Search

Rice allelopathy is a natural method of weed control that is regarded as an eco-friendly practice in agroecology. The root growth of allelopathic rice at the seedling stage plays an important role in its weed control. Our study characterizes a plant hormone that promotes root growth, abscisic acid (ABA), to explore its role in the induction of rice allelopathy. Increasing the root morphology traits (root length, root tip number, and root biomass) in rice using different concentrations of exogenous ABA resulted in increased inhibitory ratios against barnyard grass (Echinochloa crus-galli), both in a hydroponic experiment and pot test. In particular, the relative proportion of induced allelopathy to total allelopathy in non-allelopathic rice Lemont (Le) was higher than that in allelopathic rice PI31277 (PI). The total content of phenolic acid, which is an important allelochemical in rice, as previously reported, was significantly elevated in the root exudates of both PI and LE. The gene expression levels of OsPAL, OsC4H, and OsCOL related to phenolic acid synthesis were also up-regulated, with a higher regulatory fold in PI. ABA also increased the expression of OsKSL4 and CYP75B4 involved in the biosynthesis of momilactone B and tricin. Moreover, low concentrations of exogenous ABA mainly positively regulate the expression of OsIAA11, an AUX/IAA transcription factor gene, in the root of PI and Le. These findings suggest that the application of ABA could significantly enhance the weed-suppressive activity of both rice cultivars through regulating root growth and the synthesis of allelochemicals secreted by rice roots, providing an option for the improvement of rice allelopathy through chemical induction.

Rubidium and cesium are critical strategic elements, and their development and utilization are of great significance. In this study, a magnesium ammonium phosphate (MAP) adsorbent was prepared and characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, and Fourier transform infrared spectroscopy (FTIR). The adsorption performance of the adsorbent for Rb+ and Cs+ in solution was investigated. The results showed that the adsorbent exhibited high adsorption capacities of 2.83 mol/g for Rb+ and 4.37 mol/g for Cs+. In simulated brine, the adsorbent demonstrated excellent selectivity for Cs+. Kinetic and thermodynamic studies indicated that the adsorption process followed a pseudo-second order kinetic model and Langmuir isotherm model. The primary adsorption mechanism was an ion exchange. The development of this adsorbent holds significant promise for the extraction of rubidium and cesium from liquid resources.

Small UAV target detection and tracking based on cross-modality image fusion have gained widespread attention. Due to the limited feature information available from small UAVs in images, where they occupy a minimal number of pixels, the precision required for detection and tracking algorithms is particularly high in complex backgrounds. Image fusion techniques can enrich the detailed information for small UAVs, showing significant advantages under extreme lighting conditions. Image registration is a fundamental step preceding image fusion. It is essential to achieve accurate image alignment before proceeding with image fusion to prevent severe ghosting and artifacts. This paper specifically focused on the alignment of small UAV targets within infrared and visible light imagery. To address this issue, this paper proposed a cross-modality image registration network based on deep learning, which includes a structure preservation and style transformation network (SPSTN) and a multi-level cross-attention residual registration network (MCARN). Firstly, the SPSTN is employed for modality transformation, transferring the cross-modality task into a single-modality task to reduce the information discrepancy between modalities. Then, the MCARN is utilized for single-modality image registration, capable of deeply extracting and fusing features from pseudo infrared and visible images to achieve efficient registration. To validate the effectiveness of the proposed method, comprehensive experimental evaluations were conducted on the Anti-UAV dataset. The extensive evaluation results validate the superiority and universality of the cross-modality image registration framework proposed in this paper, which plays a crucial role in subsequent image fusion tasks for more effective target detection.

For modern radar systems, small unmanned aerial vehicles (UAVs) belong to a typical types of targets with ‘low, slow, and small’ characteristics. In complex combat environments, the functional requirements of radar systems are not only limited to achieving stable detection and tracking performance but also to effectively complete the recognition of small UAV targets. In this paper, a multi-dimensional feature fusion framework for small UAV target recognition utilizing a small-sized and low-cost high-resolution radar is proposed, which can fully extract and combine the geometric structure features and the micro-motion features of small UAV targets. For the performance analysis, the echo data of different small UAV targets was measured and collected with a millimeter-wave radar, and the dataset consists of high-resolution range profiles (HRRP) and micro-Doppler time–frequency spectrograms was constructed for training and testing. The effectiveness of the proposed method was demonstrated by a series of comparison experiments, and the overall accuracy of the proposed method can reach 98.5%, which demonstrates that the proposed multi-dimensional feature fusion method can achieve better recognition performance than that of classical algorithms and higher robustness than that of single features for small UAV targets.

The improvement in the utilization rate and nutritional value of soybean meal (SBM) represents a significant challenge in the feed industry. This study conducted a 50 kg SBM fermentation based on the 300 g small-scale fermentation of SBM in early laboratory research, to explore the combined effects of lactic acid bacteria (LAB) and acid protease on fermentation quality, chemical composition, microbial population, and macromolecular protein degradation during fermentation and aerobic exposure of SBM in simulated actual production. The results demonstrated that the increase in crude protein content and reduction in crude fiber content were considerably more pronounced after fermentation for 30 days (d) and subsequent aerobic exposure, compared to 3 d. It is also noteworthy that the treated group exhibited a greater degree of macromolecular protein degradation relative to the control and 30 d of fermentation relative to 3 d. Furthermore, after 30 d of fermentation, adding LAB and protease significantly inhibited the growth of undesired microbes including coliform bacteria and aerobic bacteria. In the mixed group, the microbial diversity decreased significantly, and Firmicutes replaced Cyanobacteria for bacteria in both groups’ fermentation.

Whole crop corn silage (WCCS) been an important source of roughage for confined ruminants. However, at the silage feed-out phase, the rise in temperature and relative humidity under aerobic conditions breeds the production of undesirable microorganisms, such as yeast and mold. In order to investigate the conservation characteristics and aerobic stability underlying the effects of additives in whole crop corn silage (WCCS), whole crop corn (WCC) at the milk-ripe stage was ensiled with Lentilactobacillus (L.) buchneri (LB) and different proportions of Artemisia argyi (AA) for 90 days (d) at room temperature, respectively, and aerobic exposure after 90 d fermentation was also conducted. The study found that AA as an additive improved the fermentation quality and enhanced aerobic stability of WCCS, for which the addition of 60% AA increased the lactic acid fermentation rate, with the lactic acid concentration at the end of aerobic exposure significantly higher than in all other treatment groups, at 98.21 g/kg DM (p < 0.01), which decreased the relative abundance of none wanted microorganisms and reduced the content of fungal toxins (p < 0.05). After 90 d of fermentation, LB also increased the organic acids and reduced the pH compared with control, thereby improving fermentation quality. Furthermore, we also discovered that the relative abundance of Candida within the 60% AA was the highest. Candida have the ability to convert WSC into organic acids and lower pH, thus improving the quality of silage. Particularly, 60% AA could improve the fermentation quality and aerobic stability of silage through the biosynthetic pathways of phenylalanine, tyrosine and tryptophan, as well as by participation in the hydrolysis of glycoside hydrolases (GHs). Unexpectedly, the addition of AA was found to reduce the relative abundance of antibiotic resistance genes. WCC, ensiled with 60% AA, exhibited excellent fermentation quality and aerobic stability, providing a theoretical basis for understanding the mechanisms of AA which improve the quality of WCCS during the aeration phase.

In a community, due to the different characteristics of each species, their contributions to community beta diversity may vary. Quantifying the contribution of each species to overall beta diversity (SCBD) is essential for explaining the patterns of beta diversity. However, there is currently limited research linking SCBD with species functional traits, and how species functional traits influence SCBD remains unclear. This study is based on tree census data, species functional traits, and environmental variables from a 15 ha permanent monitoring plot in a tropical karst rainforest in south China. By calculating species-specific SCBD based on abundance and presence–absence data, as well as functional distinctiveness and species ecological niche characteristics (niche position and niche width), we applied structural equation modeling (SEM) to analyze how functional traits, distinctiveness, and niche characteristics jointly influence SCBD. The results revealed that SCBD based on abundance is positively correlated with occupancy and abundance, whereas SCBD based on presence–absence data exhibits a hump-shaped relationship with occupancy and abundance. Species ecological niche characteristics directly influence SCBD, with species occupying central ecological niches having a negative effect on SCBD and niche width having a positive effect. Functional traits and functional distinctiveness indirectly impact SCBD through their influence on species ecological niche characteristics. SEM models based on the presence–absence data provide higher explanatory power. In summary, in the seasonal rainforest communities of northern tropical karst regions in China, the combined effects of species’ functional traits, functional distinctiveness, and ecological niche characteristics determine SCBD. This not only contributes to a deeper understanding of how species traits influence β-diversity, making SCBD a more applicable tool for biodiversity conservation, but also allows for the development of more effective biodiversity protection strategies by elucidating the link between SCBD and ecosystem multifunctionality.

Strengthening the interfacial contact between the reactive components effectively boosts the energy release of energetic materials. In this study, we aimed to create a close-knit interfacial contact condition between aluminum nanoparticles (Al NPs) and Polyvinylidene fluoride-hexafluoropropylene (P(VDF-HFP)) through hydrolytic adsorption and assembling 1H, 1H, 2H, 2H-Perfluorododecyltrichlorosilane (FTCS) on the surface of Al NPs. Leveraging hydrogen bonding between –CF and –CH and the interaction between C–F⋯F–C groups, the adsorbed FTCS directly leads to the growth of the P(VDF-HFP) coating layer around the treated Al NPs, yielding Al@FTCS/P(VDF-HFP) energetic composites. In comparison with the ultrasonically processed Al/P(VDF-HFP) mixture, thermal analysis reveals that Al@FTCS/P(VDF-HFP) exhibits a 57 °C lower reaction onset temperature and a 1646 J/g increase in heat release. Associated combustion tests demonstrate a 52% shorter ignition delay, 62% shorter combustion time, and a 288% faster pressurization rate. These improvements in energetic characteristics stem from the reactivity activation of FTCS towards Al NPs by the etching effect to the surface Al2O3. Moreover, enhanced interfacial contact facilitated by the FTCS-directed growth of P(VDF-HFP) around Al NPs further accelerates the whole reaction process.

Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high-resolution capacity, and is an important tool for the monitoring of building health. Buildings have geometric distortion in conventional 2D SAR images, which brings great difficulties to the interpretation of SAR images. This paper proposes a novel Rotating SAR (RSAR) mode, which acquires 3D information of buildings from two different angles in a single rotation. This new RSAR mode takes the center of a straight track as its rotation center, and obtains images of the same facade of a building from two different angles. By utilizing the differences in geometric distortion of buildings in the image pair, the 3D structure of the building is reconstructed. Compared to the existing tomographic SAR or circular SAR, this method does not require multiple flights in different elevations or observations from varying aspect angles, and greatly simplifies data acquisition. Furthermore, both simulation analysis and actual data experiment have verified the effectiveness of the proposed method.

Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations.

High-pixel-number synchrotron radiation computed tomography (CT) has the advantages of high sensitivity, high resolution, and a large field of view. It has been widely used in biomedicine, cultural heritage research, non-destructive testing, and other fields. The Nyquist sampling theorem states that when the detector’s pixels per row are increased, it requires more CT projections, resulting in a lengthened CT scan time and increased radiation damage. Sparse-view CT can significantly reduce radiation damage and improve the projection data acquisition speed. However, there is insufficient sparse projection data, and the slices reconstructed show aliasing artifacts. Currently, aliasing artifact correction processes more medical CT images, and the number of pixels of such images is small (mainly 512×512 pixels). This paper presents an aliasing artifact correction algorithm based on deep learning for synchrotron radiation CT with a high pixel number (1728×1728 pixels). This method crops high-pixel-number CT images with aliasing artifacts into patches with overlapping features. During the network training process, a convolutional neural network is utilized to enhance the details of the patches, after which the patches are reintegrated into a new CT slice. Subsequently, the network parameters are updated to optimize the new CT slice that closely approximates the full-view slice. To align with practical application requirements, the neural network is trained using only three samples to optimize network parameters and applied successfully to untrained samples for aliasing artifact correction. Comparative analysis with typical deep learning aliasing artifact correction algorithms demonstrates the superior ability of our method to correct aliasing artifacts while preserving image details more effectively. Furthermore, the effect of aliasing artifact correction at varying levels of projection sparsity is investigated, revealing a positive correlation between image quality after deep learning processing and the number of projections. However, the trade-off between rapid experimentation and artifact correction remains a critical consideration.

This paper introduces an innovative differential sampling technique for calibrating AC waveforms, leveraging a commercially available 16-bit digital-to-analog converter (DAC) as the reference standard. The novelty of this approach lies in its enhanced stability over traditional direct sampling methods, especially as the frequency of the AC waveform increases. Notably, this technique provides a cost-effective sampler alternative to the differential sampling methods that rely on a programmable Josephson voltage standard (PJVS). A critical aspect of this methodology is the precise measurement of the DAC’s output voltage, for which a static measurement strategy is adopted to utilize the exceptional linearity and transfer accuracy of the Keysight 3458A (Santa Rosa, CA, USA) in its standard DCV mode. The differential sampling method has demonstrated good accuracy, achieving a near 1 µV/V agreement with a pulse-driven AC Josephson voltage standard (ACJVS) across a 40 Hz to 200 Hz frequency range. The method attained an expanded uncertainty (k = 2) of 1 part in 106 while measuring a 0.707107 VRMS sine wave at 50 Hz, showcasing its efficacy in precise AC waveform calibration.

Ground-penetrating radar (GPR) is a widely used technology for pipeline detection due to its fast detection speed and high resolution. However, the presence of complex underground media often results in strong ground clutter interference in the collected B-scan echoes, significantly impacting detection performance. To address this issue, this paper proposes an improved clutter suppression network based on a cycle-consistency generative adversarial network (CycleGAN). By employing the concept of style transfer, the network aims to convert clutter images into clutter-free images. This paper introduces multiple residual blocks into the generator and discriminator, respectively, to improve the feature expression ability of the deep learning model. Additionally, the discriminator incorporates the squeeze and excitation (SE) module, a channel attention mechanism, to further enhance the model’s ability to extract features from clutter-free images. To evaluate the effectiveness of the proposed network in clutter suppression, both simulation and measurement data are utilized to compare and analyze its performance against traditional clutter suppression methods and deep learning-based methods, respectively. From the result of the measured data, it can be found that the improvement factor (Im) of the proposed method has reached 40.68 dB, which is a significant improvement compared to the previous network.

Salinization of cultivated soils may result in either high salt levels or alkaline conditions, both of which stress crops and reduce performance. We sampled genotypes included in the Northeast China soybean germplasm population (NECSGP) to identify possible genes that affect tolerance to alkaline soil conditions. In this study, 361 soybean accessions collected in Northeast China were tested under 220 mM NaHCO3:Na2CO3 = 9:1 (pH = 9.8) to evaluate the alkali-tolerance (ATI) at the seedling stage in Mudanjiang, Heilongjiang, China. The restricted two-stage multi-locus model genome-wide association study (RTM-GWAS) with gene–allele sequences as markers (6503 GASMs) based on simplified genome resequencing (RAD-sequencing) was accomplished. From this analysis, 132 main effect candidate genes with 359 alleles and 35 Gene × Environment genes with 103 alleles were identified, explaining 90.93% and 2.80% of the seedling alkali-tolerance phenotypic variation, respectively. Genetic variability of ATI in NECSGP was observed primarily within subpopulations, especially in ecoregion B, from which 80% of ATI-tolerant accessions were screened out. The biological functions of 132 candidate genes were classified into eight functional categories (defense response, substance transport, regulation, metabolism-related, substance synthesis, biological process, plant development, and unknown function). From the ATI gene–allele system, six key genes–alleles were identified as starting points for further study on understanding the ATI gene network.

Post-weaning diarrhea is common in piglets, causing huge economic losses worldwide. Associations between LPS challenge, intestinal inflammation, and microbiota have been reported in Duroc × Landrace × Yorkshire (DLY) crossbred pigs. However, the effects of LPS challenge in other breeds remain unclear. In the current study, we performed a comprehensive comparative analysis of the effects of LPS challenge on jejunal mucosal morphology, jejunal microbial composition, and serum indexes in two pig breeds: DLY and Heigai, an indigenous Chinese breed. The results showed that LPS caused considerable damage to the mucosal morphology, enhanced serum levels of inflammatory cytokines and the intestinal permeability index, and lowered the antioxidant capacity index. LPS challenge also changed the microbial composition and structure of the jejunum, significantly increased the abundances of Escherichia-Shigella in DLY pigs, and decreased those of Gemella and Saccharimonadales in Heigai pigs. Furthermore, LPS challenge triggered functional changes in energy metabolism and activities related to the stress response in the jejunal bacterial community, alleviating the inflammatory response in Heigai pigs. This study also revealed that Heigai pigs had a weaker immune response to LPS challenge than DLY pigs, and identified several genera related to the breed-specific phenotypes of Heigai pigs, including Gemella, Saccharimonadales, Clostridia_UCG_014, Terrisporobacter, and Dielma. Our collective findings uncovered differences between Heigai and DLY pigs in intestinal inflammation and microbiota dysbiosis induced by LPS challenge, providing a theoretical basis for unraveling the mechanism of intestinal inflammation in swine and proposing microbial candidates involved in the resistance to diarrhea in piglets.

Synthetic aperture radar (SAR) is a powerful tool for detecting and imaging targets in enclosed environments, such as tunnels and underground garages. However, SAR performance is degraded by multipath effects, which occur when electromagnetic waves are reflected by obstacles, such as walls, and interfere with the direct signal. This results in the formation of multipath ghost images, which obscure the true target and reduce the image quality. To overcome this challenge, we propose a novel method based on multi-angle observation. This method exploits the fact that the position of ghost images changes depending on the angle of the radar, while the position of the true target remains stable. By collecting and processing multiple data sets from different angles, we can eliminate the ghost images and enhance the target image. In addition, we introduce a center vector distance algorithm to address the complexity and computational intensity of existing multipath suppression algorithms. This algorithm, which defines the primary direction of multi-angle vectors from stable scattering centers as the center vector, processes and synthesizes multiple data sets from multi-angle observations. It calculates the distance of pixel intensity sequences in the composite data image from the center vector. Pixels within a specified threshold are used for imaging, and the final result is obtained. Simulation experiments and real SAR data from underground garages confirm the effectiveness of this method in suppressing multipath ghost images.

When Synthetic Aperture Radar (SAR) observes complex structural targets such as oil tanks, it is easily interfered with by multipath signals, resulting in a large number of multipath ghost images in the SAR image, which seriously affect the image clarity. To address this problem, this paper proposes a multi-aspect multipath suppression method. This method observes complex structural targets from different azimuth angles to obtain a multi-aspect image sequence and then uses the difference in sequence features between the target image and the multipath ghost image with respect to aspect angle to separate them. This paper takes a floating-roof oil tank as an example to analyze the propagation path and the ghost image characteristics of multipath signals under different observation aspects. We conclude that the scattering center of the multipath ghost image changes with the radar observation aspect, whereas the scattering center of the target image does not. This paper uses the Robust Principal Component Analysis (RPCA) method to decompose the image sequence matrix into two parts: a sparse matrix and a low-rank matrix. The low-rank matrix represents the aspect-stable principal component in the image sequence; that is, the real scattering center. The sparse matrix represents the part of the image sequence that deviates from the principal component; that is, the signal that varies with aspect, mainly including multipath signals, sidelobes, anisotropic signals, etc. By reconstructing the low-rank matrix and the sparse matrix, respectively, we can obtain the image after multipath signal suppression and also the multipath ghost image. Both the target and the multipath signal provide useful information. The image after multipath signal suppression is useful for obtaining the structural information of the target, and the multipath ghost image is useful for analyzing the multipath phenomenon of the complex structure target. This paper conducts experimental verification using real airborne SAR data of an external floating roof oil tank and compares three methods: RPCA, PCA, and sub-aperture fusion method. The experiment shows that the RPCA method can better separate the target image and the multipath ghost image.

Anthocyanins play a paramount role in color variation and significantly contribute to the economic value of ornamental plants. The conserved activation complex MYB-bHLH-WD40 (MBW; MYB: v-myb avian myeloblastosis viral oncogene homolog; bHLH: basic helix–loop–helix protein; WD40:WD-repeat protein) involved in anthocyanin biosynthesis has been thoroughly researched, but there have been limited investigations into the function of repressor factors. In this study, we characterized TgMYB4, an R2R3-MYB transcriptional repressor which is highly expressed during petal coloration in red petal cultivars. TgMYB4-overexpressing tobaccos exhibited white or light pink petals with less anthocyanin accumulation compared to control plants. TgMYB4 was found to inhibit the transcription of ANTHOCYANIDIN SYNTHASE (TfANS1) and DIHYDRO-FLAVONOL-4-REDUCTASE (AtDFR), although it did not bind to their promoters. Moreover, the TgMYB4 protein was able to compete with the MYB activator to bind to the :bHLHprotein, thereby suppressing the function of the activator MBW complex. These findings demonstrate that TgMYB4 plays a suppressive role in the regulation of anthocyanin synthesis during flower pigmentation.

Efficient evacuation route planning during underground coal mine fires is essential to minimize casualties. This study addresses current shortcomings by proposing a real-time method that integrates a multifactor coupling analysis and the optimized multilayer perceptron regressor-shortest path faster algorithm (MSPFA). This research aims to enhance evacuation route planning by overcoming factors such as inadequate consideration, low accuracy, and information lag in existing methods. This study improves the shortest path faster algorithm (SPFA) for dynamic route planning, mitigates the impact of fixed walking speed parameters using the particle swarm algorithm, and selects the optimal model (MLPRegressor) through the Bootstrap algorithm for estimating personnel walking speeds. Validated through smoke-spread experiments, the MSPFA algorithm dynamically adjusts evacuation routes, preventing toxic passages. Visualization via drawing interchange format (DXF) successfully enhances route comprehension. The MSPFA algorithm outperforms the Dijkstra algorithm with a runtime of 78.5 msand a personnel evacuation time of 3344.74 s. This research establishes a theoretical foundation for intelligent evacuation decision making in underground fire disasters. By introducing the MSPFA algorithm, it provides crucial technical support, significantly reducing the risk of casualties during emergencies.

Magnetic fields play an important role in regulating plant growth and development, especially in improving plant stress tolerance. However, the physiological mechanism underlying the magnetic effects is still unclear. Here, we examined changes in reactive oxygen species (ROS) levels and ion flux in poplar (Populus × deltoides ‘Lulin-2’) seedling roots under salt stress in a static magnetic field (SMF). SMF treatment significantly increased seedling growth and mitigated the effects of salt stress on root growth. Furthermore, SMF treatment activated ROS and calcium signals in poplar roots. Relative to the SMF treatment group, control plants had significantly higher levels of cytoplasmic free Ca2+ ([Ca2+]cyt) and ROS following exposure to high salt concentrations. Under salt conditions, SMF treatment reduced increases in Na+ concentrations and maintained stable K+ and Ca2+ concentrations and K+/Na+ and Ca2+/Na+ ratios. NMT analysis suggests that SMF treatment may drive cation effluxes in poplar seedling roots. Susceptibility tests of Na+-transport inhibitors indicated that SMF treatment contributed to Na+ repulsion and H+ uptake under salt stress. Moreover, SMF exposure allowed roots to retain the ability to reduce salt-induced K+ and Ca2+ root effluxes, and qRT-PCR results demonstrate that SMF treatment can increase the expression of stress-responsive genes such as PtrRBOHF, PtrNHX1 and PtrHA5 in poplar seedlings. Therefore, we conclude that treating poplar seedlings with SMF can help them establish a stable tolerance to salt stress by regulating ROS, [Ca2+]cyt, and their regulatory networks. This study examined the physiological responses of poplar to SMF exposure under salt stress, providing insights into plant magnetobiological effects.

The promotion of industrial-mode production of Codonopsis pilosula Nannf. var. modesta (Nannf.) L. T. Shen (C. pilosula) has expanded the demand for the postharvest storage of fresh roots. Further research is needed to establish comprehensive methods to evaluate the impact of storage conditions. This study simulated the storage process of roots at near-freezing temperature [NFT (−1 °C)] and traditional low temperatures (−6 °C, 4 °C and 9 °C) for 40 days. At different storage stages, correlation analysis was conducted using quantitative data on 20 parameters, including sensory quality, active components, antioxidant capacity and physiological changes. Appearance and principal component analysis could distinguish between fresh and stored samples, while NFT samples on the 40th day of storage were similar to fresh ones. Correlation analysis indicated that NFT storage could maintain the sensory quality by increasing the antioxidant enzyme activity and active components, reducing the accumulation of reactive oxygen species and malondialdehyde and reducing the activity of browning-related enzymes and cell-wall-degrading enzymes. These findings highlight the importance of the overall quality evaluation of fresh roots and emphasize the potential to improve fresh root and dried medicinal material quality by regulating storage conditions such as temperature.

Millimeter-wave radar and optical cameras are one of the primary sensing combinations for autonomous platforms such as self-driving vehicles and disaster monitoring robots. The millimeter-wave radar odometry can perform self-pose estimation and environmental mapping. However, cumulative errors can arise during extended measurement periods. In particular scenes where loop closure conditions are absent and visual geometric features are discontinuous, existing loop detection methods based on back-end optimization face challenges. To address this issue, this study introduces a correlative scan matching (CSM) pose estimation method that integrates visual and radar line features (VRL-SLAM). By making use of the pose output and the occupied grid map generated by the front end of the millimeter-wave radar’s simultaneous localization and mapping (SLAM), it compensates for accumulated errors by matching discontinuous visual line features and radar line features. Firstly, a pose estimation framework that integrates visual and radar line features was proposed to reduce the accumulated errors generated by the odometer. Secondly, an adaptive Hough transform line detection method (A-Hough) based on the projection of the prior radar grid map was introduced, eliminating interference from non-matching lines, enhancing the accuracy of line feature matching, and establishing a collection of visual line features. Furthermore, a Gaussian mixture model clustering method based on radar cross-section (RCS) was proposed, reducing the impact of radar clutter points online feature matching. Lastly, actual data from two scenes were collected to compare the algorithm proposed in this study with the CSM algorithm and RI-SLAM.. The results demonstrated a reduction in long-term accumulated errors, verifying the effectiveness of the method.

Synthetic Aperture Radar (SAR) image change detection aims to detect changes with images of the same area acquired at different times. It has wide applications in environmental monitoring, urban planning and resource management. Traditional change detection methods for spaceborne SAR time-series images typically adopt a pairwise comparison strategy to obtain multi-temporal change information. However, this kind of method has the problem of losing the overall change information, which is time consuming. To address this problem, this paper proposes a new change detection algorithm for spaceborne SAR time-series data based on SAR-SIFT-Logarithm Background Subtraction. This algorithm combines the SAR-SIFT image registration technology with Logarithm Background Subtraction. The method first preprocesses the input time-series data with steps like noise reducing and radiometric calibration. Then, the images will be coregistered by the SAR-SIFT step to avoid mismatches-induced detection performance degradation. Next, the parts that remained unchanged throughout the time period are modeled with a median filter to obtain the static background. The change information is then obtained via the subtraction of background and CFAR detection and clustering. The proposed algorithm is validated using the Sentinel-1 GRD and PAZ-1 time-series dataset. Experimental results demonstrate that the proposed method effectively detects the overall change information and reduces processing time compared to traditional pairwise comparison methods.

Ground-based synthetic aperture radar (GBSAR) is widely used in mountains, mines, and other areas because it can get the sub-millimeter deformation information of monitoring scenes. This technology plays a vital role in safeguarding production operations, providing accurate disaster projections, and facilitating timely early warning dissemination. However, the moving target’s defocus/displaced signal will mask the image of GBSAR, which affects the accuracy of deformation inversion. Hence, the detection of moving targets in GBSAR imagery is essential. An algorithm for moving target detection based on refocusing is proposed in this paper to address this problem. The algorithm establishes a two-dimensional parameter search space for squint angle and relative speed. Based on the parameter searching, the improved Range Doppler (RD) algorithm is used for refocusing. The optimal 2D parameters are searched via an algorithm combining the entropy minimization principle and the enlightend search. The presence of a moving target in the observation area is determined based on whether there is an optimal parameter to minimize the entropy value of the refocused image. This approach enables the detection of moving targets in GBSAR imagery. The proposed method is verified by the synthetic data.

In-line X-ray phase contrast imaging, which is simple to experiment with, provides significantly higher sensitivity, compared to conventional X-ray absorption imaging. The inversion of the relationship between recorded Fresnel diffraction intensity and the phase shift induced by the object is called phase retrieval. The transport of intensity equation (TIE), a simple method of phase retrieval, which is solved by the fast Fourier transform algorithm proposed by Paganin et al., has been widely adopted. However, the existing method suffers from excessive suppression of high-frequency information, resulting in loss of image details after phase retrieval, or insufficient detail contrast, leading to blurry images. Here, we present a straightforward extension of the two-distance FFT-TIE method by modifying the Fourier filter through the use of a five-point approximation to calculate the inverse Laplacian in a discrete manner. Additionally, we utilize a combination of continuous Fourier transform and a four-point approximation to compute the gradient operator. The method is evaluated by simulating samples with a shape similar to the resolution test map and by using a photograph of a dog for further evaluation. The algorithm that incorporates the modified gradient operator and the algorithm that solely utilizes the continuous Fourier transform for gradient computation were compared with the results obtained using the two-distance FFT-TIE method. The comparisons were conducted using the results obtained from two distances from the sample to the detector. The results show that this method improves the contrast of spatial details and reduces the suppression of high spatial frequencies compared to the two-distance FFT-TIE method. Furthermore, in the low-frequency domain, our algorithm does not lose much information compared to the original method, yielding consistent results. Furthermore, we conducted our experiments using carbon rods. The results show that both our method and the FFT-TIE method exhibit low-frequency distortion due to the requirement of close proximity between the absorption maps and the detector. However, upon closer inspection, our proposed method demonstrates superior accuracy in reproducing the finer details of the carbon rod fibers.

With the rapid advancement of network communication and big data technologies, the Internet of Things (IoT) has permeated every facet of our lives. Meanwhile, the interconnected IoT devices have generated a substantial volume of data, which possess both economic and strategic value. However, owing to the inherently open nature of IoT environments and the limited capabilities and the distributed deployment of IoT devices, traditional access control methods fall short in addressing the challenges of secure IoT data management. On the one hand, the single point of failure issue is inevitable for the centralized access control schemes. On the other hand, most decentralized access control schemes still face problems such as token underutilization, the insecure distribution of user permissions, and inefficiency.This paper introduces a blockchain-based access control framework to address these challenges. Specifically, the proposed framework enables data owners to host their data and achieves user-defined lightweight data management. Additionally, through the strategic amalgamation of smart contracts and hash-chains, our access control scheme can limit the number of times (i.e., n-times access) a user can access the IoT data before the deadline. This also means that users can utilize their tokens multiple times (predefined by the data owner) within the deadline, thereby improving token utilization while ensuring strict access control. Furthermore, by leveraging the intrinsic characteristics of blockchain, our framework allows data owners to gain capabilities for auditing the access records of their data and verifying them. To empirically validate the effectiveness of our proposed framework and approach, we conducted extensive simulations, and the experimental results demonstrated the feasibility and efficiency of our solution.

The electromagnetic wave transmitted by the millimeter-wave radar can penetrate flames, smoke, and the high-temperature field, and is the main sensor for detecting disaster victims in closed spaces. However, a moving target in the closed space will produce a considerable number of false detections in the point cloud data collected by the radar due to multipath scattering. The false detections lead to false trajectories generated by multi-target tracking filters, such as the labeled multi-Bernoulli (LMB) filter, which, therefore, leads to inaccurate estimation of the number of pedestrians. Addressing this problem, in this paper, a three-class combination of the clutter point clouds model is proposed: static clutter, non-continuous dynamic clutter (NCDC), and continuous dynamic clutter (CDC). The model is based on the spatial and temporal distribution characteristics of the CDC sequence captured by a two-dimensional (2D) millimeter-wave (MMW) radar. However, in open space, CDC appears infrequently in radar tracking applications, and thus has not been considered in multi-target tracking filters such as the LMB filter. This leads to confusion between the CDC point cloud collected by the high-resolution radar in closed spaces and the real-target point cloud. To solve this problem, the impact mechanism of the LMB filter on prediction, update, and state estimation is modeled in this paper in different stages based on the temporal and spatial distribution characteristics of CDC. Finally, a hybrid clutter model-based LMB filter (HCM-LMB) is proposed, which focuses on scenes where NCDC and CDC are mixed. The filter introduces the temporal and spatial distribution characteristics of NCDC based on the original LMB filter, and improves the prediction, update, and state estimation of the original filter by combining the impact mechanism model and the new CDC prediction, CDC estimation, and false trajectory label management algorithm. Experiments were conducted on pedestrians in building corridors using 2D MMW radar perception. The experimental results show that under the influence of CDC, the total number of pedestrians estimated by the HCM-LMB filter was reduced by 22.5% compared with that estimated by the LMB filter.

Intestinal microbiota has an important impact on pig phenotypes. Previous studies mainly focused on the microbiota of feces and worldwide farmed commercial pigs, while research on the microbiota of various intestinal sections and indigenous pig breeds is very limited. This study aimed to characterize and compare the biogeography of intestinal microbiota in pigs of one Chinese indigenous breed and one commercial crossbred. In this study, we sequenced the microbiota of six intestinal segments in the grown-up pigs of a Chinese indigenous breed, Laiwu pigs, and the worldwide farmed crossbred Duroc × Landrace × Yorkshire (DLY) pigs by 16S rRNA sequencing, characterized the biogeography of intestinal microbiota, and compared the compositional and functional differences between the two breeds. The results showed that there were obvious differences in microbial structure and abundance between the small and large intestines. Laiwu pigs had higher large intestinal diversity than DLY pigs, while DLY pigs had higher small intestinal diversity than Laiwu pigs. Moreover, some specific bacterial taxa and Kyoto Encyclopedia of Genes and Genomes pathways were found to be related to the high fat deposition and good meat quality of Laiwu pigs and the high growth speed and lean meat rate of DLY pigs. This study provides an insight into the shifts in taxonomic composition, microbial diversity, and functional profile of intestinal microbiota in six intestinal segments of Laiwu and DLY pigs, which would be essential for exploring the potential influence of the host’s genetic background on variation in microbiota composition and diversity.

Cord steel is used for making tire frames and wire saws for cutting silicon wafers. The diameter of mainstream cutting wire has been developed to be lower than 100 μm. The size and deformation ability of inclusions are very important to the wire breaking rate of cord steel during the drawing process. In order to improve the deformation ability of the inclusions in cord steel, alkali metal oxide was added into the molten steel to improve the inclusions in the steel so as to obtain good, plastic, low-melting-point inclusions. Mass fractions of 0.3%, 0.5% and 1.0% K2CO3, Na2CO3 and B2O3 were added into cord steel, which were melted in 10 furnaces (including 0% alkali metal oxides, mass fractions of 0.3%/0.5%/1.0% K2CO3, Na2CO3 and B2O3). The morphology and composition of inclusions were observed by SEM-EDS. Factsage phase diagram calculations and experimental results show that, with the increase in Na2CO3 content in cord steel, the aluminum content in the inclusions gradually decreased. When the mass fraction of Na2CO3 was 0.5% per ton, most of the inclusions in the steel fell in the low melting point region (less than 1300 °C). With the increase in K2CO3 content in cord steel, the silicon content in the inclusions decreased gradually. When the mass fraction of K2CO3 was 0.5% per ton, most of the inclusions in the steel fell in the low melting point region. The deformation ability of the inclusions added with 0.5% Na2CO3 in the steel during forging was better than that of the inclusions added with 0.5% K2CO3. After adding B2O3, the inclusions in the steel were SiO2-MnO-Al2O3 inclusions or inclusions with SiO2-MnO-Al2O3 as the core and BN wrapped around. Boron could not be dissolved into the inclusions for plastic modification.

Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR.

Radar is a non-contact, high-precision vibration measurement device and an important tool for bridge vibration monitoring. Vibration information needs to be extracted from the radar phase, but the radar phase information is sensitive to noise. Under low signal-to-noise ratio (SNR) data acquisition conditions, such as low radar transmission power or a long observation distance, differential phase jump errors occur and clutter estimation becomes difficult, which leads to inaccurate inversion of vibration deformation. Traditional low-pass filtering methods can filter out noise to improve SNR, but they require oversampling. The sampling rate needs to be several times higher than the Doppler bandwidth, which is several times higher than the vibration frequency. This puts high data acquisition requirements on radar systems and causes large data volumes. Therefore, this paper proposes a novel vibration signal filtering method called the periodic filtering method. The method uses the periodicity feature of vibration signals for filtering without oversampling. This paper derives the time-domain and frequency-domain expressions for the periodic filter and presents a deformation inversion process based on them. The process involves extracting the vibration frequency in the Doppler domain, suppressing noise through periodic filtering, estimating clutter using circle fitting on the data complex plane, and inverting final deformation with differential phase. The method is verified through simulation experiments, calibration experiments, and bridge vibration experiments. The results show that the new periodic filtering method can improve the SNR by five times, resolve differential phase jumps, and accurately estimate clutter, thus getting submillimeter-level vibration deformation at low SNR.

The conventional support forms of foundation pit retaining piles include single-row piles, double-row piles, anchor-row piles, and so on. The double-row pile supporting structure is widely used in the deep foundation pit supporting the engineering of wharves, bridges, subways, tunnels, and high-rise and super-high-rise buildings. This study on double-row pile supporting structures mainly focuses on four aspects: (1) The influence of dimension parameters, such as pile diameter and pile length, and engineering parameters, such as pile spacing and row spacing, on the deformation control of a double-row pile structure and the stability control of foundation pits; (2) Influence of the soil arch effect on the stress and deformation of the double-row pile supporting structure; (3) Study on the deformation characteristics and rules of the components and the whole structure of the double-row pile supporting structure; (4) Study on the calculation model of pile-soil interactions. Based on the above four aspects, this paper summarizes the latest research status of the existing double-row pile supporting structure and its stress and deformation characteristics. The deformation characteristics and calculation model of the pile-soil interaction of double-row piles are reviewed and evaluated. Finally, the problems and deficiencies in the research on double-row pile support are summarized. These results provide a reference for future research on the double-row pile supporting structure of the foundation pit and the numerical analysis and calculation model and lay a solid foundation for further development of the theory.

In autonomous driving, LiDAR (light detection and ranging) data are acquired over time. Most existing 3D object detection algorithms propose the object bounding box by processing each frame of data independently, which ignores the temporal sequence information. However, the temporal sequence information is usually helpful to detect the object with missing shape information due to long distance or occlusion. To address this problem, we propose a temporal sequence information fusion 3D point cloud object detection algorithm based on the Ada-GRU (adaptive gated recurrent unit). In this method, the feature of each frame for the LiDAR point cloud is extracted through the backbone network and is fed to the Ada-GRU together with the hidden features of the previous frames. Compared to the traditional GRU, the Ada-GRU can adjust the gating mechanism adaptively during the training process by introducing the adaptive activation function. The Ada-GRU outputs the temporal sequence fusion features to predict the 3D object in the current frame and transmits the hidden features of the current frame to the next frame. At the same time, the label uncertainty of the distant and occluded objects affects the training effect of the model. For this problem, this paper proposes a probability distribution model of 3D bounding box coordinates based on the Gaussian distribution function and designs the corresponding bounding box loss function to enable the model to learn and estimate the uncertainty of the positioning of the bounding box coordinates, so as to remove the bounding box with large positioning uncertainty in the post-processing stage to reduce the false positive rate. Finally, the experiments show that the methods proposed in this paper improve the accuracy of the object detection without significantly increasing the complexity of the algorithm.

The new mode of Circular Synthetic Aperture Radar (CSAR) has several advantages including multi-aspect and long-time observation, which can generate high-frame-rate image sequences to detect moving targets with a single-channel system. Nonetheless, due to CSAR being sensitive to 3D structures, static high targets are observed in scene display rotational motion within CSAR subaperture image sequences. Such motion can cause false alarms rising when utilizing image sequence-based moving target detection methods like logarithm background subtraction (LBS). To address this issue, this paper first thoroughly analyzes the moving target and static high target’s difference for the trajectory in an image sequence. Two new trajectory features of the rotation angle and moving distance are proposed to differentiate them. Based on the features, a new false alarm suppression method is proposed. The method first utilizes LBS to obtain coarse binary detection results comprising both moving and static high targets, then employs morphological filtering to eliminate noise. Next, DBSCAN and target tracking steps are employed to extract the trajectory features of the target and false alarm. Finally, false alarms are suppressed with trajectory-based feature discriminators to output detection results. The W-band CSAR open dataset is used to validate the proposed method’s effectiveness.

Ground-Based Synthetic Aperture Radar (GBSAR) has non-contact, all-weather, high resolution imaging and microdeformation sensing capabilities, which offers advantages in applications such as building structure monitoring and mine slope deformation retrieval. The Circular Scanning Ground-Based Synthetic Aperture Radar (CS-GBSAR) is one of its newest developed working mode, in which the radar rotates around an axis in a vertical plane. Such nonlinear observation geometry brings the unique advantage of three-dimensional (3D) imaging compared with traditional GBSAR modes. However, such nonlinear observation geometry causes strong sidelobes in SAR images, which makes it a difficult task to extract point cloud data. The Conventional Cell Averaging Constant False Alarm Rate (CA-CFAR) algorithm can extract 3D point cloud data layer-by-layer at different heights, which is time consuming and is easily influenced by strong sidelobes to obtain inaccurate results. To address these problems, this paper proposes a new two-step CFAR-based 3D point cloud extraction method for CS-GBSAR, which can extract accurate 3D point cloud data under the influence of strong sidelobes. It first utilizes maximum projection to obtain three-view images from 3D image data. Then, the first step CA-CFAR is applied to obtain the coarse masks of three-views. Then, the volume mask in the original 3D image is obtained via inverse projection. This can remove strong sidelobes outside the potential target region and obtain potential target area data by intersecting it with the SAR 3D image. Then, the second step CA-CFAR is applied to the potential target area data to obtain 3D point clouds. Finally, to further eliminate the residual strong sidelobes and output accurate 3D point clouds, the modified Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm is applied. The original DBSCAN method uses a spherical template to cluster. It covers more points, which is easily influenced by the strong sidelobe. Hence, the clustering results have more noise points. Meanwhile, modified DBSCAN clusters have a cylindrical template to accommodate the data’s features, which can reduce false clustering. The proposed method is validated via real data acquired by the North China University of Technology (NCUT)-developed CS-GBSAR system. The laser detection and ranging (LiDAR) data are used as the reference ground truth to demonstrate the method. The comparison experiment with conventional method shows that the proposed method can reduce 95.4% false clustered points and remove the strong sidelobes, which shows the better performance of the proposed method.

With the rapid development of deep learning, more and more complex models are applied to 3D point cloud object detection to improve accuracy. In general, the more complex the model, the better the performance and the greater the computational resource consumption it has. However, complex models are incompatible for deployment on edge devices with restricted memory, so accurate and efficient 3D point cloud object detection processing is necessary. Recently, a lightweight model design has been proposed as one type of effective model compression that aims to design more efficient network computing methods. In this paper, a lightweight 3D point cloud object detection network architecture is proposed. The core innovation of the proposal consists of a lightweight 3D sparse convolution layer module (LW-Sconv module) and knowledge distillation loss. Firstly, in the LW-Sconv module, factorized convolution and group convolution are applied to the standard 3D sparse convolution layer. As the basic component of the lightweight 3D point cloud object detection network proposed in this paper, the LW-Sconv module greatly reduces the complexity of the network. Then, the knowledge distillation loss is used to guide the training of the lightweight network proposed in this paper to further improve the detection accuracy. Finally, extensive experiments are performed to verify the algorithm proposed in this paper. Compared with the baseline model, the proposed model can reduce the FLOPs and parameters by 3.7 times and 7.9 times, respectively. The lightweight model trained with knowledge distillation loss achieves comparable accuracy to the baseline. Experiments show that the proposed method greatly reduces the model complexity while ensuring detection accuracy.

Mycotoxins, secondary metabolites of fungi, are a major obstacle to the utilization of animal feed for various reasons. Wheat straw (WS) is hollow, and miscellaneous bacteria can easy attach to its surface; the secondary fermentation frequency after silage is high, and there is a risk of mycotoxin poisoning. In this study, a storage fermentation process was used to preserve and enhance fermentation quality in WS through the addition of Artemisia argyi (AA), which is an effective method to use WS resources and enhance aerobic stability. The storage fermentation of WS treated with AA had lower pH and mycotoxin (AFB1 and DON) values than the control due to rapid changes in microbial counts, especially in the 60% AA groups. Meanwhile, the addition of 60% AA improved anaerobic fermentation profiles, showing higher lactic acid contents, leading to increased efficiency of lactic acid fermentation. A background microbial dynamic study indicated that the addition of 60% AA improved the fermentation and aerobic exposure processes, decreased microbial richness, enriched Lactobacillus abundance, and reduced Enterobacter and Aspergillus abundances. In conclusion, 60% AA treatment could improve the quality by increase fermentation quality and improve the aerobic stability of WS silage by enhancing the dominance of desirable Lactobacillus, inhibiting the growth of undesirable microorganisms, especially fungi, and reducing the content of mycotoxins.

Global sea level rise is a major environmental concern for many countries and cities, particularly for low-lying coastal areas where urban development is threatened by the combined effects of sea level rise and land subsidence. This study employed an improved two-layer network Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technology to obtain high-precision land subsidence in Singapore from 2015 to 2019. Landsat images from 1973 to 2020 were also utilized to extract changes in Singapore’s coastline. Geological, topographical, and global sea level rise data were integrated to investigate the causes and impacts of land subsidence in Singapore. The results indicate that the areas with severe subsidence coincide with land reclamation areas, where subsidence is mainly due to soil consolidation. Based on WorldDEM, land subsidence, and sea level rise data, the maximum inundation depth in Singapore by 2050 is estimated to be 1.24 m, with the Marina Bay area in Singapore’s central business district being the most vulnerable to sea level rise. This study provides data support and a scientific basis for understanding the impact of land subsidence on Singapore’s coastal areas under the influence of multiple factors using advanced InSAR technology.

According to the demand for high-performance fibers for high-latitude ocean exploration and development, this paper selects representative products of high-performance liquid crystal fibers: thermotropic liquid crystal polymer fibers (TLCP) and poly p-phenylene terephthalamide (PPTA) fibers. Through a series of freeze–thaw (F–T) experiments for simulating a real, cold marine environment, we then measure the retention of mechanical properties of these two kinds of fibers. Before that, due to the difference in their chemical structures, we tested their Yang–Laplace contact angle (YLCA) and water absorption; the results suggested that PPTA fibers would absorb more moisture. Surprisingly, then, compared with thermotropic liquid crystal polymer (TLCP) fibers, the retention of the mechanical properties of poly p-phenylene terephthalamide (PPTA) fibers decreased by around 25% after the F–T experiments. The Fourier-transformed infrared (FT-IR) analysis, the attenuated total reflection (ATR) accessory analysis and the degree of crystal orientation measured by two-dimensional wide-angle X-ray diffraction (2D-WAXD) confirm that no changes in the chemical and the orientation structure of the crystal region of the fibers occurred after they underwent the F–T cycles. However, as observed by scanning electron microscopy (SEM), there are microcracks of various extents on the surface of the PPTA fibers, but they do not appear on the surface of TLCP fibers. It is obvious that these microcracks will lead to the loss of mechanical properties; we infer that the moisture absorbed by the PPTA fibers freezes below the freezing point, and the volume expansion of the ice causes the collapse of the microfibrillar structure. The two sorts of fibers subjected to the F–T experiments are immersed in a sodium chloride solution, and the amount of water infiltrated into the PPTA microfibrillar structure is evaluated according to the content of sodium ions in the fiber surface and subsurface layers through X-ray spectroscopy (EDS) elemental analysis. From the above analysis, we found that TLCP fibers can more effectively meet the operating standards of the severe and cold marine environment.

The single channel Circular Synthetic Aperture Radar (CSAR) has the advantage of continuous surveillance of a fixed scene of interest, which can provide high frame rate image sequences to detect ground moving targets. Recent image-sequence-based CSAR moving target detection methods utilize the fact that the target signal moves fast in the image sequence. Knowledge of the target’s image trace(moving trace in the image sequence, which is equal to a target signature’s morphology in a full aperture CSAR image) can help design better detection methods. However, previous signature morphology studies are based on linear track geometry assumptions, which cannot handle CSAR’s nonlinear track. Hence, this paper proposes a new image trace method based on the range-Doppler principle. The proposed method can deduct the exact analytic function of an arbitrary moving target’s image trace in CSAR. The method assumes radar operates in side-looking mode and that the target moves on the ground plane. It combines the range-Doppler equations(i.e., iso-range and iso-Doppler relation) and Cartesian transformation between the ground and radar coordinate system to obtain the parametric functions of the image trace. Based on the method, three types of target motion (including linear and nonlinear motion) are analyzed. The proposed method is validated with both simulated and real data.

To evaluate the fermentation properties and bacterial community composition of mulberry leaves when ensiled with smooth bromegrass, and the effects of Lactobacillus plantarum inoculation on the mixed silage of mulberry leaves and smooth bromegrass, mulberry leaves were mixed with smooth bromegrass at ratios of 100:0, 90:10, 80:20, 70:30 and 60:40, and ensiled for 60 d with and without L. plantarum inoculant. The results showed that the sole fermentation of mulberry leaves failed to achieve optimum fermentation quality. Silage with a mulberry leaf ratio of 80% performed better fermentation quality compared with other non-inoculated groups, indicated by lower pH value, adequate lactic acid accumulation, and enriched proportion of Lactobacillus in the bacterial community. L. plantarum inoculation dramatically improved fermentation quality of mulberry leaf silage compared with the non-inoculated control. However, the fermentation quality of the inoculated silage decreased along with the reduction in the ratio of mulberry leaves. In conclusion, L. plantarum inoculation has the capability to improve the silage quality of mulberry leaves. Combined ensiling with smooth bromegrass could also aid in improving silage quality of mulberry leaves, with the optimum ratio of mulberry leaves being 80%.

This study assessed the effects of Lactiplantibacillus plantarum (ZZU203), cellulase-producing Bacillus methylotrophicus (CB), or their combination (ZZU203_CB) on the fermentation parameters of alfalfa after 10 and 60 days of ensiling. Additionally, the bacterial community compositions were analyzed using absolute quantification 16S-seq (AQS). The results showed that CB silage displayed a higher lactic acid (LA) concentration at 10 d, a higher abundance of Lactobacillus, and lower abundance of Pediococcus, Enterococcus, and Weissella than those in the control (CK) silage. Compared with CK silage, the ZZU203 silage increased LA concentration, fructose and rhamnose concentrations, and the abundance of Lactobacillus, and decreased pH value, ammoniacal nitrogen, acetic acid, neutral detergent fiber and acid detergent fiber concentrations, and the abundance of Pediococcus, Enterococcus, Weissella, Hafnia, and Garciella after 60 days of ensiling. In addition, ZZU203 and ZZU203_CB silage had a similar silage quality and bacterial community, while the inoculation of ZZU203_CB significantly promoted LA accumulation and the numbers of Lactobacillus at 10 d compared with ZZU203 silage. Therefore, ZZU203 or a combination of ZZU203 and CB can be used as potential silage additives to improve the silage quality of alfalfa.

Built-up areas and buildings are two main targets in remote sensing research; consequently, automatic extraction of built-up areas and buildings has attracted extensive attention. This task is usually difficult because of boundary blur, object occlusion, and intra-class inconsistency. In this paper, we propose the multi-attention feature fusion HRNet, MAFF-HRNet, which can retain more detailed features to achieve accurate semantic segmentation. The design of a pyramidal feature attention (PFA) hierarchy enhances the multilevel semantic representation of the model. In addition, we develop a mixed convolutional attention (MCA) block, which increases the capture range of receptive fields and overcomes the problem of intra-class inconsistency. To alleviate interference due to occlusion, a multiscale attention feature aggregation (MAFA) block is also proposed to enhance the restoration of the final prediction map. Our approach was systematically tested on the WHU (Wuhan University) Building Dataset and the Massachusetts Buildings Dataset. Compared with other advanced semantic segmentation models, our model achieved the best IoU results of 91.69% and 68.32%, respectively. To further evaluate the application significance of the proposed model, we migrated a pretrained model based on the World-Cover Dataset training to the Gaofen 16 m dataset for testing. Quantitative and qualitative experiments show that our model can accurately segment buildings and built-up areas from remote sensing images.

The BES1 (BRI1-EMSSUPPRESSOR1) gene family play a vital role in the BR (brassinosteroid) signaling pathway, which is involved in the growth and development, biotic, abiotic, and hormone stress response in many plants. However, there are few reports of BES1 in Cucurbita moschata. In this study, 50 BES1 genes were identified in six Cucurbitaceae species by genome-wide analysis, which could be classified into 3 groups according to their gene structural features and motif compositions, and 13 CmoBES1 genes in Cucurbita moschata were mapped on 10 chromosomes. Quantitative real-time PCR analysis showed that the CmoBES1 genes displayed differential expression under different abiotic stress and hormone treatments. Subcellular localization showed that the most of CmoBES1 proteins localized in nucleus and cytoplasm, and transactivation assay indicated 9 CmoBES1 proteins played roles as transcription factors. Our analysis of BES1s diversity, localization, and expression in Curcubitaceae contributes to the better understanding of the essential roles of these transcription factors in plants.

In the field of autonomous driving, millimeter-wave (MMW) radar is often used as a supplement sensor of other types of sensors, such as optics, in severe weather conditions to provide target-detection services for autonomous driving. RODNet (A Real-Time Radar Object-Detection Network) is one of the most widely used MMW radar range–azimuth (RA) image sequence target-detection algorithms based on Convolutional Neural Networks (CNNs). However, RODNet adopts an object-location similarity (OLS) detection method that is independent of the number of targets to obtain the final target detections from the predicted confidence map. Therefore, it gives a poor performance on missed detection ratio in dense pedestrian scenes. Based on the analysis of the predicted confidence map distribution characteristics, we propose a new generative model-based target-location detection algorithm to improve the performance of RODNet in dense pedestrian scenes. The confidence value and space distribution predicted by RODNet are analyzed in this paper. It shows that the space distribution is more robust than the value distribution for clustering. This is useful in selecting a clustering method to estimate the clustering centers of multiple targets in close range under the effects of distributed target and radar measurement variance and multipath scattering. Another key idea of this algorithm is the derivation of a Gaussian Mixture Model with target number (GMM-TN) for generating the likelihood probability distributions of different target number assumptions. Furthermore, a minimum Kullback–Leibler (KL) divergence target number estimation scheme is proposed combined with K-means clustering and a GMM-TN model. Through the CRUW dataset, the target-detection experiment on a dense pedestrian scene is carried out, and the confidence distribution under typical hidden variable conditions is analyzed. The effectiveness of the improved algorithm is verified: the Average Precision (AP) is improved by 29% and the Average Recall (AR) is improved by 36%.

Millimeter wave (MMW) radar simultaneous localization and mapping (SLAM) technology is an emerging technology in a tunnel vehicle accident rescue scene. It is a powerful tool for statistic-trapped vehicle detection with limited vision caused by darkness, heat, and smoke. A variety of SLAM frameworks have been proven to be able to obtain 3-degree-of-freedom (3-dof) pose estimation results using 2-dimention (2D) MMW radar in open space. In the application of millimeter wave radar for pose estimation and mapping in a closed environment, closed space structures and artificial targets together constitute high-intensity multi-path scattering measurement data, leading to radar false detections. Radar false detections caused by multi-path scattering are generally considered to be detrimental to radar applications, such as multi-target tracking. However, few studies analyze the mechanism of multi-path effects on radar SLAM, especially in closed spaces. In order to address the problem, this paper first presents a radar multi-path scattering theory to study the generation mechanism difference of false and radar true detection and their influences on radar SLAM 2D pose estimation and mapping in tunnel. According to the scattering mechanism differences on SLAM, a radar azimuth scattering angle signature is proposed, which allows distinguishing radar false detections from real ones. This is useful in avoiding using unreliable radar false detections to solve a radar SLAM problem. In addition, two different radar false detection revising methods combined with the CSM (correlative scan matching) algorithm are proposed in this paper. The HTMR-CSM (hard-threshold-multi-path-revised correlative scan matching) algorithm only depends on a hard threshold of radar azimuth scattering angle signature to eliminate all radar false detections as much as possible before CSM. Another idea is the STMR-CSM (soft-threshold-multi-path-revised correlative scan matching) algorithm. All the radar false detections are classified according to the distribution model of radar azimuth accuracy, and part of more reliable radar false detections are retained to estimate a more accurate pose. All the ideas in this paper are validated by using an MMW 2D radar mounted on a rail-guided robot in a tunnel. Two cars on fire were set as the targets. The experimental results show that the STMR-CSM algorithm that keeps the reliable radar false detections improves the positioning accuracy by 20% compared with CSM.

Fabry disease (FD) is a progressive multisystemic disease characterized by lysosomal enzyme deficiency. Enzyme replacement therapy (ERT) is one of the most significant advancements and breakthroughs in treating FD. However, limited resources and the high cost of ERT might prevent patients from receiving prompt and effective therapy, thereby resulting in severe complications. Future progress in ERT can uncover promising treatment options. In this study, we developed and validated a recombinant enzyme (Lanzyme) based on a CHO-S cell system to provide a new potential option for FD therapy. Our results indicated that Lanzyme was heavily glycosylated, and its highest activity was similar to a commercial enzyme (Fabrazyme®). Our pharmacokinetic assessment revealed that the half-life of Lanzyme was up to 11 min, which is nearly twice that of the commercial enzyme. In vivo experiments revealed that Lanzyme treatment sharply decreased the accumulation levels of Gb3 and lyso-Gb3 in various tissues of FD model mice, with superior or comparable therapeutic effects to Fabrazyme®. Based on these data, Lanzyme may represent a new and promising treatment approach for FD. Building this enzyme production system for ERT can offer additional choice, potentially with enhanced efficacy, for the benefit of patients with FD.

Moisture variation is extremely relevant for the stability of ecosystems in Central Asia (CA). Therefore, moisture evolution and its potential driving mechanism over the region are always a hot research topic. Although much effort has been devoted to understanding the processes of moisture evolutions in CA during the Quaternary, particularly the Holocene, the associated underlying mechanisms remain in a state of persistent debate. In this study, the granulometry, clay mineral and chroma properties of a loess section (named ZSP section) in the westerlies-dominated Ili Basin, NW China are investigated. With the accelerator mass spectrometry radiocarbon dating (AMS 14C)-based Bayesian age–depth model, we provide a sensitive record of effective moisture evolution since the last glacial maximum (LGM) in the basin, and the results help enhance understanding of the possible driving mechanisms for westerly climate change. Comparisons of clay mineralogy indices shows that the study area is involved in the Northern Hemisphere dust cycle processes as a dust source, and the content of

Micro-sprinkling irrigation is a small-flow irrigation technology that uses the grouped outlets on the micro-sprinkling hoses to spray the pressure water evenly in the field. Plants’ barriers during the middle to late growth period of winter wheat significantly reduce the irrigation quality of the micro-spray system. It is still unclear whether soil border width in wheat fields can alleviate the negative effect. In this study, a popularly-used variety (c.v. ZM 369) was adopted to test the mitigation effect of soil borders on irrigation quality, as well as soil moisture distribution, in wheat fields. Two irrigation quotas (i.e., 75 mm and 45 mm per time) and three border widths (i.e., 2.3 m, 3.3 m, and 5.3 m) were arranged in a randomized block design in the experimental years of 2020–2022. Soil moisture distribution and irrigation quality during the middle to late growth period of winter wheat (i.e., jointing to heading stage and grain filling stage) were investigated, as well as the effects on grain yield and water use efficiency (WUE). The results showed that irrigation water distribution in the direction perpendicular to micro-spray tapes generally decreased with the distance from tapes increasing. The maximum difference between the irrigation amount and water collected under the canopy was 134 mm. The uniformity coefficient of soil moisture distribution was increased by 25.8% with a 5.3 m border width compared to a 2.3 m width. Although an irrigation quota of 75 mm was beneficial for ensuring better irrigation uniformity and more stable grain yield, grain yield and WUE were produced with an irrigation quota of 45 mm. In conclusion, it is appropriate to increase border width and adopt a small quota for the micro-spray system in the North China Plain for wheat crops.

In temperate and subtropical climates, Populus deltoides is an important poplar species. To analyze the stability, adaptability, and high yield of P. deltoides hybrid genotypes in East China, 38 P. deltoides genotypes were screened at three experimental sites in Shandong Province, and the coefficients of variation and genetic characteristics of 5-year growth were examined using BLUP-GGE. Results showed that all coefficients of variance were higher for diameter at breast height (DBH) than for tree height at all sites; the repeatability and heritability of tree height were greater than those of DBH at all sites. The fitting degree of tree height and DBH was 93.72% and 92.22%, respectively. The optimum genotype varied by location and grouping, of which Shanxian County divisions are the most powerful and representative of the three sites. The growth of 5-year-old P. deltoides was highly varied because of hereditary factors. Shanxian County is the more appropriate site for growth of P. deltoides in the tested sites in East China. In East China, genotypes 16, 5, 21, and 7 are regarded as superior. The genotypes 8, 26, and 36 are better for the site Rizhao; 16, 5, and 21 for site Shanxian; and 22, 21, and 5 for site Boxing.