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This paper presents a modal analysis conducted under seabed dynamic pressure conditions. The polyethylene (PE) pipeline was approximated as a thin-walled long cylindrical shell. The impact of pressure on the pipeline structure was considered, the study investigated the circumferential dynamic characteristics. The natural characteristics of the structure were solved using the energy method. To validate the model, an experimental testing system was established to measure the natural frequencies of the polyethylene (PE) pipeline, capturing the first three natural frequencies and mode shapes of the structure. Additionally, we conduct a simulation analysis in COMSOL. The results showed that the discrepancies among the experimental, theoretical, and simulation results were within 6%, confirming the accuracy of the model. Building on this foundation, the simulation considered the impact of fluid-structure interaction on the natural frequencies of the cylindrical shell. Subsequently, the effects of pipe radius, pipe thickness, pipe length, variations in external load, and dimensionless parameters on the modal characteristics of the structure were further investigated. The comparison of simulation and theoretical results showed errors within 5%, further validating the reliability of the simulation outcomes.

This study presents a location method for buried polyethylene (PE) pipelines based on the double-tree complex wavelet cross-correlation delay. Initially, the dual-tree complex wavelet transform (DTCWT) is applied to denoise the acquired signal, followed by extracting the delay time through the cross-correlation function to locate the buried pipeline. A simulation model is established to analyze the peak values of the time-domain signals in both asymmetric and symmetric sensor layouts using COMSOL, determining the relationship between the signal time differences and pipeline positions. Then, an experimental test system is set up, and experiments are carried out under the conditions of asymmetric and symmetrical sensors and different excitation points. The results indicate that the maximum error is 4.6% for asymmetric arrangements and less than 1% for symmetric arrangements. In practical applications, the pipeline’s position can be inferred from the delay time, with higher accuracy observed as the excitation point approaches the sensor. This method addresses the limitations of existing pipeline locating techniques and provides a foundation for the development of pipeline positioning technology.

This study proposes a buried PE gas pipeline positioning method based on the elliptical method of an acoustic signal analysis. The cross-correlation time delay positioning technology is combined with the elliptical equation, forming an effective mechanism for pipeline depth positioning. First, a dual-tree complex wavelet transform is employed to denoise the collected signals, enhancing the quality and accuracy of the data. Subsequently, the cross-correlation function is utilized to extract the delay times between the signals. The obtained delay times are then substituted into the elliptical equation to calculate the depth of the buried PE pipeline. Based on this theoretical framework, a simulation model is established in COMSOL, and positioning simulation analyses are conducted under three different conditions: pipeline depth, relative sensor positions, and distances between sensors and excitation points. The simulation results indicate that a clear correlation exists between the signal delay time and the pipeline position, with simulation errors controlled within 5%, thus validating the theoretical feasibility of the method. To further assess the effectiveness of this approach, an experimental testing system is constructed. The experimental study was carried out under four different conditions: pipeline burial depth, relative sensor positions, distances between sensors and excitation points, and excitation frequencies. The experimental results demonstrate that these factors significantly affect the pipeline depth positioning. The comparison results show that the method has a high accuracy in depth positioning, with experimental errors controlled within 10%. This study proves that accurate positioning of pipeline depth could be achieved by substituting signal delay times into the elliptical equation, thereby validating the method’s feasibility in practical applications. The proposed method effectively addressed the shortcomings of existing pipeline depth positioning technologies, providing important theoretical support and a practical reference for future pipeline positioning research.

Exploring synthetic pathways for efficient photocatalysts has always been a major goal in catalysis. The performance of organic photocatalysts is affected by a variety of complex factors, and how to understand the structure–effect relationship is the key to designing efficient photocatalysts. This work explored the feasibility of constructing large-specific-surface-area conjugated microporous polymers (CMPs) based on stereoscopic units like spirobifluorene and achieving efficient photocatalytic activity by modulating the donor–acceptor (D-A) ratio with dibenzothiophene sulfone. Crosslinked pore structures were successfully constructed, and the specific surface area increased with the ratio of spirobifluorene. When the molar ratio of D-A was 1:20, polymer Spso-3 showed the highest photocatalytic hydrogen production activity, at 22.4 mmol h–1 g–1. The findings indicate that constructing D-A type CMPs should be a promising approach to improving the performance of photocatalytic water separation. The appropriate push–pull effect of the D-A structure promotes the photo-induced separation of electron–hole pairs, and the porous structure built on steric units offers ample space for catalytic reactions. This work could provide case references for structural design and the structure–effect relationship of efficient polymer photocatalysts.

Bioavailability is an important indicator for evaluating the nutritional value of protein. In this study, we investigated the effect of ultrasound on improving the bioavailability of daily-consumed dietary proteins (rice, oat, corn, and soy protein). The results indicated that there was a significant difference (P < 0.05) in in vitro simulation digestibility between the ultrasound pretreatment group and the control (without ultrasound pretreatment) group. After ultrasound pretreatment, the digestibility of four proteins increased 9.49% (rice), 9.97% (oat), 8.19% (corn), and 9.84% (soy), respectively. There was a significant difference (P < 0.01) in Caco-2 cell model absorption between the ultrasound pretreatment group and the control group. After ultrasound pretreatment, the Caco-2 cell model absorption increased 5.59% (rice), 4.16% (oat), 8.78% (corn), and 18.69% (soy), respectively. The results of CD (Circular Dichroism) and fluorescence spectroscopy indicated that ultrasound pretreatment changed the protein’s secondary structures and tertiary structure, and more hydrophobic residues were exposed. The results of molecular weight distribution indicated that ultrasound increased the content of 200–1000 Da peptides. The conclusion is that ultrasound pretreatment can effectively improve the bioavailability of these dietary proteins by changing protein structure and composition.

Forest-canopy closure (FCC) reflects the coverage of the forest tree canopy, which is one of the most important indicators of forest structure and a core parameter in forest resources investigation. In recent years, the rapid development of UAV LiDAR and photogrammetry technology has provided effective support for FCC estimation. However, affected by factors such as different tree species and different stand densities, it is difficult to estimate FCC accurately based on the single-tree canopy-contour method in complex forest regions. Thus, this study proposes a method for estimating FCC accurately using algorithm integration with an optimal window size for treetop detection and an optimal algorithm for crown-boundary extraction using UAV LiDAR data in various scenes. The research results show that: (1) The FCC estimation accuracy was improved using the method proposed in this study. The accuracy of FCC in a camphor pine forest (Pinus sylvestris var. mongolica Litv.) was 89.11%, with an improvement of 6.77–11.25% compared to the results obtained from other combined conditions. The FCC accuracy for white birch (White birch platyphylla Suk) was about 87.53%, with an increase of 3.25–8.42%. (2) The size of the window used for treetop detection is closely related to tree species and stand density. With the same forest-stand density, the treetop-detection window size of camphor pine was larger than that of white birch. The optimal window size of camphor pine was between 5 × 5~11 × 11 (corresponding 2.5~5.5 m), while that of white birch was between 3 × 3~7 × 7 (corresponding 1.5~3.5 m). (3) There are significant differences in the optimal-canopy-outline extraction algorithms for different scenarios. With a medium forest-stand density, the marker-controlled watershed (MCW) algorithm has the best tree-crown extraction effect. The region-growing (RG) method has better extraction results in the sparse areas of camphor pine and the dense areas of white birch. The Voronoi tessellation (VT) algorithm is more suitable for the dense areas of camphor pine and the sparse regions of white birch. The method proposed in this study provides a reference for FCC estimation using high-resolution remote-sensing images in complex forest areas containing various scenes.

Atomically precise metal nanoclusters (NCs) emerge as fascinating synthons in self‐assembled materials. The self‐assembly of metal NCs are highly sensitive to the environment because they have an inorganic‐organic hybridized structure and a relatively complicated conformation. Here, it is shown that when confined in crowded colloids, a water‐soluble Ag 9 ‐cored nanocluster (Ag 9 ‐NC) can self‐ assemble into ultra‐long (up to millimeters) and photoluminescent ribbons with high flexibility. The ribbon contains rectangularly organized columns of Ag 9 ‐NCs and can undergo secondary self‐assembly to form bundled and branched structures. Formation of ribbons is observed in all the tested colloids, including lyotropic liquid crystals and disordered, three‐dimensional network. The high viscosity/elasticity of the crowded colloids weakens gravity‐induced sedimentation of the ribbons, leading to the formation of an interesting class of inorganic‐organic composite materials where the hard Ag‐containing skeleton strengthens the soft matter. The simultaneously occurring symmetry breaking during the self‐assembly of Ag 9 ‐NCs gives uncontrolled supramolecular chirality, which can be tuned through the majority rule and soldier‐and‐sergeant rule by the introduction of chiral seeds. The regulated chirality and the intrinsic photoluminescence of the Ag 9 ‐NCs ribbons impart the composite material circularly polarized luminescence, opening the door for a variety of potential applications.

Background Microsatellite markers are one of the most informative and versatile DNA-based markers used in plant genetic research, but their development has traditionally been difficult and costly. The whole genome sequencing with next-generation sequencing (NGS) technologies provides large amounts of sequence data to develop numerous microsatellite markers at whole genome scale. SSR markers have great advantage in cross-species comparisons and allow investigation of karyotype and genome evolution through highly efficient computation approaches such as in silico PCR. Here we described genome wide development and characterization of SSR markers in the watermelon ( Citrullus lanatus ) genome, which were then use in comparative analysis with two other important crop species in the Cucurbitaceae family: cucumber ( Cucumis sativus L.) and melon ( Cucumis melo L.). We further applied these markers in evaluating the genetic diversity and population structure in watermelon germplasm collections. Results A total of 39,523 microsatellite loci were identified from the watermelon draft genome with an overall density of 111 SSRs/Mbp, and 32,869 SSR primers were designed with suitable flanking sequences. The dinucleotide SSRs were the most common type representing 34.09 % of the total SSR loci and the AT-rich motifs were the most abundant in all nucleotide repeat types. In silico PCR analysis identified 832 and 925 SSR markers with each having a single amplicon in the cucumber and melon draft genome, respectively. Comparative analysis with these cross-species SSR markers revealed complicated mosaic patterns of syntenic blocks among the genomes of three species. In addition, genetic diversity analysis of 134 watermelon accessions with 32 highly informative SSR loci placed these lines into two groups with all accessions of C.lanatus var . citorides and three accessions of C. colocynthis clustered in one group and all accessions of C. lanatus var . lanatus and the remaining accessions of C. colocynthis clustered in another group. Furthermore, structure analysis was consistent with the dendrogram indicating the 134 watermelon accessions were classified into two populations. Conclusion The large number of genome wide SSR markers developed herein from the watermelon genome provides a valuable resource for genetic map construction, QTL exploration, map-based gene cloning and marker-assisted selection in watermelon which has a very narrow genetic base and extremely low polymorphism among cultivated lines. Furthermore, the cross-species transferable SSR markers identified herein should also have practical uses in many applications in species of Cucurbitaceae family whose whole genome sequences are not yet available.

Key messageA SNP mutation in CmYGP gene encoding Golden2-like transcription factor is responsible for melon yellow-green plant trait.Chlorophylls are essential and beneficial substances for both plant and human health. Identifying the regulatory network of chlorophyll is necessary to improve the nutritional quality of fruits. At least six etiolation genes have been identified in different melon varieties, but none of them have been cloned, and the molecular mechanisms underlying chlorophyll synthesis and chloroplast development in melon remain unclear. Here, the NSL73046, a yellow-green plant (Cmygp) mutant, enabled the map-based cloning of the first etiolation gene in melon. CmYGP encodes a Golden2-like transcription factor. Spatiotemporal expression analyses confirmed the high CmYGP expression in all green tissues, particularly in young leaves and fruit peels. Virus-induced gene silencing and the development of near-isogenic line by marker-assisted selection further confirmed that downregulation of CmYGP can reduce chloroplast number and chlorophyll content, thereby resulting in yellow-green leaves and fruits in melon, and overexpression of CmYGP in tomatoes also led to dark-green leaves and fruits. RNA-seq analysis revealed that CmYGP greatly affected the expression of key genes associated with chloroplast development. Taken together, these findings demonstrated that CmYGP regulate chlorophyll synthesis and chloroplast development thus affect fruit development in melon. This study also offers a new strategy to enhance fruit quality in melon.

Due to the small size, variety, and high degree of mixing of herbaceous vegetation, remote sensing-based identification of grassland types primarily focuses on extracting major grassland categories, lacking detailed depiction. This limitation significantly hampers the development of effective evaluation and fine supervision for the rational utilization of grassland resources. To address this issue, this study concentrates on the representative grassland of Zhenglan Banner in Inner Mongolia as the study area. It integrates the strengths of Sentinel-1 and Sentinel-2 active-passive synergistic observations and introduces innovative object-oriented techniques for grassland type classification, thereby enhancing the accuracy and refinement of grassland classification. The results demonstrate the following: (1) To meet the supervision requirements of grassland resources, we propose a grassland type classification system based on remote sensing and the vegetation-habitat classification method, specifically applicable to natural grasslands in northern China. (2) By utilizing the high-spatial-resolution Normalized Difference Vegetation Index (NDVI) synthesized through the Spatial and Temporal Non-Local Filter-based Fusion Model (STNLFFM), we are able to capture the NDVI time profiles of grassland types, accurately extract vegetation phenological information within the year, and further enhance the temporal resolution. (3) The integration of multi-seasonal spectral, polarization, and phenological characteristics significantly improves the classification accuracy of grassland types. The overall accuracy reaches 82.61%, with a kappa coefficient of 0.79. Compared to using only multi-seasonal spectral features, the accuracy and kappa coefficient have improved by 15.94% and 0.19, respectively. Notably, the accuracy improvement of the gently sloping steppe is the highest, exceeding 38%. (4) Sandy grassland is the most widespread in the study area, and the growth season of grassland vegetation mainly occurs from May to September. The sandy meadow exhibits a longer growing season compared with typical grassland and meadow, and the distinct differences in phenological characteristics contribute to the accurate identification of various grassland types.