discussion is expected to provide insights into the physiological functions of DIO2 and the clinical syndromes associated with Thr92Ala-DIO2.

Abstract Background Aberrant epigenetic remodeling events contribute to progression and metastasis of breast cancer (Bca). The specific mechanims that epigenetic factors rely on to mediate tumor aggressiveness remain unclear. We aimed to elucidate the roles of epigenetic protein PHF6 in breast tumorigenesis. Methods Published datasets and tissue samples with PHF6 staining were used to investigate the clinical relevance of PHF6 in Bca. CCK-8, clony formation assays were used to assess cell growth capacity. Cell migration and invasion abilities were measured by Transwell assay. The gene mRNA and protein levels were measured by quantitative real-time PCR and western blot. Chromatin immunoprecipitation (ChIP)-qPCR assays were used to investigate transcriptional relationships among genes. The Co-immunoprecipitation (Co-IP) assay was used to validate interactions between proteins. The CRISPR/Cas9 editing technology was used to construct double HIF knockout (HIF-DKO) cells. The subcutaneous xenograft model and orthotopic implantation tumor model were used to asess in vivo tumor growth. Results In this study, we utilized MTT assay to screen that PHF6 is required for Bca growth. PHF6 promotes Bca proliferation and migration. By analyzing The Cancer Genome Atlas breast cancer (TCGA-Bca) cohort, we found that PHF6 was significantly higher in tumor versus normal tissues. Mechanistically, PHF6 physically interacts with HIF-1α and HIF-2α to potentiate HIF-driven transcriptional events to initiate breast tumorigenesis. HIF-DKO abolished PHF6-mediated breast tumor growth, and PHF6 deficiency in turn impaired HIF transcriptional effects. Besides, hypoxia could also rely on YAP activation, but not HIF, to sustain PHF6 expressions in Bca cells. In addition, PHF6 recuits BPTF to mediate epigenetic remodeling to augment HIF transcriptional activity. Targeting PHF6 or BPTF inhibitor (AU1) is effective in mice models. Lastly, PHF6 correlated with HIF target gene expression in human breast tumors, which is an independent prognostic regulator. Conclusions Collectively, this study identified PHF6 as a prognostic epigenetic regulator for Bca, functioning as a HIF coactivator. The fundamental mechanisms underlying YAP/PHF6/HIF axis in breast tumors endowed novel epigenegtic targets for Bca treatment.

Environmental pollution caused by marine engines fueled with fossil fuels is a matter of growing significance. The search for renewable and clean energy sources and improvements in the way fossil fuels are burnt aims to reduce the environmental impact of these engines. For this purpose, fatty acid methyl esters were produced from pure canola oil using KOH-assisted methanol-based transesterification with a maximum yield of 90.68 ± 1.6%. The marine engine’s model was created with CONVERGE software, followed by experimental verification. This paper examines the blended fuel characteristics of a diesel engine with biodiesel blends (0%, 5%, 10%, and 15%) at different loads of engines (50%, 75%, and 100%). It also explores the variation in these characteristics of B10 (10% biodiesel–diesel blends) at three different load conditions and four different EGR rates (0%, 5%, 10%, and 15%). The results indicate that the addition of biodiesel to diesel fuel reduces CO, HC, and soot emissions, while increasing NOx emissions. Additionally, the EGR rate decreases NOx emissions but results in higher levels of soot, CO, and HC emissions. Finally, response surface methodology was used to elicit the engine’s characteristics. It was determined that the optimum experimental operating conditions were 100% engine load, 6.9% biodiesel addition, and 7.7% EGR. The corresponding BTE, BSFC, NOx, and HC emissions were 38.15%, 282.62 g/(kW-h), 274.38 ppm, and 410.37 ppm, respectively.

The engine casing components operate in high-temperature and high-pressure environments. Process holes are drilled when defects occur. Welding is employed in the repair of process holes as a process for permanently joining materials. The traditional welding method relies on padding, which results in poor back formation of process holes. Additionally, the shape of the process holes imposes high requirements on the size of the droplet transition. The conventional approach of adjusting a welding current makes it difficult to achieve stable droplet transition and precise formation of small holes. It poses a challenge for the robotic welding process. To deal with this problem, the influence of the high-frequency vibration GTAW process on the directional transition of molten droplets is studied. The molten droplet directional transition process is developed. The impact of vibration energy on the molten pool is reduced. Welding repair experiments for process holes are successfully conducted. When the frequency is 3 Hz, the transition of droplets changes from a continuous one-droplet transition to a discontinuous liquid bridge transition. The residual height and mechanical properties of the repaired area are tested. The experimental results indicated that the residual height after dual-side repair is ≤0.7 mm. The X-ray and fluorescent penetration tests have a 100% first-pass qualification rate. The repaired area demonstrates a hardness of 480 HV and a room-temperature tensile strength of 1069 MPa. The repair process requirements for the casing are met.

The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals (SDGs) that form part of the United Nations 2030 Sustainable Development Agenda. In terms of anthropogenic factors threatening the conservation of heritage properties, such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability. Therefore, in this study, 589 cultural World Heritage List (WHL) properties from 115 countries were analyzed, encompassing globally distributed and statistically significant samples of “monuments and groups of buildings” (73.2%), “sites” (19.3%), and “cultural landscapes” (7.5%). Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification. Sustainability indexes (SIs) were estimated for the protection zones of each property, and the results were employed, for the first time, to assess the progress of each country toward SDG Target 11.4. Despite the apparent advances in SIs (10.4%), most countries either exhibited steady (20.0%) or declining (69.6%) SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances. This study confirms that land-cover changes are among serious threats for heritage conservation, with heritage in some countries wherein the need to address this threat is most crucial, and the proposed spatiotemporal monitoring approach is recommended.

The internal quality indicators such as soluble solids content (SSC), total acid (TA), pH, firmness index (FI) and moisture content (MC) of red globe grapes affect the taste and quality of the fruit directly. And they are also criterion for maturity. In order to obtain the internal quality indicators showed above quickly and avoid unnecessary inspection losses, a new non-destructive detection model for red globe grapes was proposed in this paper. The near-infrared spectral information of 360 samples was collected using the Antaris II near-infrared spectrometer for red globe grapes in the growing period. The collected spectral information was pre-processed by algorithms such as SNV and then modelled to determine the optimal spectral pre-processing method. The characteristic wavelengths of the spectral information were extracted by dimensionality reduction algorithms. Finally, the detection models for SSC, TA, pH, FI and MC of red grapes were established respectively based on Partial Least Squares Regression (PLSR) algorithm. For SSC and TA the optimal detection model was SG-CARS-SPA-PLSR, for pH the optimal detection model was MA-CARS-SPA-PLSR, and for FI and MC the optimal detection model was SG-CARS-PLSR. The correlation coefficients (Rp) of the optimal PLSR models established of the prediction sets for red globe grape SSC, TA, pH, FI and MC were 0.9787, 0.9811, 0.9870, 0.9568 and 0.9329 respectively, and the residual prediction deviations(RPD) were 4.8637, 4.9006, 6.0939, 3.4453 and 2.5825 respectively, indicating that the above models had high detection accuracy. The models established in this paper would provide a reliable method for the detection of the internal quality of red globe grapes.ity of red globe grapes.

With the increasing contribution of the marine economy to the national economy, it is of great significance to carry out the assessment and analysis of the high-quality development of the marine economy to promote the construction of a marine power. Based on the "six high-quality" framework of Jiangsu Province, this paper constructed a high-quality development evaluation index system of marine economy. Based on the weight of coefficient of variation method and entropy weight, TOPSIS proximity was calculated respectively, and the high-quality development of Jiangsu marine economy in 2015-2019 was compared and analyzed. The research found that: the dynamic trend and ranking of the quality of marine economic development calculated by the two weights were 2019>2015>2018>2017>2016. The remarkable optimization in 2019 was mainly due to the fact that the throughput of foreign trade goods of coastal and riverside ports, the environmental quality of coastal waters, the retention rate of natural shorelines and the per capita net income of fishermen were significantly better than the other evaluation years. Finally, some suggestions were put forward to promote the high-quality development of marine economy.

Monocyte chemotactic protein-1-induced protein-1 (MCPIP-1) is highly expressed in activated immune cells and negatively regulates immune responses, while the mechanisms underlying the immunoregulation of neutrophils in acute bacterial infection and liver injury remain elusive. Here, we examined the role of MCPIP-1 in regulating neutrophil functions during acute bacterial peritonitis and liver injury. Mice with myeloid cell-specific overexpression (McpipMye-tg) or knockout (McpipΔMye) of MCPIP-1 were generated. We found that reactive oxygen species and myeloperoxidase production, formation of neutrophil extracellular traps, and migratory capacity were deficient in McpipMye-tg neutrophils but enhanced in McpipΔMye neutrophils. The recruitment of neutrophils and pathogen clearance were markedly suppressed in McpipMye-tg mice following intraperitoneal infection with Salmonella typhimurium while intensified in McpipΔMye mice. Severe acute S. typhimurium-infected peritonitis and liver injury occurred in McpipMye-tg mice but were alleviated in McpipΔMye mice. RNA sequencing, RNA-binding protein immunoprecipitation and qPCR analysis revealed that MCPIP-1 downregulated the protective functions of neutrophils via degrading the mRNA of cold inducible RNA-binding protein. Consistently, MCPIP-1 was highly expressed in neutrophils of patients with acute infectious diseases, especially in those with liver injury. Collectively, we uncover that MCPIP-1 negatively regulates the antibacterial capacities of neutrophils, leading to exacerbating severe acute bacterial peritonitis and liver injury. It may serve as a candidate target for maintaining neutrophil homeostasis to control acute infectious diseases.

Based on the analysis technology of visible/near infrared diffuse reflectance spectroscopy a portable and nondestructive detector was designed to test comprehensive quality of red globe grape bunches in the growth period. The detector included spectrum acquisition probe, spectrometer, lithium battery, halogen lamp light source, advanced RISC machines (ARM) board and peripheral circuit. Based on microsoft foundation classes (MFC) development tool, the real-time analysis and processing software of the detector was written by C++ language. The optimal partial least squares regression (PLSR) detection model of multi-quality parameters was implanted into the hardware device. This paper selected the red globe grapes bunches in the growth period as the research samples, collected the visible/near infrared diffuse reflectance spectrum information, and then used the established PLSR model to detect the soluble solid content (SSC), total acid (TA) and pH of the samples to generate comprehensive quality parameter. So as to realize the nondestructive detecting of comprehensive quality of red globe grapes bunches in the growth period. In conclusion, the detector could realize real-time and non-destructive detecting of red globe grapes bunches in growth period aiming at the comprehensive quality.

Abstract This study investigates the problem of fault diagnosis observer design in the finite frequency domain for discrete‐time Lipschitz nonlinear systems simultaneously subjected to actuator and sensor faults and unknown input disturbances. An augmented descriptor system is developed by constructing an augmented state consisting of system states and sensor faults. A novel H−/L∞ unknown input observer (UIO) is proposed in the finite frequency domain using the generalized Kalman–Yakubovich–Popov lemma to render the residual sensitive to actuator faults and robust against disturbances, which can achieve a better fault diagnosis result compared with existing observers in the full frequency domain. In the fault diagnosis scheme, a time‐varying threshold is presented based on the L∞ performance index to detect actuator faults, and the sensor fault estimation is simultaneously achieved through an asymptotical estimation of the augmented state. Design conditions for the proposed H−/L∞ UIO are derived and converted into linear matrix inequalities to solve its design matrices together. The effectiveness of the developed H−/L∞ UIO and the fault diagnosis scheme are validated via simulations of an example of a single‐link flexible joint robot.

To solve the problem of unmanned plugging weld for the steam generator in high temperature gas cooled reactor, a new seven-degrees-of-freedom robot with 3-PSP and SCARA structure is designed based on the scheme of serial-parallel robot. In order to achieve simple and stable motion control, the D-H method and analytical method are used to study its serial-parallel kinematics and establish an accurate kinematics model. Based on MATLAB software and existing kinematic models, the Monte Carlo method is introduced to analyze the position and posture accessibility of the robot which has redundant degrees of freedom, and the rationality of the institutional design is verified. To ensure the high precision performance of the robot, the positional accuracy of the prototype is tested and analyzed under the different operating conditions by means of the standardized robot performance tests. In addition, in order to ensure the quality of plugging weld, through the T22 base material for several groups of TIG welding process experiments and mechanical properties testing to determine the reliable plugging welding process parameters. The results show that the robot can meet the requirements of plugging welding, and has accurate motion control, good reachability, high position and posture accuracy and strong expandability.

As the cornerstone of blockchain technology, consensus technology has received more attention with the continuous development of blockchain technology.The development of consensus technology has become more and more rapid, but there are still related problems.Nowadays, fault-tolerant consensus algorithms, as one of the representative blockchain consensus technologies, still have many problems to be studied.The problem of node randomness and node collusion attacks in fault-tolerant consensus algorithms had been studied, and a game-theoretic-based anti-corruption algorithm was proposed.The global randomization consensus algorithm of collusion attack improved the security and throughput of the blockchain network by realizing the randomization of nodes and solving related security problems.In the process of selecting nodes participating in the fault-tolerant consensus algorithm, the global randomization of the initiator and verifier nodes was realized by using the mapping function and the weighted random function, thereby ensuring the identity anonymity of the initiator and verifier nodes and improving the blockchain network security accordingly.The reputation update model was used to realize the dynamic update of the reputation, and the game theory was used to analyze the security problems of the fault-tolerant consensus algorithm.A more correct and efficient algorithm model was constructed to improve the throughput of the algorithm and analyze the problem of collusion attack of more than one third of the nodes in this kind of algorithm, the refined Bayesian game was used to construct a collusion contract and analyze the collusion The Nash equilibrium point between the two nodes was adopted to solve the collusion attack problem of more than one third of the nodes.The security analysis and experiments show that the global randomization consensus algorithm based on the game theory anti-collusion attack is better than PoW、PoS and PBFT.The consensus algorithm is not only effective to improve throughput and reduce computing resource consumption, but also resistant to DDoS, Eclipse attacks and collusion attacks by more than one third of nodes.

Abstract The main‐lobe jamming causes serious degradation of radar system performance in practical applications. In recent years, the Blind Source Separation (BSS) method has been applied to suppress the main‐lobe jamming. However, the received echoes of target and jamming cannot be effectively identified while the robustness of the conventional BSS method is poor due to the influence of system noise. To tackle these problems, a radar main‐lobe jamming suppression and identification method is proposed based on robust whitening BSS and Convolutional Neural Networks methods. First, the target echoes and jamming are separated by the proposed robust whitening BSS method. An iterative optimisation method is proposed to obtain the robust whitening matrix, and sufficient conditions are provided for solvability. Then, a Target echo and Jamming Identification Network (TJINet) is proposed to identify the signals after the robust whitening BSS. The proposed TJINet method uses residual blocks and branch structures to improve classification performance and utilises asymmetric convolution blocks to extract subtle features between the target echo and deceptive jamming. Simulation and experimental results show that the proposed method has better jamming suppression performance than the traditional methods under low signal‐to‐noise ratio (SNR) conditions, the algorithm converges faster, and also can stably identify the target echo and jamming signals.