Search Results (21)

To address the adaptability and insufficient imaging quality of conventional in-pipe imaging techniques for irregular pipelines or unstructured scenes, this study proposes a novel radial rotating full-depth-of-field focusing imaging system designed to adapt to the structural complexities of irregular pipelines, which can effectively acquire tiny details with a depth of 300–960 mm inside the pipeline. Firstly, a fast full-depth-of-field imaging method driven by depth features is proposed. Secondly, a full-depth rotating imaging apparatus is developed, incorporating a zoom camera, a miniature servo rotation mechanism, and a control system, enabling 360° multi-view angles and full-depth-of-field focusing imaging. Finally, full-depth-of-field focusing imaging experiments are carried out for pipelines with depth-varying characteristics. The results demonstrate that the imaging device can acquire depth data of the pipeline interior and rapidly obtain high-definition characterization sequence images of the inner pipeline wall. In the depth-of-field segmentation with multiple view angles, the clarity of the fused image is improved by 75.3% relative to a single frame, and the SNR and PSNR reach 6.9 dB and 26.3 dB, respectively. Compared to existing pipeline closed-circuit television (CCTV) and other in-pipeline imaging techniques, the developed rotating imaging system exhibits high integration, faster imaging capabilities, and adaptive capacity. This system provides an adaptive imaging solution for detecting defects on the inner surfaces of irregular pipelines, offering significant potential for practical applications in pipeline inspection and maintenance. Full article

Impulse ultrawideband (UWB) synthetic aperture radar (SAR) combines high-azimuth-range resolution with robust penetration capabilities, making it ideal for applications such as through-wall detection and subsurface imaging. In such systems, the performance of UWB antennas is critical for transmitting high-power, large-bandwidth impulse signals. However, two primary factors degrade radar imaging quality: (1) inherent limitations in antenna radiation efficiency, which lead to low-frequency signal loss and subsequent time-domain ringing artifacts; (2) impedance mismatch at the antenna terminals, causing standing wave reflections that exacerbate the ringing phenomenon. This study systematically analyzes the mechanisms of ringing generation, including its physical origins and mathematical modeling in SAR systems. Building on this analysis, we propose a Bayesian ringing suppression algorithm based on sparse optimization. The method effectively enhances imaging quality while balancing the trade-off between ringing suppression and image fidelity. Validation through numerical simulations and experimental measurements demonstrates significant suppression of time-domain ringing and improved target clarity. The proposed approach holds critical importance for advancing impulse UWB SAR systems, particularly in scenarios requiring high-resolution imaging. Full article

The riparian vegetation buffer zone is an important component of riverbank ecosystems, playing a crucial role in soil consolidation and slope protection. In this study, the riparian vegetation buffer zones in the Tongnan section of the Fujiang River Basin were selected as the research object. Surveys and experiments were conducted to assess the species composition and the soil and water conservation effectiveness of the riparian vegetation buffer zone. There are a total of 35 species, mainly comprising angiosperms and ferns. The dominant species include Cynodon dactylon, Setaria viridis, Phragmites australis, Erigeron canadensis, and Melilotus officinalis. The Patrick richness index (R) and Shannon–Wiener diversity index (H) are more significantly influenced by the types of land use in the surrounding area, whereas the impact on the Simpson diversity index (D) and Pielou uniformity index (E) is comparatively less pronounced. When the root diameter is less than 0.2 mm, the tensile strength of Cynodon dactylon roots is the highest. For root diameters larger than 0.2 mm, Melilotus officinalis roots exhibit the highest tensile strength. The presence of plant root systems significantly reduces erosion, delaying the time to reach maximum erosion depth by 1–4 min, decreasing erosion depth by 9–38 mm, and reducing the total amount of erosion by 20.17–58.90%. The anti-scouribility effect of Cynodon dactylon is significantly better than that of Setaria viridis. The root system notably enhances soil shear strength, delaying the shear peak by 0.26–4.8 cm, increasing the shear peak by 4.76–11.37 kPa, and raising energy consumption by 23.76–46.11%. Phragmites australis has the best resistance to shear, followed by Erigeron canadensis, with Melilotus officinalis being the least resistant. Therefore, to balance the anti-scouribility effect and shear resistance of plant roots, it is recommended to use a combination of Cynodon dactylon and Phragmites australis for shallow-rooted and deep-rooted planting. This approach enhances the water and soil conservation capacity of riverbanks. Full article

The task of camouflaged object segmentation (COS) is a challenging endeavor that entails the identification of objects that closely blend in with their surrounding background. Furthermore, the camouflaged object’s obscure form and its subtle differentiation from the background present significant challenges during the feature extraction phase of the network. In order to extract more comprehensive information, thereby improving the accuracy of COS, we propose a diffusion model for a COS network that utilizes frequency domain information as auxiliary input, and we name it FreDiff. Firstly, we proposed a frequency auxiliary module (FAM) to extract frequency domain features. Then, we designed a Global Fusion Module (GFM) to make FreDiff pay attention to the global features. Finally, we proposed an Upsample Enhancement Module (UEM) to enhance the detailed information of the features and perform upsampling before inputting them into the diffusion model. Additionally, taking into account the specific characteristics of COS, we develop the specialized training strategy for FreDiff. We compared FreDiff with 17 COS models on the four challenging COS datasets. Experimental results showed that FreDiff outperforms or is consistent with other state-of-the-art methods under five evaluation metrics. Full article

Camouflaged object detection (COD) is a challenging task that involves identifying objects that closely resemble their background. In order to detect camouflaged objects more accurately, we propose a diffusion model for the COD network called DMNet. DMNet formulates COD as a denoising diffusion process from noisy boxes to prediction boxes. During the training stage, random boxes diffuse from ground-truth boxes, and DMNet learns to reverse this process. In the sampling stage, DMNet progressively refines random boxes to prediction boxes. In addition, due to the camouflaged object’s blurred appearance and the low contrast between it and the background, the feature extraction stage of the network is challenging. Firstly, we proposed a parallel fusion module (PFM) to enhance the information extracted from the backbone. Then, we designed a progressive feature pyramid network (PFPN) for feature fusion, in which the upsample adaptive spatial fusion module (UAF) balances the different feature information by assigning weights to different layers. Finally, a location refinement module (LRM) is constructed to make DMNet pay attention to the boundary details. We compared DMNet with other classical object-detection models on the COD10K dataset. Experimental results indicated that DMNet outperformed others, achieving optimal effects across six evaluation metrics and significantly enhancing detection accuracy. Full article

This study investigated the transcriptomic mechanisms underlying melatonin accumulation and the enhancement of salt tolerance in hull-less barley seeds subjected to zinc sulphate stress. Following zinc sulphate treatment, hull-less barley seeds demonstrated increased melatonin accumulation and improved salt tolerance. Through transcriptome analysis, the study compared gene expression alterations in seeds (using the first letter of seed, this group is marked as ‘S’), seeds treated with pure water (as the control group, is marked as ‘C’), and germinated seeds exposed to varying concentrations of zinc sulphate (0.2 mM and 0.8 mM, the first letter of zinc sulphate, ‘Z’, is used to mark groups ‘Z1’ and ‘Z2’). The analysis revealed that 8176, 759, and 622 differentially expressed genes (DEGs) were identified in the three comparison groups S.vs.C, C.vs.Z1, and C.vs.Z2, respectively. Most of the DEGs were closely associated with biological processes, including oxidative-stress response, secondary metabolite biosynthesis, and plant hormone signaling. Notably, zinc sulphate stress influenced the expression levels of Tryptophan decarboxylase 1 (TDC1), Acetylserotonin O-methyltransferase 1 (ASMT1), and Serotonin N-acetyltransferase 2 (SNAT2), which are key genes involved in melatonin synthesis. Furthermore, the expression changes of genes such as Probable WRKY transcription factor 75 (WRKY75) and Ethylene-responsive transcription factor ERF13 (EFR13) exhibited a strong correlation with fluctuations in melatonin content. These findings contribute to our understanding of the mechanisms underlying melatonin enrichment in response to zinc sulphate stress. Full article

Kainoid synthases are key enzymes in the biosynthesis of kainoids. Kainoids, as represented by DA and KA, are a class of naturally occurring non-protein amino acids with strong neurotransmitter activity in the mammalian central nervous system. Marine algae kainoid synthases include PnDabC from diatoms, which synthesizes domoic acid (DA), and DsKabC and GfKabC from red algae, which synthesize kainic acid (KA). Elucidation of the catalytic mechanism of kainoid synthases is of great significance for the rational design of better biocatalysts to promote the industrial production of kainoids for use in new drugs. Through modeling, molecular docking, and molecular dynamics simulations, we investigated the conformational dynamics of kainoid synthases. We found that the kainoid synthase complexes showed different stability in the simulation, and the binding and catalytic processes showed significant conformational transformations of kainoid synthase. The residues involved in specific interactions with the substrate contributed to the binding energy throughout the simulation process. Binding energy, the relaxed active pocket, electrostatic potential energy of the active pocket, the number and rotation of aromatic residues interacting with substrates during catalysis, and the number and frequency of hydrogen bonds between the individual functional groups revealed the structure–activity relationships and affected the degree of promiscuity of kainoid synthases. Our research enriches the understanding of the conformational dynamics of kainoid synthases and has potential guiding significance for their rational design. Full article

The symbiotic relationship between arbuscular mycorrhizal fungi (AMF) and plants is well known for its benefits in enhancing plant growth and stress resistance. Research on whether key components of the AMF colonization process, such as MyC factors, can be directly utilized to activate plant symbiotic pathways and key functional gene expression is still lacking. In this paper, we found that, using a hydroponics system with Lotus japonicus, MyC factor analogue chitin oligomer 5 (CO5) had a more pronounced growth-promoting effect compared to symbiosis with AMF at the optimal concentration. Additionally, CO5 significantly enhanced the resistance of Lotus japonicus to various environmental stresses. The addition of CO5 activated symbiosis, nutrient absorption, and stress-related signaling pathways, like AMF symbiosis, and CO5 also activated a higher and more extensive gene expression profile compared to AMF colonization. Overall, the study demonstrated that the addition of MyC factor analogue CO5, by activating relevant pathways, had a superior effect on promoting plant growth and enhancing stress resistance compared to colonization by AMF. These findings suggest that utilizing MyC factor analogues like CO5 could be a promising alternative to traditional AMF colonization methods in enhancing plant growth and stress tolerance in agriculture. Full article

Thiocyanates are effective in inhibiting the growth of microorganisms in raw milk to extend shelf life, but excessive addition can cause human health problems. Currently, ion chromatography and spectrophotometry are the main methods used in industry to determine SCN, but the pre-treatment process is cumbersome and time-consuming and has low sensitivity. Aqueous two-phase flotation (ATPF) technology has the advantages of simplicity, rapidity and economy. In this study, an acetonitrile/ammonium sulfate ATPF–gold nanoparticle (AuNP) colorimetric method was developed for the determination of SCN⁻ in raw milk, and ATPF was used to separate and concentrate SCN⁻ in raw milk to improve the detection sensitivity under convenient and economical conditions. The separation conditions were optimized by single-factor experiments and RSM, while the detection conditions, effects of CTAB concentration, pH and reaction time, were investigated. The “aggregation–anti-aggregation” mechanism of the gold-nano colorimetric method for the determination of SCN⁻ was investigated by UV-vis absorption spectroscopy (UV-vis) and transmission electron microscopy (TEM). Under the optimal separation and detection conditions, the SCN⁻ concentration showed a linear relationship with A630/A520 values in the concentration range of 0–2.5 mg/L with R² of 0.9933, limit of detection (LOD) of 0.0919 mg/L, limit of quantitation (LOQ) of 0.306 mg/L, intra-day precision of 5.3% and spiked recoveries of 80.91–101.25%. In addition, the ATPF-AuNP colorimetric method demonstrated high selectivity and stability. Full article

Geospatial data, especially remote sensing (RS) data, are of significant importance for public services and production activities. Expertise is critical in processing raw data, generating geospatial information, and acquiring domain knowledge and other remote sensing applications. However, existing geospatial service platforms are more oriented towards the professional users in the implementation process and final application. Building appropriate geographic applications for non-professionals remains a challenge. In this study, a geospatial data service architecture is designed that links desktop geographic information system (GIS) software and cloud-based platforms to construct an efficient user collaboration platform. Based on the scalability of the platform, four web apps with different themes are developed. Data in the fields of ecology, oceanography, and geology are uploaded to the platform by the users. In this pilot phase, the gap between non-specialized users and experts is successfully bridged, demonstrating the platform’s powerful interactivity and visualization. The paper finally evaluates the capability of building spatial data infrastructures (SDI) based on GeoNode and discusses the current limitations. The support for three-dimensional data, the improvement of metadata creation and management, and the fostering of an open geo-community are the next steps. Full article

Focusing on the problem of low detection precision caused by the few-shot and multi-scale characteristics of air objects, we propose a few-shot air object detection network (FADNet). We first use a transformer as the backbone network of the model and then build a multi-scale attention mechanism (MAM) to deeply fuse the W- and H-dimension features extracted from the channel dimension and the local and global features extracted from the spatial dimension with the object features to improve the network’s performance when detecting air objects. Second, the neck network is innovated based on the path aggregation network (PANet), resulting in an improved path aggregation network (IPANet). Our proposed network reduces the information lost during feature transfer by introducing a jump connection, utilizes sparse connection convolution, strengthens feature extraction abilities at all scales, and improves the discriminative properties of air object features at all scales. Finally, we propose a multi-scale regional proposal network (MRPN) that can establish multiple RPNs based on the scale types of the output features, utilizing adaptive convolutions to effectively extract object features at each scale and enhancing the ability to process multi-scale information. The experimental results showed that our proposed method exhibits good performance and generalization, especially in the 1-, 2-, 3-, 5-, and 10-shot experiments, with average accuracies of 33.2%, 36.8%, 43.3%, 47.2%, and 60.4%, respectively. The FADNet solves the problems posed by the few-shot characteristics and multi-scale characteristics of air objects, as well as improving the detection capabilities of the air object detection model. Full article

Camouflaged object detection (COD), in a broad sense, aims to detect image objects that have high degrees of similarity to the background. COD is more challenging than conventional object detection because of the high degree of “fusion” between a camouflaged object and the background. In this paper, we focused on the accurate detection of camouflaged objects, conducting an in-depth study on COD and addressing the common detection problems of high miss rates and low confidence levels. We proposed a ternary cascade perception-based method for detecting camouflaged objects and constructed a cascade perception network (CPNet). The innovation lies in the proposed ternary cascade perception module (TCPM), which focuses on extracting the relationship information between features and the spatial information of the camouflaged target and the location information of key points. In addition, a cascade aggregation pyramid (CAP) and a joint loss function have been proposed to recognize camouflaged objects accurately. We conducted comprehensive experiments on the COD10K dataset and compared our proposed approach with other seventeen-object detection models. The experimental results showed that CPNet achieves optimal results in terms of six evaluation metrics, including an average precision (AP)₅₀ that reaches 91.41, an AP₇₅ that improves to 73.04, and significantly higher detection accuracy and confidence. Full article

In recent years, protecting important objects by simulating animal camouflage has been widely employed in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult to achieve than traditional object detection techniques due to the high degree of fusion of objects camouflaged with the background. In this paper, we strive to more accurately and efficiently identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules: the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a particular region. The two sets of output camouflaged object maps were merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, was designed based on two modules to give greater attention to the camouflaged object. Extensive experiments demonstrate that compared with 19 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics in the public COD dataset. Additionally, compared to other COD methods, MAGNet has lower computational complexity and faster segmentation. We also validated the model’s generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explored some extended applications of COD. Full article

Maduramicin ammonium (MAD) is one of the most frequently used anticoccidial agents in broiler chickens. However, the high toxicity and low solubility of MAD limit its clinical application. In this study, MAD-loaded nanostructured lipid carriers (MAD–NLCs) were prepared to overcome the defects of MAD by using highly soluble nanostructured lipid carriers (NLCs). The formulation was optimized via a three-level, three-factor Box–Behnken response surface method. Then, the optimal MAD–NLCs were evaluated according to their hydrodynamic diameter (HD), zeta potential (ZP), crystal structure, encapsulation efficiency (EE), drug loading (DL), in vitro release, and anticoccidial effect. The optimal MAD–NLCs had an HD of 153.6 ± 3.044 nm and a ZP of −41.4 ± 1.10 mV. The X-ray diffraction and Fourier-transform infrared spectroscopy results indicated that the MAD was encapsulated in the NLCs in an amorphous state. The EE and DL were 90.49 ± 1.05% and 2.34 ± 0.04%, respectively, which indicated that the MAD was efficiently encapsulated in the NLCs. In the in vitro study, the MAD–NLCs demonstrated a slow and sustained drug release behavior. Notably, MAD–NLCs had an excellent anticoccidial effect against Eimeria tenella in broiler chickens. In summary, MAD–NLCs have huge potential to form a new preparation administered via drinking water with a powerful anticoccidial effect. Full article