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Abstract Underwater friction stir welding (UFSW) achieves reliable joining between dissimilar materials and meets the welding demand for function and properties in lightweight structures of modern engineering. A defect monitoring method based on Variational Mode Decomposition optimized by Beluga Whale Optimization and Hilbert–Huang Transform (BWO–VMD–HHT) is proposed to solve the unclear feature of AE signal in UFSW due to the aqueous medium. UFSW experiments on Al alloy and carbon fiber reinforced thermoplastic (CFRTP) are carried out with AE signals measured. The time–frequency domain features of AE signals are extracted by BWO–VMD–HHT. The experimental results show that the main frequency of the AE signal is 22.5 kHz, and surface crack defects, shallow hole defects, and deep hole defects are accompanied by the transfer phenomena of different frequency components. Then, the feature vectors are built by frequency components in the BWO–VMD–HHT spectrum and reduced by principal component analysis, including 22.5 kHz, 24 kHz, 20.6 kHz, 18.4 kHz, 17.3 kHz, and 15.6 kHz. The feature vectors are divided into the train and test sets, and the welding defect prediction model (ResNet18-attention) is built by ResNet18 and trained by feature vectors. In the test set, the ResNet18-attention is compared with the BP, SVM, and RBF. Test results show that the precision of models has improved by at least 10%, which are trained by BWO–VMD–HHT features vector. Also, ResNet18-attention has achieved an average precision of 0.906 and recognizes the category of weld defect accurately, and this method can be applied to the defect monitoring of UFSW.

ObjectiveSeptic cardiomyopathy (SCM), a prevalent and critical condition in individuals suffering from sepsis and septic shock, remains elusive in terms of its intricate pathogenesis, thereby lacking definitive diagnostic standards. Current clinical management predominantly revolves around addressing the underlying disease and alleviating symptoms, yet mortality rates persist at elevated levels. This research endeavors to delve into the effects of low molecular weight heparin on Heparanase (HPA) levels in SCM patients, while assessing the clinical significance of HPA as a diagnostic marker in this patient population.MethodA comprehensive cohort of 105 patients diagnosed with SCM was recruited from the Department of Critical Care Medicine at the First Hospital of Lanzhou University, spanning the period from September 2022 to October 2023, serving as the primary research subjects for this investigation. A prospective, randomized controlled trial was undertaken, wherein 53 SCM patients were randomly allocated to a control group receiving standard therapy, while 52 patients were randomly assigned to an intervention group receiving conventional treatment augmented with low molecular weight heparin (LMWH). On the 1st, 3rd, and 7th days post-treatment, the following parameters were measured and documented: HPA levels, syndecan-1 levels, IL-6, TNF-α, CD4+/CD8+ cell ratio, anti-Xa factor, antithrombin III (AT-III) levels, left ventricular ejection fraction (LVEF), fractional shortening (FS), E/e’ ratio, stroke volume (SV), cardiac performance index (CPI), global end-diastolic volume index (GEDVI), N-terminal pro-brain natriuretic peptide (NT-proBNP), cardiac troponin I (CTnI), heart-type fatty acid-binding protein (H-FABP), lactate (Lac) levels, central venous oxygen saturation (ScvO2), Sequential Organ Failure Assessment (SOFA) score, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, ICU length of stay, and 28-day mortality rate.ResultsIn comparison to the control group, the LMWH group demonstrated significantly lower levels of HPA and syndecan-1 (p

Recently, deep learning-based methods for synthetic aperture radar (SAR) ship detection have made remarkable advancements. However, most existing methods primarily focus on achieving high detection accuracy by employing complex models, leading to an increase in computational costs. In addition, some methods do not adequately consider the impact of speckle noise interference. To address these challenges, we propose a multiscale ship detector, called visual saliency-lightweight ship detector (VS-LSDet), utilizing visual saliency and lightweight convolutional neural network. First, a visual saliency enhancement module is proposed as a preprocessing step to visually highlight the ships and weaken the impact of speckle noise in the image. Second, a lightweight backbone called ghost-shuffle net (GSNet) is designed. We introduce two types of ghost-shuffle blocks as basic convolution blocks by introducing ghost convolution to reduce the model complexity and channel shuffle operation to enhance the representation ability of the feature map. Then, we propose a multishape dilated convolution block incorporated into GSNet to enlarge its receptive fields, further improving the detector's performance. Finally, a hybrid attention module (HyAM) is proposed, it leverages both spatial and channel information within the feature map. HyAM can emphasize ship-related features while suppressing irrelevant features from the background in the feature map. Experimental results on public SAR ship datasets demonstrate that, compared to other ship detection methods, VS-LSDet achieves higher detection accuracy with lower model complexity. Specifically, on the SSDD dataset, the AP value of VS-LSDet is 97.51%, with 2.53 M parameters and 6.21 GFLOPs.

Wind turbine blades (WTBs) are prone to damage from their working environment, including surface peeling and cracks. Early and effective detection of surface defects on WTBs can avoid complex and costly repairs and serious safety hazards. Traditional object detection methods have disadvantages of insufficient detection capabilities, extended model inference times, low recognition accuracy for small objects, and elongated strip defects within WTB datasets. In light of these challenges, a novel model named DCW-YOLO for surface damage detection of WTBs is proposed in this research, which leverages image data collected by unmanned aerial vehicles (UAVs) and the YOLOv8 algorithm for image analysis. Firstly, Dynamic Separable Convolution (DSConv) is introduced into the C2f module of YOLOv8, allowing the model to more effectively focus on the geometric structural details associated with damage on WTBs. Secondly, the upsampling method is replaced with the content-aware reassembly of features (CARAFE), which significantly minimizes the degradation of image characteristics throughout the upsampling process and boosts the network’s ability to extract features. Finally, the loss function is substituted with the WIoU (Wise-IoU) strategy. This strategy allows for a more accurate regression of the target bounding boxes and helps to improve the reliability in the localization of WTBs damages, especially for low-quality examples. This model demonstrates a notable superiority in surface damage detection of WTBs compared to the original YOLOv8n and has achieved a substantial improvement in the mAP@0.5 metric, rising from 91.4% to 93.8%. Furthermore, in the more rigorous mAP@0.5–0.95 metric, it has also seen an increase from 68.9% to 71.2%.

The purpose of this study is to explore a method for the high-yield production of hydrogen by pyrolysis and steam reforming of polymer plastics. The developed Fe-based catalyst supported on activated carbon was applied to reactions with polypropylene for hydrogen production. The effects of iron loading (%) in the catalyst, the total catalyst amount, and the water content in the reaction atmosphere on the performance of hydrogen and gas production were investigated. Under the optimal conditions, the hydrogen yield without water added reached 38.73 mmol/gPP, and this yield was significantly improved by adding water into the reaction atmosphere. By optimizing the amount of water added, the hydrogen yield reached 112.71 mmol/gPP. The surface morphology and structural components of the fresh and used catalysts were characterized, and the morphology and quantity of carbon deposition on the catalyst were analysed. The catalytic stability of the 15Fe/AC catalyst was determined by repeating the test 10 times under the optimal reaction conditions. As the reaction time increased, the selectivity of the catalyst for hydrogen decreased and that for hydrocarbons increased. Moreover, the experimental method used in this study had excellent hydrogen production capacity. Thus, this study provided a novel method for the high-efficiency production of hydrogen by pyrolysis and steam reforming of polymer plastics.

Anthocyanins, constituents of flavonoid compounds prevalent in plants, possess significant value in both plant development and human nutrition. The regulation of anthocyanin biosynthesis primarily involves the orchestration of MYB, bHLH, and WD40 transcription factors. Consequently, the bHLH family assumes a pivotal role in modulating plant developmental processes. In the present investigation, a transcription factor, denoted as LvbHLH13, was identified as a positive regulator of anthocyanin pigmentation in lily petals. LvbHLH13 is classified within the IIId subgroup of Arabidopsis bHLH proteins. Functional analyses involving the transient expression and gene silencing of LvbHLH13 revealed its capacity to enhance and diminish anthocyanin accumulation, respectively, by modulating the LvMYB5 expression, thereby influencing the downstream structural gene expression. The overexpression of LvbHLH13 resulted in an increase in the expression of the downstream structural genes related to anthocyanin synthesis, whereas silencing of LvbHLH13 correspondingly decreased the expression. Yeast one-hybrid and EMSA assays demonstrated the interaction between LvbHLH13 and the LvMYB5 promoter, leading to the activation of anthocyanin biosynthesis. A further luciferase (LUC) analysis corroborated the stimulatory effect of LvbHLH13 on the LvMYB5 promoter sequence. Consequently, LvbHLH13 assumed a crucial role in lily-petal pigmentation. A yeast two-hybrid analysis revealed that LvbHLH13 diverged from typical bHLH transcription factor behavior as it did not form a complex with MYB to regulate anthocyanin biosynthesis. This discrepancy could be attributed to the deletion of the N-terminal conserved sequence of LvbHLH13. This study provides a new bHLH candidate and bHLH-MYB partner to explore the anthocyanin regulatory network in further research and provides new opportunities for breeding lilies with various anthocyanin contents. These findings lay a theoretical foundation for subsequent investigations into lily flower coloring mechanisms.

In this paper, a single-feed microstrip antenna (MA) equipped with a transmission-mode focusing metasurface (MS) is proposed to achieve dual-polarization capabilities and superior high-gain radiation performance. The original-feed MA comprises two distinct layers of coaxial-fed tangential patches, enabling it to emit a circular polarization (CP) wave with a gain of 3.5 dBic at 5.6 GHz and linear polarization (LP) radiation with a gain of 4 dBi at 13.7 GHz. To improve the performance of the single-feed MA, a dual-polarization transmission focusing MS is proposed and numerically substantiated. By positioning the originally designed MA at the focal point of the MS, we create a transmission-mode MS antenna system capable of achieving CP and LP radiations with the significantly higher gains of 12.9 dBic and 14.8 dBi at 5.6 GHz and 13.7 GHz, respectively. Measurements conducted on the fabricated dual-polarization focusing MS antenna closely align with the simulation results, validating the effectiveness of our approach. This work underscores the significant potential of dual-polarization high-speed data systems and offers a practical solution for enhancing antenna gains in contemporary wireless communication systems.

Non-routine activities such as startup, shutdown, maintenance, and operation commissioning require increased human interaction with the corresponding process. Owing to operator or procedural violations, the risk of accidents can be high during non-routine activities, even though they are performed less frequently. To identify and evaluate the hazards of non-routine processes, an integrated method combining job hazard analysis (JHA), hazard and operability analysis (HAZOP), and deviation degrees is proposed. JHA is applied to break down an operational process into steps, which are further defined as nodes in HAZOP for hazard scenario analysis. The concept of deviation degree is defined by integrating the operational and control function deviations to quantify the deviation analysis. Finally, the heating-furnace startup process in an oil and gas gathering and transmission station was selected to illustrate the proposed integrated method. The results show that this method constitutes a systematical and intuitive approach to identify hazard scenarios and evaluate risks, as well as to establish preventive measures for non-routine processes.

In the paper, the existence and uniqueness of the equilibrium point in the Cohen-Grossberg neural network (CGNN) are first studied. Additionally, a switched Cohen-Grossberg neural network (SCGNN) model with time-varying delay is established by introducing a switched system to the CGNN. Based on reducing the conservativeness of the system, a flexible terminal interpolation method is proposed. Using an adjustable parameter to divide the invariant time-delay interval into multiple adjustable terminal interpolation intervals $ (2^{\imath +1}-3) $, more moments when signals are transmitted slowly can be captured. To this end, a new Lyapunov-Krasovskii functional (LKF) is constructed, and the stability of SCGNN can be estimated. Using the LKF method, a quadratic convex inequality, linear matrix inequalities (LMIs) and ordinary differential equation theory, a new form of stability criterion is obtained and specific instances are given to prove the applicability of the new stability criterion.

Horticultural plants such as vegetables, fruits, and ornamental plants are crucial to human life and socioeconomic development. Gibberellins (GAs), a class of diterpenoid compounds, control numerous developmental processes of plants. The roles of GAs in regulating growth and development of horticultural plants, and in regulating significant progress have been clarified. These findings have significant implications for promoting the quality and quantity of the products of horticultural plants. Here we review recent progress in determining the roles of GAs (including biosynthesis and signaling) in regulating plant stature, axillary meristem outgrowth, compound leaf development, flowering time, and parthenocarpy. These findings will provide a solid foundation for further improving the quality and quantity of horticultural plants products.

Ovarian insufficiency results from a number of disorders, and a certain causal relationship between psychological stress and ovarian insufficiency has been reported, but the underlying mechanism remains unclear. In our study, C57BL/6J female mice were subjected to chronic unpredictable mild stress (CUMS), and depression-like mice were selected and identified according to the behavioral tests. The defective ovarian follicle development, low 17 β-estradiol (E2), and anti-Mullerian hormone (AMH) levels, which were consistent with the clinical characteristics of ovarian insufficiency, indicated that depression-like mice may be used to assess the effects of psychological stress on female reproductive function. To investigate a possible mechanism, lipid homeostasis of the ovary was detected by liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis, and the decreased abundance of cholesteryl ester (CE 24:4) was supported to be associated with the downregulated E2. Moreover, granulosa cells did undergo more apoptosis in response to psychological stress, which was caused by downregulated Bcl2 and Bcl2/Bax in granulosa cells. Additionally, the disorder of cell death and growth-related pathways in depression-like mouse ovaries was confirmed by RNA-seq analysis. Taken together, this study will provide a better understanding of the female reproductive problem under psychological stress.

The traditional von Neumann architecture separates memory from the central processing unit (CPU), resulting in aggravated data transfer bottlenecks between the CPU and memory during a data volume surge. Emerging technologies, such as in‐memory computing (IMC), provide a new way to overcome the limitations due to the separation of memory and computation. However, existing IMC efforts are generally limited to a single (gate‐control or drain‐control) mode of operation to achieve functionality. Herein, a 2D ferroelectric channel device that enables the feasibility of multioperation modes is proposed. In addition, rich functionalities, such as logic, nonvolatile memory, and neuromimetic plasticity modulation, by switching the operating modes are realized. A device that facilitates multimodal operations and a promising technical solution for further development of burgeoning computing architecture is provided.

Accurate land cover (LC) datasets are the basis for global environmental and climate change studies. Recently, numerous open-source annual LC datasets have been created due to advances in remote sensing technology. However, the agreements and sources of error that affect the accuracy of current annual LC datasets are not well understood, which limits the widespread use of these datasets. We compared four annual LC datasets, namely the CLCD, MCD12Q1, CCI-LC, and GLASS-LC, in the Yellow River Basin (YRB) to identify their spatial and temporal agreement for nine LC classes and to analyze their sources of error. The Mann–Kendall test, Sen’s slope analysis, Taylor diagram, and error decomposition analysis were used in this study. Our results showed that the main LC classes in the four datasets were grassland and cropland (total area percentage > 80%), but their trends in area of change were different. For the main LC classes, the temporal agreement was the highest between the CCI-LC and CLCD (0.85), followed by the MCD12Q1 (0.21), while the lowest was between the GLASS-LC and CLCD (−0.11). The spatial distribution of area for the main LC classes was largely similar between the four datasets, but the spatial agreement in their trends in area of change varied considerably. The spatial variation in the trends in area of change for the cropland, forest, grassland, barren, and impervious LC classes were mainly located in the upstream area region (UA) and the midstream area region (MA) of the YRB, where the percentage of systematic error was high (>68.55%). This indicated that the spatial variation between the four datasets was mainly caused by systematic errors. Between the four datasets, the total error increased along with landscape heterogeneity. These results not only improve our understanding of the spatial and temporal agreement and sources of error between the various current annual LC datasets, but also provide support for land policy making in the YRB.

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models’ application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472.

The cold metal transfer (CMT) process is widely used in thin plate welding because of its characteristics of low heat input and stable arc. In actual production, a larger weld gap, misalignment, or other problems due to assembly error lead to serious welding defects, such as burn-through and a lack of fusion. The arc sound contains a wealth of information related to the quality of the weld. This work analyzes the mechanism of CMT arc sound generation, as well as the correlation between the time–frequency spectrum of the arc sound signal and welding quality. This paper studies the extraction of the multi-channel time–frequency spectrum of an arc sound and inputs it to a custom convolutional neural network for the CMT welding defect identification of thin aluminum alloy plates. The experimental result shows that the average accuracy of the proposed model is 91.49% in the defect identification of a CMT arc-welded aluminum alloy sheet, which is higher than that of the single-channel time–frequency convolutional neural network and other traditional classification models.

In order to ensure the security of logistics information and to query information quickly and efficiently, using searchable encryption algorithms, combined with the characteristics of the blockchain, a searchable and encrypted logistics information blockchain data query algorithm is proposed. First, the logistics information is divided into multiple data files, encrypted with an asymmetric searchable encryption algorithm, and then stored in the cloud server. The keyword index value is extracted from each data file and uploaded to the blockchain. This solution can be used at any time Update and query data. Finally, analyze the correctness, completeness and safety of the scheme of this article, which proves the feasibility of this scheme.

Dehydrins (DHNs) play crucial roles in a broad spectrum of abiotic stresses in model plants. However, the evolutionary role of DHNs has not been explored, and the function of DHN proteins is largely unknown in Ammopiptanthus nanus (A. nanus), an ancient and endangered legume species from the deserts of northwestern China. In this study, we isolated a drought-response gene (c195333_g1_i1) from a drought-induced RNA-seq library of A. nanus. Evolutionary bioinformatics showed that c195333_g1_i1 is an ortholog of Arabidopsis DHN, and we renamed it AnDHN. Moreover, DHN proteins may define a class of proteins that are evolutionarily conserved in all angiosperms that have experienced a contraction during the evolution of legumes. Arabidopsis plants overexpressing AnDHN exhibited morpho-physiological changes, such as an increased germination rate, higher relative water content (RWC), higher proline (PRO) content, increased peroxidase (POD) and catalase (CAT) activities, lower contents of malondialdehyde (MDA), H2O2 and O2–, and longer root length. Our results showed that the transgenic lines had improved drought resistance with deep root system architecture, excellent water retention, increased osmotic adjustment, and enhanced reactive oxygen species (ROS) scavenging. Furthermore, the transgenic lines also had enhanced salt and cold tolerance. Our findings demonstrate that AnDHN may be a good candidate gene for improving abiotic stress tolerance in crops.Key Message: Using transcriptome analysis in Ammopiptanthus nanus, we isolated a drought-responsive gene, AnDHN, that plays a key role in enhancing abiotic stress tolerance in plants, with strong functional diversification in legumes.

The macrophage receptor with collagenous structure (MARCO) plays a key role in bacteria-binding innate immune response. According to the transcriptome data MARCO was identified from the spleen (designated as NaMARCO) of Nibea albiflora, its full-length (GenBank accession no. MN243703.1) was 1788 bp, and the open reading frames (ORF) was 1278 bp encoding a polypeptide of 425 amino acids with predicted pI/MW 8.72/43.61 kDa, which had the conserved scavenger receptor cysteine-rich (SRCR) domain similar to its mammalian counterparts. The BLASTp analysis and phylogenetic relationship strongly suggested that NaMARCO shared high similarity with known MARCO, especially at theSRCR domain. The expression of NaMARCO was detected in six tissues (liver, spleen, head kidney, muscle, gill and stomach) and the highest expression appeared in the spleen. After being challenged with Pseudomonas plecoglossicida and poly I:C, NaMARCO expressed with a time-dependent manner in the spleen, and significantly got the peak at 48 h (9.86 times compared with 0 h) for P.plecoglossicida infection, but the peak appeared at 2 h by being challenged with poly I:C (5.18 times compared with 0 h). The results suggested that NaMARCO might be involved in the immune defence of N.albiflora. In addition, subcellular localization of NaMARCO was performed in EPC cells (epithelial cells from fathead minnow), and it was expressed mainly on the cell membrane, the endoplasmic reticulum and the organelle membrane surrounding the nucleus appeared positive signals. These results suggested that NaMARCO, as an important kind of transmembrane scavenger receptors, had a potential role in the immune defense system of N.albiflora against the pathogen.

Abstract Background Roses are important plants for human beings with pivotal economical and biological traits like continuous flowering, flower architecture, color and scent. Due to frequent hybridization and high genome heterozygosity, classification of roses and their relatives remains a big challenge. Results Here, to identify potential markers for phylogenetic reconstruction and to reveal the patterns of natural selection in roses, we generated sets of high quality and comprehensive reference transcriptomes for Rosa chinensis ‘Old Blush’ (OB) and R. wichuriana ‘Basye’s Thornless’ (BT), two species exhibiting contrasted traits of high economical importance. The assembled reference transcriptomes showed transcripts N50 above 2000 bp. Two roses shared about 10,073 transcripts (N50 = 2282 bp), in which a set of 5959 transcripts was conserved within genera of Rosa. Further comparison with species in Rosaceae identified 4447 transcripts being common (Rosaceae-common) in Rosa, Malus, Prunus, Rubus, and Fragaria, while a pool of 164 transcripts being specific for roses (Rosa-specific). Among the Rosaceae-common transcripts, 409 transcripts showed a signature of positive selection and a clustered expression in different tissues. Interestingly, nine of these rapidly evolving genes were related to DNA damage repair and responses to environmental stimulus, a potential associated with genome confliction post hybridization. Coincident with this fast evolution pattern in rose genes, 24 F-box and four TMV resistant proteins were significantly enriched in the Rosa-specific genes. Conclusions We expect that these Rosaceae-common and Rosa-specific transcripts should facilitate the phylogenetic analysis of Rosaceae plants as well as investigations of Rosa-specific biology. The data reported here could provide fundamental genomic tools and knowledge critical for understanding the biology and domestication of roses and for roses breeding.

A misaligned rigid rotor system with flexible supports differing from the traditional flexible rotor system, which refers to its practical rotating center line determined by supports offset from the theoretical one, often suffers support structure damage risk. In the present work, the dynamic characteristics of a misaligned rigid rotor system with flexible squirrel cage supports are focused, and the vibrations and the stress of its support structures under different misaligned offsets are investigated with experimental and simulated analysis. The finite element model for a rigid rotor system with flexible supports in a scaled test rig is established, and its strain energy distribution is analyzed to find that the first two modes of the system are referred to rigid-body modes and the strain energy is mainly distributed on the squirrel cage supports. Based on the analysis results, a rigid-flexible coupling dynamic model is proposed through a data exchange between ADAMS and ANSYS and validated by measurements. The misaligned conditions are focused on, and the influences of misalignment on the vibrations of the rigid rotor system and the reaction force and stress of its support structures are investigated analytically and experimentally. The results from simulation agree very well with the measurements and reveal that the static stress of squirrel cage increases just about proportionally with misalignment levels, but the vibration displacement amplitudes and stress amplitudes show very little change. The more serious the misaligned condition, the higher the static stress of the squirrel cage. Because misalignment will bring out the additional reaction forces in the misaligned direction, it will further result in higher stress and even more serious damage risk for the flexible supports than other parts in the rigid rotor system.

BackgroundThe prevalence of pulmonary embolism (PE) in the acute exacerbation of chronic obstructive pulmonary disease (AE-COPD) is highly controversial. We conducted a systematic review and meta-analysis to summarize the epidemiology and characteristics of PE with AE-COPD for current studies.MethodsWe searched the PubMed, EMBASE, Cochrane Library and Web of Science databases for studies published prior to October 21, 2020. Pooled proportions with 95% confidence intervals (95% CIs) were calculated using a random effects model. Odds ratios (ORs) and mean differences (MDs) with 95% confidence intervals were used as effect measures for dichotomous and continuous variables, respectively.ResultsA total of 17 studies involving 3170 patients were included. The prevalence of PE and deep vein thrombosis (DVT) in AE-COPD patients was 17.2% (95% CI: 13.4%-21.3%) and 7.1% (95% CI: 3.7%-11.4%%), respectively. Dyspnea (OR = 6.77, 95% CI: 1.97-23.22), pleuritic chest pain (OR = 3.25, 95% CI: 2.06-5.12), lower limb asymmetry or edema (OR = 2.46, 95% CI:1.51-4.00), higher heart rates (MD = 20.51, 95% CI: 4.95-36.08), longer hospital stays (MD = 3.66, 95% CI: 3.01-4.31) were associated with the PE in the AE-COPD patients. Levels of D-dimer (MD = 1.51, 95% CI: 0.80-2.23), WBC counts (MD = 1.42, 95% CI: 0.14-2.70) were significantly higher and levels of PaO2 was lower (MD = -17.20, 95% CI: -33.94- -0.45, PConclusionsThe prevalence of PE during AE-COPD varies considerably among the studies. AE-COPD patients with PE experienced an increased risk of death, especially among the ICU patients. Understanding the potential risk factors for PE may help clinicians identify AE-COPD patients at increased risk of PE.Prospero registration numberCRD42021226568.

The research on damage detection in wind turbine blades plays an important role in reducing the risk of shut down in wind turbines. Rapid and accurate damage identification by using efficient detection models is the focus of the current research on damage detection in wind turbine blades. To solve the problems of the complex structure of the model and high time consumption in deep learning models, an improved broad learning system (BLS) model using the algorithm of chunking based on non-local means (NLMs) was proposed, which was called the CBNLM-BLS. The chunked, in-parallel accelerated integral image approach was used to optimize the NLM to speed up the BLS. Experiment results showed that the proposed model achieved a classification accuracy of 99.716%, taking 28.662 s to detect damage in the wind turbine blades. Compared with deep neural network models, such as ResNet, AlexNet and VGG-19, the proposed CBNLM-BLS had higher classification accuracy, shorter training time and less complex model construction and parameters. Compared with traditional BLSs, the CBNLM-BLS had less time complexity. It is of great significance to identify damage in wind turbine blades more efficiently.

The yellow drum (Nibea albiflora; Na) constitutes an important seafood fish species that is subjected to intensive cage culture in China. The fish is susceptible to infection by bacteria and virtual pathogens, which results in considerable economic loss. In the present study, the pathogenic characteristics and immune response of the yellow drum were analyzed after Pseudomonas plecoglossicida or poly I:C challenge. Tissue sections revealed cell vacuolization in the liver and obvious macrophage infiltration in the spleen following P. plecoglossicida infection, whereas these tissues exhibited no significant lesions upon poly I:C challenge. Superoxide dismutases (SODs) and heat shock proteins (HSPs), which play important roles in the immune response of fish in defense against environmental threats or stresses, were further analyzed. The quantitative real time PCR (qPCR) analysis showed that mRNAs for intracellular copper/zinc-SOD (NaSOD1), manganese SOD (NaSOD2), extracellular copper/zinc-SOD (NaSOD3), HSP70, and HSP90 were expressed in 11 tested tissues including high levels in the liver, spleen, kidney, and gill; further significant increase was observed in the liver, spleen, and gill following P. plecoglossicida or poly I:C challenge. Moreover, the activities of total SOD and the contents of NaHSP70 and NaHSP90 in the liver were up-regulated after P. plecoglossicida or poly I:C challenge, which was generally similar to the fluctuations of mRNA expression in the tissue. The results showed that the dynamic changes in SODs and HSPs exhibited a similar tendency, which might indicate a coordinate action against P. plecoglossicida or poly I:C challenge. These findings will contribute towards better understanding of the biological process involved in defending against pathogenic bacteria and virtual challenge, as well as provide insight regarding the molecular mechanisms of the initial immune response in the yellow drum.

Ultrasonic bonding is a convenient bonding technology, which features sufficient cleanliness, high efficiency, no need for additional bonding aids, and other outstanding advantages. In recent years, it has been introduced into the field of the micro–nano assembly of polymer micro–nano devices, but it is still difficult for current ultrasonic bonding technology to meet the accuracy requirements of the micro–nano assembly. To improve the control accuracy of the hot-melt interface in the process of ultrasonic bonding, an online hot-melt interface monitoring method and an online ultrasonic transmission efficiency detection method are proposed in this article. With these detection methods, the real-time monitoring of the hot-melt interface can be realized on the basis of machine vision, while high-frequency dynamic force sensors can be used to detect the ultrasonic vibration transmitted from the ultrasonic horn to the anvil. Based on these methods, a functional anvil based on visual monitoring and ultrasonic detection is developed, the finite element method has been used to analyze the transmission characteristics of ultrasonic vibration, and experiments are carried out regarding online detection in the process of ultrasonic bonding. The results show that this system can realize the online detection of the hot-melt interface and ultrasonic transmission information, providing a new control method for ultrasonic bonding technology.

Three-point bending fatigue test of 5083 and 5A06 aluminum alloy T-welded joints is carried out, and the fatigue life of the specimens with different influencing factors are obtained. Finite element model of the T-welded joint is established and the nodal force based structural stress is calculated. Neighborhood rough set theory is used for analysis of the factors which influence the fatigue life of the aluminum alloy welded joints. Key influencing factors are studied and the fatigue characteristic domains are determined. The master S-N curve characterized by the nodal force based structural stress range and cycles to failure on semi log coordinate as well as S-N curves corresponding to the fatigue characteristic domain is fitted. A case study of fatigue life prediction of 5A06 aluminum alloy welded joint indicates the effectiveness of the fatigue life prediction method based on the fatigue characteristic domain.

This project explored the integrated use of satellite, ground observations and hydrological distributed models to support water resources assessment and monitoring in High Mountain Asia (HMA). Hydrological data products were generated taking advantage of the synergies of European and Chinese data assets and space-borne observation systems. Energy-budget-based glacier mass balance and hydrological models driven by satellite observations were developed. These models can be applied to describe glacier-melt contribution to river flow. Satellite hydrological data products were used for forcing, calibration, validation and data assimilation in distributed river basin models. A pilot study was carried out on the Red River basin. Multiple hydrological data products were generated using the data collected by Chinese satellites. A new Evapo-Transpiration (ET) dataset from 2000 to 2018 was generated, including plant transpiration, soil evaporation, rainfall interception loss, snow/ice sublimation and open water evaporation. Higher resolution data were used to characterize glaciers and their response to environmental forcing. These studies focused on the Parlung Zangbo Basin, where glacier facies were mapped with GaoFeng (GF), Sentinal-2/Multi-Spectral Imager (S2/MSI) and Landsat8/Operational Land Imager (L8/OLI) data. The geodetic mass balance was estimated between 2000 and 2017 with Zi-Yuan (ZY)-3 Stereo Images and the SRTM DEM. Surface velocity was studied with Landsat5/Thematic Mapper (L5/TM), L8/OLI and S2/MSI data over the period 2013–2019. An updated method was developed to improve the retrieval of glacier albedo by correcting glacier reflectance for anisotropy, and a new dataset on glacier albedo was generated for the period 2001–2020. A detailed glacier energy and mass balance model was developed with the support of field experiments at the Parlung No. 4 Glacier and the 24 K Glacier, both in the Tibetan Plateau. Besides meteorological measurements, the field experiments included glaciological and hydrological measurements. The energy balance model was formulated in terms of enthalpy for easier treatment of water phase transitions. The model was applied to assess the spatial variability in glacier melt. In the Parlung No. 4 Glacier, the accumulated glacier melt was between 1.5 and 2.5 m w.e. in the accumulation zone and between 4.5 and 6.0 m w.e. in the ablation zone, reaching 6.5 m w.e. at the terminus. The seasonality in the glacier mass balance was observed by combining intensive field campaigns with continuous automatic observations. The linkage of the glacier and snowpack mass balance with water resources in a river basin was analyzed in the Chiese (Italy) and Heihe (China) basins by developing and applying integrated hydrological models using satellite retrievals in multiple ways. The model FEST-WEB was calibrated using retrievals of Land Surface Temperature (LST) to map soil hydrological properties. A watershed model was developed by coupling ecohydrological and socioeconomic systems. Integrated modeling is supported by an updated and parallelized data assimilation system. The latter exploits retrievals of brightness temperature (Advanced Microwave Scanning Radiometer, AMSR), LST (Moderate Resolution Imaging Spectroradiometer, MODIS), precipitation (Tropical Rainfall Measuring Mission (TRMM) and FengYun (FY)-2D) and in-situ measurements. In the case study on the Red River Basin, a new algorithm has been applied to disaggregate the SMOS (Soil Moisture and Ocean Salinity) soil moisture retrievals by making use of the correlation between evaporative fraction and soil moisture.

The scatter degree of the fatigue samples is reduced when the nodal force based structural method is used for steel welded joints, while it is still high for aluminum alloy welded joints. Statistical method and rough set theory is used to fatigue analysis so that fatigue characteristic domains are determined and S-N curves are fitted. Experiment results show that fatigue life of the aluminum alloy welded joints is under the influence of some key factors and the fatigue data with the same characteristics distribute in a relatively independent area. Accordingly, a novel S-N curve modeling method of aluminum alloy welded joints based on the fatigue characteristics domain is proposed. In the proposed method, the nodal force based structural stress method is used for stress calculation and neighborhood rough set theory is used for character extraction to obtain the key factors. Then fatigue characteristics domains are divided and S-N curves are fitted on each fatigue characteristics domain instead of on the whole domain so that a set of S-N curves are obtained. Statistical results show that selection of the S-N curve for the aluminum alloy welded joints according to different fatigue characteristic domain is more accurate.

Fish produce and release bile salts as chemical signalling substances that act as sensitive olfactory stimuli. To investigate how bile salts affect olfactory signal transduction in large yellow croaker (Larimichthy crocea), deep sequencing of olfactory epithelium was conducted to analyse olfactory-related genes in olfactory transduction. Sodium cholates (SAS) have typical bile salt chemical structures, hence we used four different concentrations of SAS to stimulate L. crocea, and the fish displayed a significant behavioural preference for 0.30% SAS. We then sequenced olfactory epithelium tissues, and identified 9938 unigenes that were significantly differentially expressed between SAS-stimulated and control groups, including 9055 up-regulated and 883 down-regulated unigenes. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses found eight categories linked to the olfactory transduction pathway that was highly enriched with some differentially expressed genes (DEGs), including the olfactory receptor (OR), Adenylate cyclase type 3 (ADCY3) and Calmodulin (CALM). Genes in these categories were analysed by RT-qPCR, which revealed aspects of the pathway transformation between odor detection, and recovery and adaptation. The results provide new insight into the effects of bile salt stimulation in olfactory molecular mechanisms in fishes, and expands our knowledge of olfactory transduction, and signal generation and decline.

In this study, we investigated the fatigue behavior of Q460 welded joints using tensile fatigue tests. Furthermore, real-time temperature profiles of the examined specimens were recorded by infrared thermography. Based on the obtained thermographic data, we calculated the entropy production rate of the specimens under different stress amplitudes. Hypothetically, the entropy production during high-cycle fatigue (HCF) could be divided into two parts. The first is induced by inelastic behavior that corresponds to damage accumulation, and the second originates from anelasticity associated with recoverable non-damaging microstructural motions. The turning point of entropy production under different stress levels represents an index for fatigue limit estimation. Then, considering the average damage threshold that exists during HCF, the entropy production related to damage accumulation (cumulative damage entropy) is obtained by testing three specimens under the same stress amplitude above the fatigue limit. Finally, a rapid three-parameter S-N curve with a survival probability rate of 50% is obtained. Then, combined with the maximum likelihood method, the 5% and 95% survival probability rate S-N curves are established. Most of experimental data are distributed in the area between S-N curves that correspond to 5% and 95% survival probability rate, indicating good accordance with the test data.

In order to promote the renewal and development of the capital city and avoid the "low-speed development trap" in the economy, the government proposed to use "high-tech" industries as the driving force to accelerate the construction of a "high-tech" industrial economic structure and increase investment in industrial innovation, so as to better guide and support overall and long-term development of economic and social. This article uses the SWOT analysis model, based on the actual development of Beijing’s industry, deeply analyzes the advantages, disadvantages, opportunities and challenges of the city’s development of "high-tech" industries, and proposes four aspects of countermeasure, such as improving fiscal and tax incentive policies, improving financing methods, optimizing industrial layout, promoting dual-chain integration. Beijing Municipal Government should speed up a reasonable "high-tech" industrial structure, give full play to the role of "high-tech" industries in "improving quality and efficiency" in Beijing's economic development, and then optimize and enhance the core functions of the capital.

In order to promote China's economy high-quality development, the country has continuously lowered the certification standards for high-tech enterprises. The existing research literature has not yet reached a clear conclusion about the impact of high-tech qualification certification on enterprise innovation performance. This paper uses the Shanghai and Shenzhen A-share listed companies from 2013 to 2018 as a research sample. Through the propensity score matching method, it is found that the implementation of the high-tech enterprise identification policy has an effect on the innovation performance of the enterprise, especially for strategic innovation represented by the number of non-invention patent applications. The sub-sample results show that the identification policy has a more obvious impact on the innovation performance of non-state-owned enterprises, young enterprises and larger enterprises. Finally, based on the results of the research, it is proposed to continuously improve the identification policy, guide enterprises to make substantial innovations, and implement hierarchical tax rates for different types of enterprises to help improve the identification policies for high-tech enterprises.

Airborne eddy covariance (EC) measurement is one of the most effective methods to directly measure the surface mass and energy fluxes at the regional scale. It offers the possibility to bridge the scale gap between local- and global-scale measurements by ground-based sites and remote-sensing instrumentations, and to validate the surface fluxes estimated by satellite products or process-based models. In this study, we developed an unmanned aerial vehicle (UAV)-based EC system that can be operated to measure the turbulent fluxes in carbon dioxides, momentum, latent and sensible heat, as well as net radiation and photosynthetically active radiation. Flight tests of the developed UAV-based EC system over land were conducted in October 2020 in Inner Mongolia, China. The in-flight calibration was firstly conducted to correct the mounting error. Then, three flight comparison tests were performed, and we compared the measurement with those from a ground tower. The results, along with power spectral comparison and consideration of the differing measurement strategies indicate that the system can resolve the turbulent fluxes in the encountered measurement condition. Lastly, the challenges of the UAV-based EC method were discussed, and potential improvements with further development were explored. The results of this paper reveal the considerable potential of the UAV-based EC method for land surface process studies.

Objective: The aim of this study was to validate the accuracy of a new automatic method for scar segmentation and compare its performance with that of two other frequently used segmentation algorithms. Methods: Twenty-six late gadolinium enhancement cardiovascular magnetic resonance images of diseased hearts were segmented by the full width at half maximum (FWHM) method, the n standard deviations ( n SD) method, and our new automatic method. The results of the three methods were compared with the consensus ground truth obtained by manual segmentation of the ventricular boundaries. Results: Our automatic method yielded the highest Dice score and the lowest volume difference compared with the consensus ground truth segmentation. The n SD method produced large variations in the Dice score and the volume difference. The FWHM method yielded the lowest Dice score and the greatest volume difference compared with the automatic, 6SD, and 8SD methods, but resulted in less variation when different observers segmented the images. Conclusion: The automatic method introduced in this study is highly reproducible and objective. Because it requires no manual intervention, it may be useful for processing large datasets produced in clinical applications.

Spot welding of dissimilar materials can utilize the respective advantage comprehensively, of which reliable prediction of fatigue life is the key issue in the structure design and service process. Taking into account almost all the complex factors that have effects on the fatigue behavior such as load level, thickness, welding nugget diameter, vibrational frequency, and material properties, this article proposed an energy dissipation-based method that is able to predict the fatigue life for spot-welded dissimilar materials rapidly. In order to obtain the temperature gradient, the temperature variations of four-group spot-welded joint of SUS301 L-DLT stainless steel and Q235 carbon steel during high-cycle fatigue tests were monitored by thermal infrared scanner. Specifically, temperature variation disciplines of specimen surface were divided into four stages: temperature increase, temperature decrease, continuous steady increase in temperature, and ultimate drop after the fracture. The material constant C that a spot-welded joint of dissimilar material needs to reach fracture is 0.05425°C·mm 3 . When the specimen was applied higher than the fatigue limit, the highest error between experimental values and predicted values is 18.90%, and others are lower than 10%. Therefore, a good agreement was achieved in fatigue life prediction between the new method and the validation test results.

The scatter degree of the fatigue samples is reduced when the nodal force based structural method is used for steel welded joints, while it is still high for aluminum alloy welded joints. Statistical method and rough set theory is used to fatigue analysis so that fatigue characteristic domains are determined and S-N curves are fitted. Experiment results show that fatigue life of the aluminum alloy welded joints is under the influence of some key factors and the fatigue data with the same characteristics distribute in a relatively independent area. Accordingly, a novel S-N curve modeling method of aluminum alloy welded joints based on the fatigue characteristics domain is proposed. In the proposed method, the nodal force based structural stress method is used for stress calculation and neighborhood rough set theory is used for character extraction to obtain the key factors. Then fatigue characteristics domains are divided and S-N curves are fitted on each fatigue characteristics domain instead of on the whole domain so that a set of S-N curves are obtained. Statistical results show that selection of the S-N curve for the aluminum alloy welded joints according to different fatigue characteristic domain is more accurate.

In order to obtain comprehensive assessment of the factors influencing fatigue life and to further improve the accuracy of fatigue life prediction of welded joints, soft computing methods, including entropy-based neighborhood rough set reduction algorithm, the particle swarm optimization (PSO) algorithm and support vector regression machine (SVRM) are combined to construct a fatigue life prediction model of titanium alloy welded joints. By using an entropy-based neighborhood rough set reduction algorithm, the influencing factors of the fatigue life of titanium alloy welded joints such as joint type, plate thickness, etc. are analyzed and the reduction results are obtained. Fatigue characteristic domains are proposed and determined subsequently according to the reduction results. The PSO-SVRM model for fatigue life prediction of titanium alloy welded joints is established in the suggested fatigue characteristic domains. Experimental results show that by taking into account the impact of joint type, the PSO-SVRM model could better predict the fatigue life of titanium alloy welded joints. The PSO-SVRM model indicates the relationship between fatigue life and fatigue life influencing factors in multidimensional space compared with the conventional least-square S-N curve fitting method, it could predict the fatigue life of the titanium alloy welded joints more accurately thus helps to the reliability design of the structure.

Airborne eddy covariance (EC) is one of the most effective ways to directly measure turbulent flux at a regional scale. This study aims to find the optimum spatial window length for turbulent heat fluxes calculation from airborne eddy covariance measurements under near neutral to unstable atmospheric stability conditions, to reduce the negative influences from mesoscale turbulence, and to estimate local meaningful turbulent heat fluxes accurately. The airborne flux measurements collected in 2008 in the Netherlands were used in this study. Firstly, the raw data was preprocessed, including de-spike, segmentation, and stationarity test. The atmospheric stability conditions were classified as near neutral, moderately unstable, or very unstable; the stable condition was excluded. Secondly, Ogive analysis for turbulent heat fluxes from all available segmentations of the airborne measurements was used to determine the possible window length range. After that, the optimum window length for turbulent heat flux calculations was defined based on the analysis of all possible window lengths and their uncertainties. The results show that the choice of the optimum window length strongly depends on the atmospheric stability conditions. Under near neutral conditions, local turbulence is mixed insufficiently and vulnerable to heterogeneous turbulence. A relatively short window length is needed to exclude the influence of mesoscale turbulence, and we found the optimum window length ranges from 2000 m to 2500 m. Under moderately unstable conditions, the typical scale of local turbulence is relative large, and the influence of mesoscale turbulence is relatively small. We found the optimum window length ranges from 3900 m to 5000 m. Under very unstable conditions, large convective eddies dominate the transmission of energy so that the window length needs to cover the large eddies with large energy transmission. We found the optimum window length ranges from 4500 m to 5000 m. This study gives a comprehensive methodology to determine the optimizing window length in order to compromise a balance between the accuracy and the surface representativeness of turbulent heat fluxes from airborne EC measurements.

Ultrasonic bonding has an increasing application in the micro assembly of polymeric micro-electro mechanical systems (MEMS) with high requirements for fusion precision. In the ultrasonic bonding process, the propagation of ultrasonic vibration in polymer components is related to the interfacial fusion, which can be used as a monitoring parameter to control ultrasonic energy. To study the vibration propagation in viscoelastic polymer components, finite element analysis on the bonding of poly methyl methacrylate (PMMA) micro connector to substrate for microfluidic system is carried out. Curves of propagated vibration amplitude corresponding to interfacial temperatures are obtained. The ultrasonic vibration propagated in PMMA components are measured through experiments. The theoretical and experimental results are contrasted to analyze the change mechanism of vibration propagation related to temperature. Based on the ultrasonic bonding process controlled by the feedback of vibration propagation, interfacial fusions at different vibration propagation states are obtained through experiments. Interfacial fusion behavior is contrasted to the propagated vibration amplitude in theoretical and experimental studies. The relation between vibration propagation and fusion degree is established with the proper parameter range for the obtained high quality bonding.

An evaluation model of aluminum alloy welded joint low-cycle fatigue data based on information entropy is proposed. Through calculating and analyzing the information entropy of decision attributes, quantitative contribution of stress concentration, plate thickness, and loading mode to the fatigue destruction are researched. Results reveal that the total information entropy of the fatigue data based on nominal stress, structural stress and equivalent structural stress are, respectively, 0.9702, 0.8881, and 0.8294. There is consistency between the reducing trend of the weight-based information entropy and the smaller and smaller standard deviation of the S-N curves. In the structural stress based S-N curve, total stress concentration factor is crucial for the distribution of the fatigue data and the weight based information entropy of membrane stress concentration factor is 0.6754, which illustrates that stress concentration is a key issue of welded structure to which ought to be attached great importance. Subsequently, in the equivalent structural stress-based S-N curve, the weight based information entropy of stress ratio is 0.5759, which plays an important role in the distribution of fatigue data. With the importance level of the attributes on the S-N curves investigated, the correction of R in the equivalent structural stress based master S-N curve method should be carried out to make the welding fatigue prediction much more accurate.