Search Results (98)

To enhance the generalization of networks and avoid redundant training efforts, cross-image hyperspectral anomaly detection (HAD) based on deep learning has been gradually studied in recent years. Cross-image HAD aims to perform anomaly detection on unknown hyperspectral images after a single training process on the network, thereby improving detection efficiency in practical applications. However, the existing approaches may require additional supervised information or stacking of networks to improve model performance, which may impose high demands on data or hardware in practical applications. In this paper, a simple and lightweight unsupervised cross-image HAD method called Variational Joint Discrimination Network (VJDNet) is proposed. We leverage the reconstruction and distribution representation ability of the variational autoencoder (VAE), learning the global and local discriminability of anomalies jointly. To integrate these representations from the VAE, a probability distribution joint discrimination (PDJD) module is proposed. Through the PDJD module, the VJDNet can directly output the anomaly score mask of pixels. To further facilitate the unsupervised paradigm, a sample pair generation module is proposed, which is able to generate anomaly samples and background representation samples tailored for the cross-image HAD task. The experimental results show that the proposed method is able to maintain the detection accuracy with only a small number of parameters. Full article

This study investigates the impact of circumferential angle (φ) and interaction angle (θ) between hydrogen jets and diesel sprays in a co-axial hydrogen–diesel injector on combustion and emissions in a hydrogen–diesel dual-fuel engine using 3D CFD simulations. The results demonstrate that a co-axial dual-layer nozzle design significantly enhances combustion performance by leveraging hydrogen jet kinetic energy to accelerate fuel–air mixing. Specifically, a co-axial alignment (φ = 0°) between hydrogen and diesel sprays achieves optimal combustion characteristics, including the highest in-cylinder pressure (20.92 MPa), the earliest ignition timing (−0.3° CA ATDC), and the maximum indicated power of the high-pressure cycle (47.26 kW). However, this configuration also results in elevated emissions, with 29.6% higher NOx and 34.5% higher soot levels compared to a φ = 15° arrangement. To balance efficiency and emissions, an interaction angle of θ = 7.5° proves most effective, further improving combustion efficiency and increasing indicated power to 47.69 kW while reducing residual fuel mass. For applications prioritizing power output, the φ = 0° and θ = 7.5° configuration is recommended, whereas a φ = 15° alignment with a moderate θ (5–7.5°) offers a viable compromise, maintaining over 90% of peak power while substantially lowering NOx and soot emissions. Full article

In rapidly urbanizing areas, there is a notable aggregation of vitality in high-density urban environments, accompanied by an increasing discrepancy between the supply and demand of urban green space (UGS). This study presented an integrated framework comprising a model for UGS supply-demand coupling coordination and a measure of urban vitality. Using downtown Beijing as a case study, the Gini coefficient assessed UGS supply-demand disparities across different vitality types. The study examined how UGS supply and demand factors interact with urban vitality, revealing the impact of UGS supply-demand imbalances on various dimensions of vitality and the UGS mismatches experienced by different vitality groups. The study showed that: (1) 63.29% of central Beijing’s areas had low UGS supply-demand coordination, with 39.23% experiencing UGS mismatches; (2) UGS supply and demand were significantly correlated with urban vitality spatial distribution; (3) these factors significantly impacted urban comprehensive vitality; (4) and there were notable UGS distribution disparities among vitality groups, with economic vitality group perceiving the greatest inequity (Gini = 0.311), followed by social vitality (Gini = 0.289) and cultural vitality group (Gini = 0.247). These findings offer valuable insights for a more refined assessment and enhancement of UGS, aiming to achieve balanced, high-quality, and sustainable urban development. Full article

Objectives: This study aimed to characterize the basic structure of sialylated glycopeptide (SCP) from edible bird’s nest, and to explore the intervention effect and mechanism of SCP based on a mouse lung inflammation model induced by lipopolysaccharide (LPS). Methods: C57BL/6 mice were randomly divided into the control group (CON), model group (LPS), EBN group, SCP group, and SA group. Results: The results showed that SCP had the typical structures of polypeptides and carbohydrates. SCP effectively intervened in the lung inflammation response. The number of neutrophils (Neu) in BALF decreased by 41.3%, the level of tumor necrosis factor-α (TNF-α) decreased by 36.4%, and the W/D ratio of lung tissues decreased by 27.2%, effectively preventing pathological changes in lung tissues. A total of 40 differential metabolites such as choline, linolenic acid, and xanthine were screened between the SCP group and the LPS group. These differential metabolites were mainly enriched in the metabolic pathways of glycerophospholipids, alpha-linolenic acid, and purines. Conclusions: The research results support that SCP, as the active substance of edible bird’s nest, can effectively improve lung inflammation, providing theoretical guidance for the development of functional edible bird’s nest products. Full article

Currently, regional flood research often lacks a synergistic assessment of both flood occurrence risk and flood duration, limiting the comprehensive understanding needed for sustainable disaster risk reduction. To address this gap, this study applies advanced machine learning approaches to assess flood hazards in the Yangtze River Delta, one of China’s most economically and environmentally significant regions. Specifically, XGBoost is employed to evaluate flood occurrence risk, while LSTM is used to predict flood duration. A novel flood risk index (FRI) is proposed to quantify the integrated risk by combining these two dimensions, supporting more sustainable and effective flood risk management strategies. Furthermore, SHAP analysis is conducted to identify the most critical factors contributing to flooding. The results demonstrate that XGBoost delivers strong predictive performance, with average precision, recall, F1-score, accuracy, and AUC values of 0.823398, 0.831667, 0.827090, 0.826435, and 0.871062, respectively. Areas with high flood risk, long duration, and elevated FRI values are mainly concentrated in major river basins and coastal zones. The range of flood risk spans from 0.000073 to 0.998483 (mean: 0.237031), flood duration from 0.223598 to 2.077040 (mean: 0.940050), and FRI from 0 to 0.934256 (mean: 0.091711). Cities with over 40% of their areas falling in medium to high FRI zones include Suzhou (48.99%), Jiaxing (48.07%), Yangzhou (46.87%), Suqian (44.19%), Changzhou (43.43%), Wuxi (43.20%), Lianyungang (42.21%), Yancheng (40.88%), Huai’an (40.73%), and Bengbu (40.06%). SHAP analysis reveals that elevation and rainfall are the most critical factors influencing flood occurrence, underscoring the importance of integrating environmental variables into sustainable flood risk governance. Full article

Background: Rose processing faces critical challenges in preserving bioactive compounds and aroma profiles during thermal treatments, particularly given the growing demand for natural ingredients in the food and cosmetic industries. Methods: Using widely targeted metabolomics, we first characterized volatile profiles of four major commercial cultivars (Hetian, Damask, Bulgarian, and Fenghua; n = 6 replicates per cultivar), identifying terpenoids as dominant components (p < 0.05). Subsequent thermal optimization focused on Hetian rose, where WGCNA and K-means analyses revealed temperature-dependent dynamics (40–55 °C, triplicate drying trials per temperature). Results: Hetian rose exhibited significantly higher accumulation (p < 0.05) of a unique sesquiterpene marker, 4-(1,5-dimethyl-1,4-hexadienyl)-1-methyl-cyclohexene. Systematic drying optimization identified 50 °C as the thermal threshold for optimal color, bioactive retention, and sensory quality. Mechanistic analysis identified 193 temperature-responsive metabolites (VIP > 1, FC < 0.25 or >4, p < 0.01), with terpenoid biosynthesis (MVA/MEP pathways) and esterification dynamics emerging as critical control points. Conclusions: This study establishes the first cultivar-specific processing framework for roses, demonstrating that metabolic signature-guided drying improves product quality. The findings advance our understanding of thermal impacts on aroma biochemistry while providing actionable protocols for natural product industries. Full article

Electrophilic compounds are bioactive components commonly found in foods that are capable of covalently modifying nucleophilic sites on biologically functional macromolecules. These compounds may elicit positive bioactivity or negative biotoxicity, posing significant challenges in terms of time and resource expenditure in the de novo characterization of their biological activity. In this study, we developed a database of 332 food-derived electrophilic compounds and used a semi-supervised k-nearest neighbors (KNN) machine learning model to predict their bioactivity. Molecular docking analysis identified the three chalcone compounds with the highest potential positive activity—4-hydroxyderricin (4HD), isoliquiritigenin (ISO), and butein. Furthermore, in cell experiments, treatment with 4HD, ISO, and butein significantly reduced reactive oxygen species (ROS) levels. An RT-qPCR analysis demonstrated that these chalcones significantly upregulated the mRNA expression of Nrf2 and its downstream antioxidant genes, including Nqo1, HO-1, Gsr, Gclc, and Gclm. ISO’s cytoprotective and antioxidant effects were abolished following these findings, which highlight that 4HD, ISO, and butein are effective Nrf2 activators and suggest that comprehensive virtual technology is a promising strategy for identifying functional bioactive compounds. Full article

Early diagnosis of skin cancer can significantly improve patient survival. Currently, skin lesion classification faces challenges such as lesion–background semantic entanglement, high intra-class variability, artifactual interference, and more, while existing classification models lack modeling of physicians’ diagnostic paradigms. To this end, we propose DermViT, a medically driven deep learning architecture that addresses the above issues through a medically-inspired modular design. DermViT consists of three main modules: (1) Dermoscopic Context Pyramid (DCP), which mimics the multi-scale observation process of pathological diagnosis to adapt to the high intraclass variability of lesions such as melanoma, then extract stable and consistent data at different scales; (2) Dermoscopic Hierarchical Attention (DHA), which can reduce computational complexity while realizing intelligent focusing on lesion areas through a coarse screening–fine inspection mechanism; (3). Dermoscopic Feature Gate (DFG), which simulates the observation–verification operation of doctors through a convolutional gating mechanism and effectively suppresses semantic leakage of artifact regions. Our experimental results show that DermViT significantly outperforms existing methods in terms of classification accuracy (86.12%, a 7.8% improvement over ViT-Base) and number of parameters (40% less than ViT-Base) on the ISIC2018 and ISIC2019 datasets. Our visualization results further validate DermViT’s ability to locate lesions under interference conditions. By introducing a modular design that mimics a physician’s observation mode, DermViT achieves more logical feature extraction and decision-making processes for medical diagnosis, providing an efficient and reliable solution for dermoscopic image analysis. Full article

Medical image registration is essential in clinical practices such as surgical navigation and image-guided diagnosis. The Transformer architecture of TransMorph demonstrates better accuracy in non-rigid registration tasks. However, its weaker spatial locality priors necessitate large-scale training datasets and a heavy number of parameters, which conflict with the limited annotated data and real-time demands of clinical workflows. Moreover, traditional downsampling and upsampling always degrade high-frequency anatomical features such as tissue boundaries or small lesions. We proposed WaveMorph, a wavelet-guided multi-scale ConvNeXt method for unsupervised medical image registration. A novel multi-scale wavelet feature fusion downsampling module is proposed by integrating the ConvNeXt architecture with Haar wavelet lossless decomposition to extract and fuse features from eight frequency sub-images using multi-scale convolution kernels. Additionally, a lightweight dynamic upsampling module is introduced in the decoder to reconstruct fine-grained anatomical structures. WaveMorph integrates the inductive bias of CNNs with the advantages of Transformers, effectively mitigating topological distortions caused by spatial information loss while supporting real-time inference. In both atlas-to-patient (IXI) and inter-patient (OASIS) registration tasks, WaveMorph demonstrates state-of-the-art performance, achieving Dice scores of 0.779 ± 0.015 and 0.824 ± 0.021, respectively, and real-time inference (0.072 s/image), validating the effectiveness of our model in medical image registration. Full article

The vigorous development of marine engines fueled by natural gas can effectively support the reform of energy structures in the field of ship power, aligning with the global trend toward sustainable development and green shipping. However, the presence of knock significantly hinders the improvement of engine thermal efficiency. Therefore, studying the knock mechanism in natural gas engines is not only crucial for enhancing engine power and economy but also for advancing the transition to cleaner and more sustainable energy sources in the maritime industry. In this paper, via a 2D numerical model, the dominant role in the knock mechanism of stoichiometric methane combustion in a combustion chamber of a rapid compression machine (RCM) is revealed. It further establishes the association mechanism between constant-volume combustion and pressure wave suppression at high temperatures. The results show that the knock is caused by the end-gas auto-ignition. The increase in initial temperature can significantly change auto-ignition modes and combustion modes, but initial pressure has little effect on this. The increase in initial temperature will inhibit the strength of pressure waves, and the increase in initial pressure cannot significantly increase the strength of pressure waves. The main cause why auto-ignition occurs earlier is not due to the increase in the strength of pressure waves, but the decrease in the required increase in temperature to attain ignition temperature caused by the increase in initial temperature. The peak pressure is affected by the initial pressure on the left wall before auto-ignition and the increase in pressure on the left wall at low to medium initial temperature. The pressure oscillation amplitude is positively correlated to the increase in pressure on the left wall. Constant volume combustion will occur at a high initial temperature. The increase and decrease in pressure are very uniform which will lead to the decrease in the pressure oscillation amplitude. The peak pressure depends on the influence of initial temperature and pressure on the increase in pressure produced by constant volume combustion. Full article

Marine black shales are important to geologists, because they are not only potential sources and reservoir rocks for shale gas/oil, but also, their deposition could influence the climatic and oceanic environments. Here, a detailed study of the shales in the Dalong Formation in South China was conducted to understand the changes in continental weathering and upwelling and their influences on organic matter accumulation in the late Permian. The results revealed that the deposition of the Dalong and Daye Formations could be divided into five stages, with the highest TOC values (>2%) being observed in stages 2 and 4, intermediate TOCs (~1% to 2%) being observed in stages 1 and 3, and the lowest TOC values (<1%) being observed in stage 5. This study attributed the enhanced organic matter accumulation in stages 2 and 4 to enhanced continental weathering (high CIA values and δ²⁶Mg values) and intense upwelling (high Mo/TOC ratios and low δ¹³C_org and Co_EF × Mn_EF values), both of which contributed to high primary productivity and increased anoxia of the bottom waters, further leading to the accumulation of organic matter. Overall, both enhanced continental weathering and upwelling contributed to the development of anoxia, even euxinia, of the seawater and further triggered an end-Permian mass extinction (EPME). Full article

The Late Paleozoic strata on the northwestern margin of the Tarim Block provide valuable insights into the subduction and collision processes that formed the Southwest Tianshan Orogenic Belt. This study integrates detrital zircon U-Pb dating and sandstone geochemical analysis of the Balikelike and Kalundaer formations to examine sedimentary provenance and tectonic settings during the Cisuralian–Guadalupian Epoch in the Keping area on the northwestern margin of the Tarim Block. Three of five Precambrian detrital zircon U-Pb age populations, 2500–2300 and 2000–1800 Ma and 900–600 Ma, are likely related to the fragmentation of the Columbia supercontinent and Rodinia’s assembly, respectively. Two Paleozoic detrital zircons, 500–380 Ma, are associated with Paleozoic magmatism. Among them, ~295 Ma zircons are associated with post-collisional extension and emplacement of the Tarim Large Igneous Province. Geochemical analysis of sandstones, coupled with tectonic reconstruction, indicates a passive continental margin setting in the northwestern margin of the Tarim Block during the Silurian Period, later transitioned to a foreland basin from the Pennsylvanian to the Guadalupian Epochs. The crustal transformation from the Middle-late Devonian to Early Mississippian marked the closure of the South Tianshan Ocean (STO), involving a soft collision and significant uplift, with major orogenesis occurring in the Late Guadalupian. Five key stages are identified in the evolution of the foreland basin: (1) Middle-late Devonian to Early Mississippian initiation (remnant ocean basin stage); (2) Late Mississippian to Early Pennsylvanian early stage; (3) Late Pennsylvanian to Early Cisuralian middle stage; (4) the Late Cisuralian stage; and (5) the terminal Guadalupian stage. These findings provide new constraints on when STO closed and propose an innovative foreland basin evolution model during the late post-collisional phase from the Late Mississippian to Guadalupian. Collectively, the data advance our understanding of the tectonic processes that shaped the northwestern Tarim Block, with broader implications for Paleozoic geodynamics. Full article

Lung cancer remains the leading cause of cancer-related mortality globally, with a poor prognosis primarily due to late diagnosis and limited treatment options. This research highlights the critical demand for advanced prognostic tools by creating a model centered on aging-related genes (ARGs) to improve prediction and treatment strategies for lung adenocarcinoma (LUAD). By leveraging datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we developed a prognostic model that integrates 14 ARGs using the least absolute shrinkage and selection operator (LASSO) alongside Cox regression analyses. The model exhibited strong predictive performance, achieving area under the curve (AUC) values greater than 0.8 for one-year survival in both internal and external validation cohorts. The risk scores generated by our model were significantly correlated with critical features of the tumor microenvironment, including the presence of cancer-associated fibroblasts (CAFs) and markers of immune evasion, such as T-cell dysfunction and exclusion. Higher risk scores correlated with a more tumor-promoting microenvironment and increased immune suppression, highlighting the model’s relevance in understanding LUAD progression. Additionally, XRCC6, a protein involved in DNA repair and cellular senescence, was found to be upregulated in LUAD. Functional assays demonstrated that the knockdown of XRCC6 led to decreased cell proliferation, whereas its overexpression alleviated DNA damage, highlighting its significance in tumor biology and its potential therapeutic applications. This study provides a novel ARG-based prognostic model for LUAD, offering valuable insights into tumor dynamics and the tumor microenvironment, which may guide the development of targeted therapies and improve patient outcomes. Full article

Amid global environmental degradation, understanding the spatiotemporal dynamics and trade-offs of ecosystem services (ESs) under varying land-use scenarios is critical for advancing the sustainable development of social–ecological systems. This study analyzed the Chaohu Lake Basin (CLB), focusing on four scenarios: natural development (ND), economic priority (ED), ecological protection (EP), and sustainable development (SD). Using the PLUS model and multi-objective genetic algorithm (MOGA), land-use changes for 2030 were simulated, and their effects on ESs were assessed quantitatively and qualitatively. The ND scenario led to significant declines in cropland (3.73%) and forest areas (0.18%), primarily due to construction land expansion. The EP scenario curbed construction land growth, promoted ecosystem recovery, and slightly increased cropland by 0.05%. The SD scenario achieved a balance between ecological and economic goals, maintaining relative stability in ES provision. Between 2010 and 2020, construction land expansion, mainly concentrated in central Hefei City, led to a marked decline in habitat quality (HQ) and landscape aesthetics (LA), whereas water yield (WY) and soil retention (SR) improved. K-means clustering analysis identified seven ecosystem service bundles (ESBs), revealing significant spatial heterogeneity. Bundles 4 through 7, concentrated in mountainous and water regions, offered high biodiversity maintenance and ecological regulation. In contrast, critical ES areas in the ND and ED scenarios faced significant encroachment, resulting in diminished ecological functions. The SD scenario effectively mitigated these impacts, maintaining stable ES provision and ESB distribution. This study highlights the profound effects of different land-use scenarios on ESs, offering insights into sustainable planning and ecological restoration strategies in the CLB and comparable regions. Full article

Accurate disclosure and proactive engagement in ESG practices are essential for achieving high-quality economic development, particularly as China addresses significant challenges during its reform journey. The Classified Reform of State-Owned Enterprises (CRSOE) is a strategic initiative by the Chinese government aimed at fostering this development. Our study leverages the implementation of the CRSOE as an exogenous shock, employing the difference-in-differences approach to assess the policy’s governance impact on ESG decoupling from the perspective of ownership heterogeneity. The policy was found to alleviate ESG decoupling, particularly pronounced among SOEs with special functions. The governance effect is achieved by reducing the aspiration–performance gap. Specifically, the policy effectively narrows the disparity between a company’s actual performance and the expected performance based on the industry average, thereby mitigating ESG decoupling. However, the policy’s impact can be weakened by factors such as political connections among executives and media attention. Furthermore, the CRSOE effectively addresses greenwashing practices within ESG decoupling, with a particularly strong effect on SOEs that fail to disclose ESG information in alignment with Global Reporting Initiative (GRI) standards. These findings highlight the importance of understanding the broader implications and underlying mechanisms of the policy. Therefore, building on the assessment of how the CRSOE policy impacts ESG decoupling, we also examine the mechanisms through which this policy operates and how its effectiveness varies under different conditions of heterogeneity. By extending the application of principal-agent theory and performance feedback theory, our research suggests that policymakers should prioritize market-driven reforms for fully competitive SOEs and promote a stronger emphasis on non-financial goals. Additionally, it is essential to mitigate the undue influence of political promotions on the management of all SOEs. Full article