Search Results (1,188)

Small water bodies are widely spread and play crucial roles in supporting regional agricultural and aquaculture activities. PlanetScope imagery has a high resolution (3 m) with daily global coverage and has obviously enhanced small water body mapping. Recent studies have demonstrated the effectiveness of deep learning for mapping small water bodies using PlanetScope; however, a persistent challenge remains in the scarcity of high-quality, manually annotated water masks used for model training, which limits the generalization capability of data-driven deep learning models. In this study, we propose a transfer learning framework that leverages Sentinel-2 data to improve PlanetScope-based small water body mapping, capitalizing on the spectral interoperability between PlanetScope and Sentinel-2 bands and the abundance of open-source Sentinel-2 water masks. Eight state-of-the-art segmentation models have been explored. Additionally, this paper presents the first assessment of the VMamba model for small water body mapping, building on its demonstrated success in segmentation tasks. The models were pre-trained using Sentinel-2-derived water masks and subsequently fine-tuned with a limited set (1292 image patches, 256 × 256 pixels in each patch) of manually annotated PlanetScope labels. Experiments were conducted using 5648 image patches and two areas of 9636 km² and 2745 km², respectively. Among the evaluated methods, VMamba achieved higher accuracy compared with both CNN- and Transformer-based models. This study highlights the efficacy of combining global Sentinel-2 datasets for pre-training with localized fine-tuning, which not only enhances mapping accuracy but also reduces reliance on labor-intensive manual annotation in regional small water body mapping. Full article

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R² values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R² of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection. Full article

Achieving synergies between government-led and market-based policy instruments is critical to advancing Energy–Environmental Productivity and Sustainable Urban Development. This study investigates the effects of China’s dual-pilot energy policies (New Energy Demonstration Cities (NEDCs) and Energy Consumption Permit Trading (ECPT)) on urban environmental productivity (UEP) across 279 prefecture-level cities from 2006 to 2023. Utilizing a Non-Radial Directional Distance Function (NDDF) approach, combined with Difference-in-Differences (DID) estimation and spatial econometric models, the analysis reveals that these synergistic policies significantly enhance both comprehensive and net measures of UEP. Mechanism analysis highlights the roles of industrial restructuring, technological innovation, and energy transition in driving these improvements, while heterogeneity analysis indicates varying effects across different city types. Spatial spillover analysis further demonstrates that policy impacts extend beyond targeted cities, contributing to broader regional gains in UEP. These findings offer important insights for the design of integrated energy and environmental policies and support progress toward key Sustainable Development Goals (SDG 7, SDG 11, and SDG 12). Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

For a vertex subset S of a graph $G,$ if each vertex of G is either in S or adjacent to some vertex in $S,$ then S is a dominating set of $G.$ Let S be a dominating set of a graph $G.$ If each vertex v not in S has a neighbor u in S such that $(S\backslash \{u\left\}\right)\cup \left\{v\right\}$ is also a dominating set of $G,$ then S is a secure dominating set of $G.$ If each vertex u in S has a neighbor v not in S such that $(S\backslash \{u\left\}\right)\cup \left\{v\right\}$ is also a dominating set of G, then S is a co-secure dominating set of G. The minimum cardinality of a secure (resp. co-secure) dominating set of G is the secure (resp. co-secure) domination number of $G.$ Arumugam et al. proposed the questions to characterize a graph G such that the co-secure domination number of G equals the independence number and the secure domination number of G, respectively. Inspired by those questions, in this paper, we obtain two classes of claw-free graphs such that the co-secure domination number equal the independence number and the secure domination number. Our results provide some theoretical basis of claw-free graphs for networks. Full article

Chinese public building carbon emissions trading system (CETS) pilots have employed different carbon quota methods over more than ten years. However, there are few quantitative comparisons on CETS emission reduction effects in different pilots based on the carbon quota analysis. This paper first calculates the annual carbon quotas of public buildings based on carbon quota allocation methodologies from municipal policy documents. Then, the factors affecting the carbon quotas of public buildings are analyzed. Finally, the emission reduction effects are analyzed and compared between the pilots. The findings are concluded as follows: (1) Public building stock area and energy efficiency demonstrate significant effects on the carbon quota. (2) The average annual carbon quota deficits of public buildings were 929,800 tons in Beijing and 596,000 tons in Shanghai, while the carbon quota was an annual surplus of 296,400 tons in Shenzhen, indicating that carbon quota allocations in Beijing and Shanghai pilots are more conducive to promoting the active participation of high-emission enterprises. (3) The emission reduction effect in Beijing is most pronounced, followed by Shanghai and finally Shenzhen. Accordingly, the reasons for the difference in emission reduction effects are analyzed. This study contributes to the carbon quota allocation and emission reduction of public buildings. Full article

Peanut (Arachis hypogaea L.) is a globally important oilseed cash crop, yet its limited genetic diversity and unique reproductive biology present persistent challenges for conventional crossbreeding. Traditional breeding approaches are often time-consuming and inadequate, mitigating the pace of cultivar development. Essential for double fertilization and programmed cell death (PCD), DUF679 membrane proteins (DMPs) represent a membrane protein family unique to plants. In the present study, a comprehensive analysis of the DMP gene family in peanuts was conducted, which included the identification of 21 family members. Based on phylogenetic analysis, these genes were segregated into five distinct clades (I–V), with AhDMP8A, AhDMP8B, AhDMP9A, and AhDMP9B in clade IV exhibiting high homology with known haploid induction genes. These four candidates also displayed significantly elevated expression in floral tissues compared to other organs, supporting their candidacy for haploid induction in peanuts. Subcellular localization prediction, confirmed through co-localization assays, demonstrated that AhDMPs primarily localize to the plasma membrane, consistent with their proposed roles in the reproductive signaling process. Furthermore, chromosomal mapping and synteny analyses revealed that the expansion of the AhDMP gene family is largely driven by whole-genome duplication (WGD) and segmental duplication events, reflecting the evolutionary dynamics of the tetraploid peanut genome. Collectively, these findings establish a foundational understanding of the AhDMP gene family and highlight promising targets for future applications in haploid induction-based breeding strategies in peanuts. Full article

Prolonged expression of gene-editing components in CRISPR-modified plants can interfere with phenotypic analysis of target traits, increase the risk of off-target mutations, and lead to unnecessary metabolic burden. To mitigate these issues in peanut (Arachis hypogaea L.), callus-specific promoters were screened to restrict Cas9 expression to the callus stage, minimizing its activity in regenerated plants. In this study, six callus-specific genes in peanut were identified by mining RNA sequencing datasets and validating their expression profiles using quantitative reverse transcriptase PCR. The promoters of Arahy.H0FE8D, Arahy.WT3AEF, Arahy.I20Q6X, Arahy.ELJ55T, and Arahy.N9CMH4 were cloned and assessed for their expression activity. Beta-glucuronidase (GUS) histochemical staining confirmed that all five promoters were functional in peanut callus. Further investigation revealed their ability to drive cytosine base editing via a deaminase-nCas9 fusion protein, with all promoters successfully inducing precise base substitutions in peanut. Notably, P_Ah-H0FE8D, P_Ah-WT3AEF, P_Ah-ELJ55T, and P_Ah-N9CMH4 exhibited comparable or higher editing efficiencies than the commonly used cauliflower mosaic virus 35S promoter. These findings provide valuable tools for improving the biosafety of CRISPR-based genome editing in peanut breeding programs. Full article

Against the backdrop of ecological civilization construction and regional coordinated development strategies, functional zone (MFOZ) planning guides national spatial development through differentiated policies. However, a prominent conflict exists between the ecological protection responsibilities and regional development rights in restricted and prohibited development zones, leading to a vicious cycle of “ecological protection → restricted development → poverty exacerbation”. This paper focuses on the synergistic mechanisms between ecological compensation and targeted poverty alleviation. Based on the capability approach and sustainable development goals (SDGs), it analyzes the dialectical relationship between the two in terms of goal coupling, institutional design, and practical pathways. The study finds that ecological compensation can break the “ecological poverty trap” through the internalization of externalities and the enhancement of livelihood capabilities. Nevertheless, challenges remain, including low compensation standards, unbalanced benefit distribution, and insufficient legalization. Through case studies of the compensation reform in the water source area of Southern Shaanxi, China, and the Common Agricultural Policy (CAP) of the European Union, this paper proposes the construction of a long-term mechanism integrating differentiated compensation standards, market-based fund integration, legal guarantees, and capability enhancement. The research emphasizes the need for institutional innovation to balance ecological protection and livelihood improvement, promoting a transition from “blood transfusion” compensation to “hematopoietic” development, thereby offering a Chinese solution for global sustainable development. Full article

Hot work die steel is an alloy steel with good high-temperature performance, which is widely used in mechanical manufacturing, aerospace, and other fields. During the working process of hot working mold steel, it is subjected to high temperature, wear, and other effects, which can lead to a decrease in the surface hardness of the mold, accelerate surface damage, shorten the service life, and reduce the quality of the workpiece. In order to improve the wear resistance of the mold, this paper conducts two surface treatments, chrome plating and nitriding, on the surface of hot work mold steel, and compares the high-temperature wear behavior of the materials after the two surface treatments. The results indicate that the hot work die steel obtained higher surface hardness and wear resistance after nitriding surface modification. After nitriding treatment, the surface of hot work die steel contains ε phase (Fe_2–3N), which improves its surface hardness and wear resistance, thus exhibiting better surface hardness and wear resistance than the chrome-plated sample. In this study, the high-temperature wear behavior of hot work die steel after two kinds of surface strengthening treatments was deeply discussed, and the high-temperature wear mechanism of steel after surface strengthening was revealed. It provides a theoretical basis and experimental basis for the surface modification of hot working die steel, and also provides new ideas and methods for improving the service life and workpiece quality of hot working die steel in industrial production. In this study, the advantages and disadvantages of high-temperature wear resistance of hot working die steel after chromium plating and nitriding were systematically compared for the first time, which provided a scientific basis for the selection of surface strengthening technology of hot working die steel and had important academic value and practical application significance. Full article

The “milky disease” in Chinese mitten crabs (Eriocheir sinensis), caused by Metschnikowia bicuspidata, poses significant threats to aquaculture, though its pathogenic mechanisms remain poorly understood. This study employs transcriptomic sequencing to analyze gene expression changes in Metschnikowia bicuspidata under hemocyte challenge, iron overload (1 mmol/mL), and combined stress, with functional validation through Common in Fungal Extracellular Membrane (CFEMgene) overexpression strains. Key findings reveal that (1) hemocyte challenge activated base excision repair (−log₁₀[P] = 7.58) and ribosome biogenesis pathways, indicating fungal adaptation through DNA repair and enhanced protein synthesis to counter host immune attacks (e.g., ROS-mediated damage). (2) Iron overload induced glutathione metabolism and pentose phosphate pathway enrichment, demonstrating mitigation of ferroptosis through NADPH/GSH antioxidant systems and autophagy/proteasome coordination. (3) Under combined stress, ribosome biogenesis (−log₁₀[P] = 1.3) and non-homologous end-joining pathways coordinated DNA repair with stress protein synthesis, complemented by vacuolar V-ATPase-mediated iron compartmentalization. (4) CFEM genes showed significant upregulation under hemocyte stress, with overexpression strains exhibiting enhanced biofilm formation (35% increased MTT cytotoxicity) and infectivity (40% higher infection rate), confirming CFEM domains mediate pathogenesis through iron homeostasis and virulence factor production. This work elucidates how M. bicuspidata employs metabolic reprogramming, oxidative stress responses, and CFEM-mediated iron regulation to establish infection, providing critical insights for developing targeted control strategies against milky disease. Full article

The regulation of the active sites of a catalyst is important for its application. Herein, a series of Ag₁Cu_x/SBA-15 catalysts with different molar ratios of Ag to Cu were synthesized via the impregnation method, and the active sites of Ag₁Cu_x were regulated via various pretreatment conditions. These as-prepared Ag₁Cu_x/SBA-15 catalysts were characterized by many technologies, and their catalytic performance was estimated through CO catalytic oxidation. Among these catalysts, Ag₁Cu_0.025/SBA-15, with a Ag/Cu molar ratio of 1:0.025 and pretreated under the condition of 500 °C O₂/Ar for 2 h, followed by 300 °C H₂ for another 2 h, presented optimal CO degradation performance, which could realize the oxidation of 98% CO at 34 °C (T₉₈ = 34 °C). Meanwhile, Ag₁Cu_0.025/SBA-15 also displayed great reusability. Characterization results, such as X-ray diffraction (XRD), ultraviolet–visible diffuse reflectance spectra (UV-vis DRS), temperature-programmed H₂ reduction (H₂-TPR), and physical adsorption, suggested that the optimal catalytic performance of Ag₁Cu_0.025/SBA-15 was ascribed to its high interspersion of Ag nanoparticles, better low-temperature reduction ability, the interaction between Ag and Cu, and its high surface area and large pore volume. This study provides guidance for the regulation of active sites for low-temperature catalytic degradation. Full article

In this study, the molecular mechanism underlying melanin synthesis within the fermentation broth of Auricularia heimuer (A. heimuer) was explored. The absorbance at 500 nm, melanin yield, and tyrosinase activity in the fermentation broth were assessed across different fermentation durations. Mycelia samples were gathered on the 4th (R1), 8th (R2), and 10th (R3) days of liquid fermentation for transcriptome sequencing. As fermentation progressed, the absorbance, melanin yield, and tyrosinase activity in the broth rose. A total of 5915 differentially expressed genes (DEGs) were detected, with 1136 DEGs between R2 and R1, 3717 between R3 and R1, and 2950 between R3 and R2. Gene Ontology (GO) analysis showed that DEGs were significantly enriched in oxidoreductase activity and ribosome structural constituent terms. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed significant enrichment of DEGs in ribosome and amino acid metabolism pathways. Key DEGs, including transcription factors, glycosidases, P450 enzymes, laccases, and glutamate dehydrogenase, were identified during melanin production in the fermentation broth. These DEGs may play crucial roles in melanin synthesis. This study offers a foundation for further exploring the melanin synthesis mechanism in A. heimuer fermentation broth. Full article

The coastal zone presents complex hydrodynamic interactions among inland groundwater, reservoir water, and intruding seawater, with important implications for ecosystem functioning and water quality. However, the relative roles of hydraulic connectivity and seawater-driven salinity gradients in shaping microbial communities at the aquifer–reservoir interface remain unclear. Here, we integrated hydrochemical analyses with high-throughput 16S rRNA gene sequencing to investigate bacterial community composition, assembly processes, and co-occurrence network patterns across groundwater_in (entering the reservoir), groundwater_out (exiting the reservoir), and reservoir water in a coastal system. Our findings reveal that seawater intrusion exerts a stronger influence on groundwater_out, leading to distinct chemical profiles and salinity-driven environmental filtering, whereas hydraulic connectivity promotes greater microbial similarity between groundwater_in and reservoir water. Groundwater samples exhibited higher alpha and beta diversity compared to the reservoir, with dominant taxa such as Comamonadaceae, Flavobacteriaceae, and Rhodobacteraceae serving as indicators of seawater intrusion. Community assembly analyses showed that homogeneous selection predominated, especially under strong salinity gradients, while dispersal limitation and spatial distance also contributed in areas of reduced connectivity. Key chemical factors, including TDS, Na⁺, Cl⁻, Mg²⁺, and K⁺, strongly shaped groundwater communities. Additionally, groundwater bacterial networks were more complex and robust than those in reservoir water, suggesting enhanced resilience to salinity stress. Collectively, this study demonstrates that salinity gradients can override the effects of hydraulic connectivity in structuring bacterial communities and their networks at coastal interfaces. Our findings provide novel microbial insights relevant for understanding biogeochemical processes and support the use of microbial indicators for more sensitive monitoring and management of coastal groundwater resources. Full article

Industrial image anomaly detection requires the simultaneous identification of local structural and global logical anomalies. Existing methods specialize in single-type anomalies due to divergent feature requirements: structural anomalies demand fine-grained local features, while logical anomalies need semantic features. Consequently, designing a unified network architecture that effectively captures both features without task conflicts remains a key challenge. To address this problem, we propose a Dual-Stream Memory Bank Anomaly Detection (DSMBAD) framework, which enables the collaborative detection of both structural and logical anomalies from complementary perspectives. The framework consists of two memory banks: one stores patch features for detecting structural anomalies through local feature discrepancies, while the other uses segmentation maps to model component relationships for logical anomaly identification. Additionally, a feature distillation mechanism aligns features from different backbone networks to enhance global semantic information. We also introduce a shape-based anomaly scoring method that quantifies differences in component relationships using spatial–morphological features. Experimental results on the MVTec LOCO AD dataset show that our method achieves 91.0% I-AUROC (logical) and 90.8% (structural), significantly outperforming single-type models. Ablation studies confirm the dual-stream design and module effectiveness, offering a novel unified solution. Full article