Search Results (1,838)

Gut microbial community assembly involves a critical bioenergetic trade-off, yet the gut microbes with roles in influencing intestinal metabolic homeostasis remain poorly understood in pelagic ecosystems. A central unresolved question is whether microbiome structure is primarily governed by stochastic geographic drift or by deterministic metabolic filters imposed by diet. Here, we test the metabolic release hypothesis, which posits that access to high-quality prey physiologically “releases” the host from obligate dependence on diverse fermentative symbionts. By integrating δ15N analysis with 16S rRNA metabarcoding in the anchoveta from the South Pacific waters (Engraulis ringens), we reveal a profound, diet-induced restructuring of the gut ecosystem. We demonstrate that trophic ascent triggers a deterministic collapse in microbial alpha diversity (rs = −0.683), driven by the near-complete competitive exclusion of fermentative bacteria (rs = −0.874) and the resulting dominance of a specialized proteolytic core. Mechanistically, the bioavailability of zooplankton-derived protein favors efficient endogenous hydrolysis over costly microbial fermentation, rendering functional redundancy obsolete. Crucially, we find that while metabolic function converges, taxonomic identity remains geographically structured (r = 0.532), suggesting that local environments supply the specific taxa to fulfill universal metabolic roles. These findings establish a link between δ15N as a nutritional physiology proxy of anchoveta and its gut for microbial functional state, bridging the gap between nutritional physiology and ecosystem modeling to better inform the management of global forage fish stocks. Full article

To address the problems of missed and false detections caused by insufficient feature quality in small object detection from UAV perspectives, this paper proposes a UAV small object detection algorithm based on YOLOv8 feature optimization. A spatial cosine convolution module is introduced into the backbone network to optimize spatial features, thereby alleviating the problem of small object feature loss and improving the detection accuracy and speed of the model. An improved C2f_SCConv feature fusion module is employed for feature integration, which effectively reduces feature redundancy in spatial and channel dimensions, thereby lowering model complexity and computational cost. Meanwhile, the WIoU loss function is used to replace the original CIoU loss function, reducing the interference of geometric factors in anchor box regression, enabling the model to focus more on low-quality anchor boxes, and enhancing its small object detection capability. Ablation and comparative experiments on the VisDrone dataset validate the effectiveness of the proposed algorithm for small object detection from UAV perspectives, while generalization experiments on the DOTA and SSDD datasets demonstrate that the algorithm possesses strong generalization performance. Full article

Procedural knowledge is essential in specialized domains, and natural language tools for retrieving procedural knowledge are necessary for non-expert users to facilitate their understanding and learning. In this study, we focus on function decomposition trees, a framework for representing procedural knowledge, and propose a natural language interface leveraging Retrieval-Augmented Generation (RAG). The natural language interface converts the user’s inputs into SPARQL queries, retrieving relevant data and subsequently presenting them in an accessible and chat-based format. Such a flexible and purpose-driven search facilitates users’ understanding of functions of artifacts or human actions and their performance of these actions. We demonstrate that the tool effectively retrieves actions, goals, and dependencies using an illustrative real-world example of a function decomposition tree. In addition, we evaluated the system by comparing it with ChatGPT 4o and Microsoft GraphRAG. The results suggest that the system can deliver responses that are both necessary and sufficient for users’ needs, while the outputs of other systems lack the key elements and return redundant information. Full article

Urban lakes, as critical components of urban ecosystems, provide essential ecological services but face water quality deterioration due to rapid urbanization and associated land use changes. This study investigated the temporal and spatial characteristics and evolution mechanisms of water quality in Wuhan city lakes, with a focus on the Great East Lake basin (GELB), a typical urban lake cluster in the middle Yangtze River basin. By integrating monthly water quality monitoring data (2017–2023) with high-resolution land use data (2020), we employed the Water Quality Index (WQI), Spearman correlation analysis, and Redundancy Analysis (RDA) to assess water quality and the impact of land use on major pollutants. The results revealed significant spatial heterogeneity: Sha Lake (SL) exhibited the best water quality, while Yangchun Lake (YCL) and North Lake (NL) showed the worst conditions. Seasonal variations in water quality were observed, influenced by the ecological functions of lakes and surrounding land use. Notably, understanding these seasonal dynamics provides insights into nutrient cycle operations and their effective management under varying climatic conditions. In addition, the correlation between chlorophyll-a concentration and nutrient elements in urban lakes was not consistent, with some lakes showing significant negative correlations. The water quality of urban lakes is influenced by both land use and human management. Land use analysis indicated high impervious surfaces in East Lake (EL), SL, and YCL exacerbated runoff-driven nutrient loads, the nitrogen elevation from agricultural runoff of Yan East Lake (YEL) and NL’s pollution from historical industrial discharge. This study highlights the urgent need for targeted water management strategies to mitigate the impact of urbanization on water quality and provide a scientific basis for effective governance and ecological restoration in rapidly urbanizing areas around the world. By adopting an integrated approach combining water quality assessments with land use data, this research offers valuable insights for sustainable urban lake management. Full article

Wildfires alter multiple soil functions in forest ecosystems, but how they reconfigure the linkages between these functions is not fully understood. We evaluated the 1-year-postfire and 11-year-postfire effects of wildfire on carbon sequestration, nutrient cycling, fertility maintenance, and erosion regulation, as well as their relationships, in a Chinese boreal larch forest. We further identified the environmental drivers regulating these associations. One year postfire, the soil fertility index transiently increased by 85%, whereas the carbon sequestration and nutrient cycling declined by 58% and 54%, respectively. Principal component analysis showed that wildfire decoupled the multivariate relationships between four soil functions. While these functions were closely clustered in unburned controls, they became dispersed one year postfire, indicating functional dissociation. After eleven years of recovery, a partial reassembly occurred, but with a reconfigured functional structure distinct from the pre-fire state. For the functional pairs, the impact of wildfire was limited to shifting the relationship between the soil fertility and nutrient cycling from a non-significant negative correlation to a significant positive correlation. Redundancy analysis showed that the soil water content remained the primary environmental driver of soil functional relationships before and after the fire, but its role reversed from negative in unburned stands to positive during the postfire recovery, suggesting a shift toward water-mediated functional coupling. Wildfires in boreal forests have far-reaching effects on soil ecosystems, including impacts on the relationships between various soil functions. Our results indicate that wildfire reconfigures the network of soil function linkages in boreal forests, with implications for the recovery of boreal soil ecosystems. Full article

With the large-scale integration of renewable energy, the operational complexity of power systems has increased, placing higher demands on the accuracy of load forecasting. To address the nonlinear characteristics of load variations and improve feature utilization, this paper proposes an IAVOA–EATCN load forecasting model. In the feature engineering stage, an expand–reduce transformation is employed to reconstruct the original multi-feature inputs, and variational mode decomposition (VMD) is further applied to extract low- and high-frequency components, thereby compressing redundant features while preserving essential information structures. In terms of model architecture, the nonlinear representation capability of the temporal convolutional network (TCN) is enhanced by introducing the FlexSwish activation function, and an Efficient Channel Attention (ECA) mechanism is integrated to strengthen the perception of critical features. For parameter optimization, an improved African Vulture Optimization Algorithm (IAVOA) is proposed, which initializes the population using perturbation-enhanced dynamic Tent mapping, balances global exploration and local exploitation through adaptive parameter control, and incorporates elite retention and migration mechanisms to avoid premature convergence. Experimental results on real-world load data demonstrate that the proposed model achieves RMSE, R², and MAE values of 26.5544, 0.9804, and 18.5589, respectively, significantly outperforming benchmark methods and exhibiting strong generalization capability and practical potential for intelligent load forecasting. Full article

Although deep reinforcement learning has achieved great success in the field of autonomous driving, it still faces technical obstacles, such as balancing safety and efficiency in complex driving environments. This paper proposes a deep reinforcement learning multi-vehicle safety enhancement framework that integrates a safety barrier function (SBF-DRL). SBF-DRL first provides independent monitoring assurance for each autonomous vehicle through redundant functions and maintains safety in local vehicles to ensure the safety of the entire multi-autonomous vehicle driving system. Secondly, combining the safety barrier function constraints and the deep reinforcement learning algorithm, a meta-control policy using Markov Decision Process modeling is proposed to provide a safe logic switching assurance mechanism. The experimental results show that SBF-DRL’s collision rate is controlled below 3% in various driving scenarios, which is far lower than other baseline algorithms, and achieves a more effective trade-off between safety and efficiency. Full article

In machine learning, feature selection (FS) is crucial for simplifying data while preserving the variables that most influence predictive performance. Although FS has been extensively studied, addressing it in an unsupervised setting remains challenging. Without class labels, optimization is more prone to slow convergence and the local optima. In particular, unsupervised text FS has received comparatively little attention, and its effectiveness is often limited by the underlying search strategy. To address this issue, we propose a hybrid breeding cooperative whale optimization algorithm (HBCWOA) tailored to unsupervised text FS. HBCWOA combines the cooperative evolutionary mechanism of hybrid breeding optimization with the global search capability of the whale optimization algorithm. The population is partitioned into three lines that evolve independently, while high-quality candidates are periodically exchanged among them to maintain diversity and promote stable, progressive convergence. Moreover, we design an adaptive dynamic accurate probabilistic transfer function (ADAPTF) to balance exploration and exploitation. By integrating the refinement ability of S-shaped transfer functions with the broader search ability of V-shaped ones, ADAPTF adaptively adjusts the exploration depth, reduces redundancy, and improves the convergence stability. After FS, K-means clustering is employed to assess how well the selected features structure document groups. Experiments on the CEC2022 benchmark functions and eight text datasets, under multiple evaluation metrics, show that HBCWOA attains faster convergence, more effective search exploration, and higher clustering accuracy than its S-shaped and V-shaped variants as well as several competitive text FS methods. Full article

Pipelines are critical infrastructures in both industrial production and daily life. However, defects frequently arise due to environmental and manufacturing factors, which may lead to severe safety risks. To overcome the limitations of traditional object detection methods, such as inefficient feature extraction and the loss of critical information, this paper proposes an improved algorithm, termed FALW-YOLOv8, built upon the YOLOv8 architecture. Specifically, the FasterBlock is incorporated into the C2f module to replace standard convolutional layers, effectively reducing computational redundancy while improving feature extraction efficiency. In addition, the ADown module is employed to enhance multi-scale feature preservation, while the LSKA attention mechanism is introduced to improve detection accuracy, particularly for small defects. The Wise-IoU v2 loss function is further adopted to refine bounding box regression for complex samples. Experimental results demonstrate that the proposed FALW-YOLOv8 achieves a 5.8% improvement in mAP50, along with a 34.8% reduction in model parameters and a 30.86% decrease in computational cost. These results indicate that the proposed method achieves a favorable balance between accuracy and efficiency, making it well-suited for real-time industrial pipeline inspection applications. Full article

In the classic energy sector, as well as in the manufacturing and process industries, Programmable Logic Controller (PLC) systems are used for electrical substation control. However, PLCs frequently do not support the communication protocols defined on the standard International Electrotechnical Commission (IEC) 61850. Therefore, this paper presents a vendor-independent method for the integration of Protection and Control (P&C) Intelligent Electronic Devices (IEDs), components of the substation bay level, in PLCs from the substation station level. The method can be used with legacy and modern controllers that offer an open communication interface, where the use of Transmission Control Protocol/Internet Protocol (TCP/IP) is supported. Since many legacy systems offer an open communication interface, this method makes it possible to reuse PLCs, bringing cost efficiency and ecological benefits. The method can be used in a single or redundant way since redundancy is always required in power distribution control. A prototype was developed for the integration over IEC 61850 Manufacturing Message Specification (MMS), and its functional validation is presented in this paper. This solution, besides reducing hardware and software acquisition costs, also contributes to a reduction in electronic waste (E-Waste) and the achievement of Sustainable Development Goals (SDGs). Full article

To mitigate the challenges of false positives and undetected small-scale defects in fabric inspection, this study proposes an advanced fabric defect detection system that leverages an optimized YOLOv9 algorithm. First, redundant computations are reduced by introducing DualConv to replace standard convolution. Second, the LSKA attention mechanism is incorporated to increase the weight of important features, thereby enhancing the accuracy of small target detection and improving the generalization ability. Additionally, the focal modulation network is employed to replace the fast spatial pyramid module, mitigating the loss of detailed information caused by the feature pooling operation. Furthermore, the conventional feature pyramid network is replaced with bidirectional feature pyramid network, which is utilized for efficient feature fusion, thereby enhancing multiscale feature representation and improving detection accuracy. Finally, the bounding box loss function is optimized by introducing the shape-IoU loss function, which facilitates more rapid model convergence and significantly improves detection accuracy. Experiments conducted on a fabric defect dataset demonstrate that the proposed algorithm yields a 6.7% increase in mAP@0.5 and a 14.7% improvement in mAP@0.5–0.95, while simultaneously reducing the model’s total parameters by 17.8% and computational FLOPs by 14.4%, compared with those of the original algorithm. The improved YOLOv9 model significantly enhances the precision and accuracy of defect detection while maintaining inference speed (55.8 FPS) that meets industrial requirements. Full article

Seismic disasters pose major challenges to urban resilience, particularly in high-density cities where the concentration of people, buildings, and infrastructure amplifies disaster risk. This study establishes a GIS-based analytical framework to evaluate the spatial distribution and fairness of seismic emergency shelters in Seoul, using built-up neighborhoods (called dongs in Korean) as the basic analytical unit. Three dimensions are assessed: (1) 500 m walking accessibility based on the road network; (2) redundancy, representing the number of shelters simultaneously reachable; and (3) fairness analysis, integrating spatial and population-based dimensions to reveal disparities between shelter provision and population demand. The results indicate that overall accessibility in Seoul is relatively high, with more than 50% of dongs achieving coverage levels above 50%. However, distinct spatial disparities remain. Central and mountainous areas, such as Jung-gu, Jongno-gu, and southern Seocho-gu, show coverage rates below 20%, while districts in the southwest and northeast exhibit higher redundancy. Fairness analysis further reveals inequality in shelter capacity relative to population: excluding null values, the median coverage ratio is 0.92 and the mean is 1.29, with only 44.97% of dongs achieving sufficient or surplus capacity (coverage ≥ 1). Notably, 44 dongs fall into the Low–High category, representing areas with large populations but limited shelter access, mainly concentrated in Jungnang-gu, Gangbuk-gu, and Yangcheon-gu. These dongs should be prioritized in future planning. Policy implications highlight strengthening shelter provision in high-population but low-coverage zones, incorporating evacuation functions into urban redevelopment, promoting inter-district resource sharing, and improving public awareness. The proposed framework provides a transferable model for optimizing seismic shelter systems in other high-density urban contexts. Full article

Medical imaging is an indispensable tool in clinical diagnosis and therapeutic decision-making, encompassing a wide range of modalities such as radiography, ultrasound, CT, and MRI. With the rapid advancement of deep learning technologies, significant progress has been made in medical image analysis. However, existing deep learning methods are often limited by dataset size, which can lead to overfitting, while traditional approaches relying on hand-crafted features lack specificity and fail to fully capture complex pathological information. To address these challenges, we propose RadioGuide-DCN, an innovative radiomics-guided decorrelated classification network. Our method integrates radiomics features as prior information into deep neural networks and employs a feature decorrelation loss mechanism combined with an anti-attention feature fusion module to effectively reduce feature redundancy and enhance the model’s capacity to capture both local details and global patterns. Specifically, we utilize a Kolmogorov–Arnold Network (KAN) classifier with learnable activation functions to further boost performance across various medical imaging datasets. Experimental results demonstrate that RadioGuide-DCN achieves an accuracy of 93.63% in BUSI image classification and consistently outperforms conventional radiomics and deep learning methods in multiple medical imaging classification tasks, significantly improving classification accuracy and AUC scores. Our study offers a novel paradigm for integrating deep learning with traditional imaging approaches and holds broad clinical application potential, particularly in tumor detection, image classification, and disease diagnosis. Full article

Fertilization regimes impact the carbon cycle processes in paddy soils. However, the effects of shifting fertilization regimes on the structure of microbial communities and functional genes involved in soil carbon (C)-cycling remain unclear. A long-term field experiment was established with three paired fertilization shift treatments: chemical fertilizer (CF) and CF to normal-rate organic fertilizer (CF-NOM); normal-rate organic fertilizer (NOM) and NOM to CF (NOM-CF); high-rate organic fertilizer (HOM) and HOM to CF (HOM-CF). Metagenomic sequencing and bioinformatics analysis were employed to investigate the effects of fertilization shifts on soil C-cycling microbial community structure, functional genes, and environmental factors. The results showed that compared to CF treatment, CF-NOM significantly increased soil organic carbon (SOC), mineral-associated organic carbon (MAOC), particulate organic carbon (POC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), and the emissions of CO₂ and CH₄ (p < 0.05). The NOM-CF led to significant reductions in MAOC, MBC, DOC, and CO₂ and CH₄ emissions. The HOM-CF shift caused significant decreases in SOC, MAOC, POC, MBC, DOC, and CO₂ and CH₄ emissions. Fertilization shifts had no significant effect on the α-diversity of C-cycling microbial communities (p > 0.05), but β-diversity showed a significant restructuring of community composition. Network analysis indicated that fertilization shifts increased positive microbial correlations while reducing network modularity. C-cycling functional genes responded sensitively to fertilization disturbances, especially key genes in the carbon fixation pathway (cdhDE, cooS). Redundancy analysis indicated that soil bulk density (BD) and POC are key environmental factors regulating functional differences in carbon metabolism, which collectively influenced microbial community structure and functional gene abundance along with other factors. We concluded that the C-cycling process in paddy soil was greatly altered by shifts in fertilization regimes, influenced by microbial diversity, functional genes, and network structure linked to soil characteristics. Full article

Intensive greenhouse management profoundly alters soil biogeochemical processes and biotic interactions, distinguishing greenhouse soils from open-field systems. Understanding the drivers of soil fauna assembly is essential for sustaining soil health and productivity. In this study, we examined nematode community drivers in greenhouse melon systems under 2- and 10-year rotations using environmental DNA sequencing. Plant phenology, more than rotation, shaped nematode communities, particularly omnivore predators and bacterivores. This driver was mirrored by a shift in nematode faunal indices from an enriched, bacterial-dominated state at seedling stages to a structured state at maturity. LDA Effect Size and random forest identified key genera (Prismatolaimus, Acrobeloides, and Ceramonema), demonstrating multidimensional drivers of community assembly. Redundancy analysis showed soil organic matter (SOM) and acid phosphatase as major drivers. Mantel tests indicated that the microbial biomass carbon and nitrogen ratio (MBC/MBN) consistently explained community variation (relative abundance: r = 0.229; functional diversity: r = 0.321). Structural equation modeling linked available phosphorus to microbial carbon cycling via cumulative carbon mineralization (CCM, 0.41) and MBC (0.40). SOM increased MBN (0.62) but suppressed Chao1 (−0.76). MBN had the strongest positive effect on Pielou_e (0.49). pH negatively affected functional diversity (−0.33), while nitrate nitrogen (0.35) and CCM (0.32) had positive effects. Our results indicate that MBC and MBN act as microbial bridges linking soil properties to nematode diversity, providing a mechanistic basis for optimizing greenhouse soil management and ecosystem functioning. Full article