Search Results (63,921)

The Guanting Gorge section of the Yongding River features complex terrain and densely distributed hydraulic projects. Under extreme rainstorm conditions, flood disasters are highly likely to occur, triggering secondary disasters such as landslides, barrier lakes, and dam breaks, resulting in prominent chain disaster risks and difficult prevention and control. To accurately evaluate the potential flood disaster chain risks in this region, 12 representative evaluation indicators were selected to establish a flood disaster chain risk evaluation system. The analytic hierarchy process (AHP) and entropy weight method (EW) were adopted to calculate subjective and objective indicator weights, respectively. Game theory was applied to achieve the optimal weight fusion and determine the final indicator weights. On this basis, a variable fuzzy model was constructed to identify flood disaster chain risks, and the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) was used to verify the accuracy and reliability of the model. The results show that cumulative precipitation, terrain slope, and annual maximum precipitation are the core driving factors inducing regional flood disaster chains, with corresponding weights of 0.2058, 0.1293, and 0.0980, respectively. High- and extremely high-risk areas are mainly concentrated in the western and southern river valleys, among which the Zhaitang–Luopoling river reach and the river section from Luopoling Reservoir to Sanjiadian Hub present the most prominent risks. The spatial distribution of risk zones is highly consistent with the actual disaster sites of the July 2023 Haihe River extreme rainstorm event, and the model achieves an AUC value of 0.901, indicating high accuracy and reliable simulation results. This study accurately identifies the core inducing factors and high-risk sections of flood disaster chains in the Guanting Gorge section of the Yongding River, which can provide scientific references and technical support for regional flood disaster chain prevention and control, hydraulic project operation and management, and disaster prevention and mitigation planning. Full article

Referring remote sensing image segmentation (RRSIS) aims to segment a target instance from remote sensing imagery according to a natural-language expression. It provides a flexible way to retrieve and localize specific objects in remote sensing scenes, benefiting intelligent Earth observation applications. Although existing methods have achieved promising progress by strengthening vision–language alignment, most of them still represent the expression as a holistic language feature and rely on convolution-dominated decoding for mask prediction. Such a paradigm tends to entangle target category, inter-object relation, and spatial position cues, making it difficult to distinguish the intended instance from multiple same-class distractors in complex remote sensing scenes. To address this limitation, we propose SRGFormer, a graph reasoning framework for RRSIS. Specifically, a semantic role decomposition (SRD) module decomposes the referring expression into target, relation, and position semantics, providing explicit linguistic priors for instance-level localization. Guided by the decomposed relation semantics, a semantic-relational graph transformer (SRGT) performs relation-aware graph reasoning over fused multi-scale visual features, enabling long-range dependency modeling among spatially distributed candidate instances. Furthermore, a progressive mask refinement (PMR) module continuously injects the decomposed semantic priors into semantic modulation, query initialization, and iterative mask decoding, thereby alleviating semantic fading during mask generation. Extensive experiments demonstrate that SRGFormer achieves substantial improvements on RefSegRS, attaining 66.08% mIoU and 76.93% oIoU (surpassing the prior state of the art by 3.96% and 2.83%, respectively) along with a notable 15.95% gain in Pr@0.7. Experiments on the additional RRSIS-D benchmark further demonstrate the general applicability of our approach, where SRGFormer maintains competitive performance (65.87% mIoU and 24.61% Pr@0.9) against existing methods. These results demonstrate that the proposed framework improves target localization and fine-grained mask prediction in complex remote sensing scenes. Full article

Chronic ankle instability (CAI) is associated with impaired sensorimotor function and dynamic postural control; however, reach distance alone may not fully capture task-specific neuromuscular strategies during functional balance tasks. This preliminary cross-sectional case–control study examined modified Star Excursion Balance Test (mSEBT) performance and thigh muscle activation during the mSEBT in novice recreational runners with CAI. Thirty-two novice recreational runners were classified into a CAI group (n = 16) or a healthy control group (n = 16). Normalized mSEBT reach distances, composite score, weight-bearing lunge test performance, and surface electromyography (sEMG) activity of the rectus femoris, vastus lateralis, vastus medialis, and biceps femoris were analyzed. The anterior reach direction was designated as the primary functional and sEMG task before the formal analyses. No significant between-group differences were observed in functional performance or anterior-task thigh muscle activation. Within the CAI group, anterior reach distance was shorter in the involved limb before adjustment (p = 0.014), but the difference did not remain significant after false discovery rate (FDR) correction (q = 0.120). In contrast, rectus femoris (RF) activation during anterior reaching was significantly lower in the involved limb than in the uninvolved limb (p = 0.002, q = 0.035) and remained significant after FDR correction. In this preliminary sample, sEMG provided complementary information to mSEBT reach distance by identifying a side-specific difference in RF activation within novice recreational runners with CAI. Full article

Copper-deficient copper selenide (Cu_2−xSe) nanocrystals possess strong near-infrared (NIR) absorption and efficient photothermal conversion, making them attractive candidates for photoacoustic imaging . In this study, Cu_2−xSe nanocrystals with distinct morphologies were synthesized using different selenium precursors and evaluated as photoacoustic contrast agents. Se–oleylamine precursors produced predominantly disk-shaped nanocrystals with average dimensions of approximately 20 nm in diameter and 5 nm in thickness, while Se–TOP/TOPO precursors yielded smaller spherical nanocrystals. Structural characterization by transmission electron microscopy, high-resolution TEM, and selected-area electron diffraction confirmed the formation of highly crystalline copper-deficient Cu_2−xSe nanocrystals with a face-centered cubic crystal structure. UV–Vis–NIR spectroscopy revealed broad optical absorption extending into the NIR region, with morphology-dependent spectral characteristics. Multispectral optoacoustic tomography demonstrated strong photoacoustic signal generation from both nanodisks and nanospheres over a broad wavelength range. In vivo studies using PEGylated Cu_2−xSe nanospheres showed successful lymphatic uptake following hind paw injection and enabled visualization of the draining popliteal lymph node through spectral unmixing of nanoparticle and hemoglobin signals. These results demonstrate that Cu_2−xSe nanocrystals are promising photoacoustic contrast agents for lymphatic imaging and other biomedical imaging applications. Full article

Natural inland saline–alkaline wetlands offer opportunities for evaluating microbial responses to long-term salinity stress. This study examined surface soils from non-saline, moderately saline, and hypersaline sites in the Luyang Lake wetland, measuring comprehensive edaphic variables (including SAR, ESP, carbonate/bicarbonate chemistry, moisture, DOC, and inorganic N) alongside bacterial and fungal communities via 16S rRNA and ITS sequencing. A coupled salinity–ion and nutrient gradient was identified, with hypersaline soils characterized by high Na⁺, Cl⁻, SAR, and ESP alongside depleted organic carbon and nitrogen. Bacterial α-diversity exhibited a significant unimodal response along the salinity gradient (quadratic regression: p < 0.001), peaking at moderate salinity. Fungal Shannon diversity declined with increasing salinity, but fungal Chao1 richness showed a U-shaped response, highlighting domain-specific and metric-dependent patterns along the gradient. Community assembly analyses revealed contrasting dynamics: deterministic processes were more prevalent in bacterial assembly in hypersaline soils, while fungal assembly remained predominantly stochastic. Co-occurrence networks showed sparser topological structure in high-salinity soils. These patterns are consistent with domain-specific microbial variation along the gradient to coupled edaphic stressors in inland saline–alkaline wetlands. Full article

Distributed acoustic sensing (DAS) is increasingly used for third-party intrusion (TPI) detection in oil and gas pipeline monitoring, but labeled DAS data are often scarce, leading to overfitting, poor generalization, and increased false alarms and missed detections. Conventional data augmentation, GAN-based synthesis, and transfer learning may generate physically implausible samples or fail to cover the event feature space. To address this, we propose a physics-guided large language model (LLM) prompt-engineering framework for DAS data augmentation and pipeline intrusion detection. The framework establishes a physically grounded feature-indicator framework for DAS disturbance-event classification by mapping primary event mechanisms to measurable signal indicators, and then uses a standardized four-module prompt template to guide LLM-based synthesis-script generation. A two-stage iterative verification procedure is further introduced to constrain the generated samples in terms of physical-mechanism compliance and feature-parameter consistency. Synthetic data are combined with real data to train a lightweight PatchTransformer model for TPI detection, while an additional CNN is used to assess cross-architecture applicability. Using the public DAS1K benchmark with five-fold stratified cross-validation and a univariate controlled experiment (0–800 synthetic samples per category), the results show that the use of synthetic data improves detection performance overall. The configuration with 600 synthetic samples per category achieves 92.27% accuracy and 92.38% macro-F1, outperforming the conventional augmentation baseline by 4.74 and 4.86 percentage points, respectively. An additional CNN experiment also showed consistent performance gains across the tested augmentation settings, indicating that the benefit of the proposed synthetic data was not restricted to the PatchTransformer architecture. These findings indicate that LLM-assisted data augmentation can effectively improve the generalization of DAS-based pipeline intrusion detection when field-labeled samples are scarce. Full article

Meat flavor significantly influences consumer preference and market value, particularly for indigenous pig breeds renowned for distinctive sensory characteristics. While traditional research has focused on genetic factors and feeding regimens, emerging evidence suggests that gut microbiota plays a crucial role in meat quality attributes. However, the specific contribution of bacterial–fungal co-occurrence to meat flavor formation remains largely unexplored. This study aimed to characterize the associations between intestinal bacterial–fungal co-occurrence networks and the muscle flavor-related metabolite profiles of CX pigs, using an integrated multi-omics approach. Twenty male pigs (10 CX and 10 LAN, 12 months old) were subjected to comprehensive analyses, including meat quality evaluation, electronic nose analysis, 16S and 18S rRNA sequencing, and untargeted metabolomics. CX pigs exhibited significantly superior meat quality characteristics, including higher moisture content (p < 0.001), fat content (p = 0.008), and meat color scores (p < 0.001). Electronic nose analysis revealed significantly higher response values across all ten aroma sensors in CX pigs (p < 0.001), with the most pronounced differences observed in sensors detecting sulfur compounds and organic compounds. Untargeted metabolomics identified 40 differential metabolites, with 27 up-regulated in CX pigs, including key flavor compounds such as glycocholic acid, isorhamnetin, and pantothenic acid. Microbiome analysis demonstrated significantly higher bacterial alpha diversity in CX pigs (p < 0.05), with enrichment of beneficial bacteria, including Rikenellaceae_RC9_gut_group, Prevotellaceae_UCG_003, and Phascolarctobacterium, while fungal communities showed enrichment of Candida_Lodderomyces_clade. Correlation network analysis revealed that Rikenellaceae_RC9_gut_group demonstrated strong positive correlations with flavor compounds (r = 0.575 for isorhamnetin, r = 0.535 for pantothenic acid, p < 0.001) and all electronic nose responses (r = 0.434–0.691, p < 0.001). Bacterial–fungal co-occurrence networks showed synergistic relationships, with Rikenellaceae_RC9_gut_group positively correlated with Candida_Lodderomyces_clade (r = 0.711, p < 0.001) while exhibiting antagonistic relationships with Piromyces (r = −0.714, p < 0.001). These findings offer novel insights for developing microbiome-targeted strategies to enhance meat quality in pig production systems. Full article

Phosphorus recovery from wastewater and waste streams has become increasingly critical for addressing phosphate-rock supply insecurity and eutrophication control. Building on previous reviews of phosphorus recovery technologies and product applications, this review presents a product-oriented perspective for evaluating phosphorus recovery, explicitly integrating phosphorus sources, process design, product quality, application requirements, regulatory considerations, and life-cycle assessment (LCA) or techno-economic assessment (TEA). Collectively, the available evidence indicates that phosphorus sources differ substantially in phosphorus concentration, speciation, and impurity profiles, underscoring the importance of source–product matching for effective phosphorus recovery. Different phosphorus sources, therefore, require tailored recovery pathways to produce products that satisfy specific quality and application requirements. Product quality is governed primarily by process design rather than by the recovered mineral itself, with purity, contaminant control, regulatory compliance, and end-use suitability ultimately determining the environmental and economic value of recovered phosphorus. A meaningful comparison of recovery pathways further requires harmonized LCA/TEA, as environmental and economic outcomes are highly sensitive to system boundaries, functional units, and allocation assumptions. In addition, regulatory requirements are emerging as a key determinant of product commercialization and should therefore be incorporated throughout process design and technology development. Overall, this review highlights a shift from removal-oriented phosphorus recovery to product-oriented phosphorus recovery, emphasizing the production of high-quality, application-specific phosphorus products as the foundation for technology selection, product innovation, and the transition toward a circular phosphorus economy. Full article

To accurately predict temperature rise due to the concrete hydration heat released from the #0 segment of a continuous concrete girder bridge at an early construction stage for structural design purposes, researchers proposed an approach incorporating empirical predictive formulae with a preliminary numerical analysis. However, due to the uniqueness of the structural geometry and material in each engineering case and the limited data shared by the whole engineering community, no universal predictive empirical model for temperature rise due to hydration heat has yet been identified for practical use that can be applied to a variety of different projects. Moreover, the preliminary numerical analyses are usually based on questionable assumptions and simplifications of the physical truth, the accuracy of which also requires further validation. To this end, the present research measured the time-varying temperature samples of early age massive concrete in the #0 segment of Huangsha Harbor Bridge (a twin-deck three-span continuous concrete box girder bridge located in Jiangsu Province, China) and examined the accuracy of the predictive empirical models formulated by other researchers and the usability of a numerical modal established on a commercial finite element (FE) platform by comparing the corresponding results with the data from the present field measurements. The results suggest that the empirical formulae proposed can generally effectively describe the actual temperature distribution patterns related to the thermal issue, but they are characterized by inferior usability in some cases. In addition, the present comparison also indicates that the actual maximum temperature rise can be correctly predicted by the preliminary FE analysis in most cases. Full article

Section-load calibration is used in aircraft wing-box testing. This study evaluates a fixed-position quasi-static load-calibration and identification procedure for one 7050 aluminum wing-box test section instrumented with surface-bonded fiber Bragg grating (FBG) sensors. A multi-point FBG network was arranged on the skins and webs using finite-element-guided sensor placement to construct bending-, shear-, and torsion-related response features; strain-free reference FBGs provided temperature compensation. All experiments used the same specimen geometry, fixed-root boundary condition, sensor layout, and four actuator positions. Conditions 1–6 were used for regression calibration, whereas Conditions 7 and 8 were held out for interpolation-type validation within the same loading configuration. The maximum/average relative errors were 6.53%/1.51% for bending moment, 2.62%/0.86% for shear force, and 4.04%/1.23% for torsional moment. These results apply only to local laboratory calibration of the tested configuration and do not establish transfer to other geometries, boundary conditions, sensor layouts, loading positions, environmental conditions, or dynamic loading. Full article

Based on survey data from the National Ecological Civilization Pilot Zone (Jiangxi), this study divides rural residents’ self-reported policy compliance in household waste classification (PC) into habit-based policy compliance in waste classification (HPC) and decision-based policy compliance in waste classification (DPC). The integration of a random forest model with Shapley Additive Explanations (SHAP) is utilized to identify the key predictors of each compliance type and to reveal the non-linear predictive contributions and marginal effects through which these factors respectively affect HPC and DPC. Furthermore, fuzzy-set qualitative comparative analysis (fsQCA) is used to uncover the multiple configurational pathways driving each type of compliance. Research indicates the following: (1) The key predictive factors of HPC and DPC are different. (2) Subjective norms (SUN), information interaction (II), and social norms (SON) exhibit significant nonlinear predictive contributions to HPC; policy identification (PI), SON, and procedural fairness (PF) show nonlinear trends in their contributions to the predictive probability of DPC. (3) There are five configuration paths for HPC and four for DPC. This study not only deepens the understanding of rural residents’ HPC and DPC but also provides practical guidance for governments to formulate differentiated and efficient measures to encourage compliance with waste classification policies. Full article

Cross-border transport of engineering materials for international railway construction projects is characterized by substantial heterogeneity among material categories, complex intermodal networks, and considerable variability in customs-clearance and cross-gauge transshipment times at border crossings. Route optimization that focuses solely on minimizing deterministic transportation costs is therefore insufficient to ensure continuous operations at overseas construction sites. Unlike existing intermodal-routing models, which generally assume homogeneous cargo and do not represent competition among heterogeneous material categories for shared cross-border capacity under uncertain clearance and transshipment times, the proposed model explicitly incorporates these features. This study develops a route-optimization model for the cross-border intermodal transport of multiple categories of engineering materials under time uncertainty. The model has two objectives—minimizing total transportation cost and minimizing total transportation time—and includes constraints on supply–demand balance, flow continuity, the shared capacities of arcs, border ports, and transshipment nodes, category-specific capacity use, material–mode compatibility, and maximum delivery times. Triangular fuzzy numbers characterize uncertainty in arc travel, transshipment, and customs-clearance times. The α-cut method transforms the fuzzy time constraints into deterministic equivalents, and the augmented ε-constraint method (AUGMECON2) generates the cost–robust-time Pareto frontier. A case study of the China–Thailand Railway corridor on the Central Route of the Pan-Asia Railway validates the proposed model. Moving from the cost-optimal to the time-optimal solution reduces robust transportation time by 23.13% while increasing total cost by 13.74%. The cost–time trade-off also exhibits increasing marginal costs. The cross-gauge transshipment station is the only shared hub operating near full capacity, whereas maritime travel time is the most sensitive source of uncertainty affecting robust transportation time. Tightening the delivery-time limit for rails has the greatest effect on both total cost and network resilience. These findings support differentiated route planning for cross-border engineering materials, capacity expansion at critical hubs, and decisions that balance transportation budgets with project-schedule requirements. Full article

In the campus planning of severe cold regions, indoor pedestrian corridors have emerged as an important strategy for mitigating harsh weather and maintaining pedestrian network continuity. These enclosed systems provide thermal comfort and physical convenience for faculty and students. However, reduced visual contact with outdoor natural environments may be associated with less favorable affective experiences. Existing research primarily emphasizes functional connectivity and spatial efficiency, with limited quantitative evidence on the visual characteristics of indoor corridors and their relationship with emotional perception. Drawing upon environmental psychology, this study examines the associations between corridor visual characteristics and five AI-predicted affective perception dimensions. Using deep-learning-based computer vision, semantic segmentation, and statistical analysis, environmental features and predicted perception scores were analyzed. A local human validation showed moderate overall agreement between human consensus ratings and AI-predicted scores, supporting their use as exploratory perception proxies. The findings indicate that indoor-corridor-related visual features were negatively associated with predicted beauty scores and positively associated with predicted depression scores, while predicted boringness was positively associated with wall enclosure. Predicted safety and liveliness showed weaker associations with individual visual features. These results provide exploratory evidence for balancing thermal protection, circulation efficiency, and psychological comfort in future campus corridor design. Full article

To address the challenges of information fusion and prediction for highly non-stationary, noisy, and lag-responsive heterogeneous time-series data from multi-source environmental sensing, this paper proposes a novel adaptive hybrid framework, SAPSO-VMD-GRU. First, the framework employs Variational Mode Decomposition (VMD) to decouple the target sequence into trend, periodic, and random components to reduce complexity and filter noise. Then, Lagged Cross-Correlation Analysis (LCCA) is introduced to quantify the time-lagged correlation between the target components and variables such as rainfall and multi-depth soil temperature and moisture, eliminating redundant features to achieve deep fusion of multi-source information. This paper designs an adaptive particle swarm optimization algorithm, SAPSO, by integrating improved Circle chaotic initialization, Sa-function-based nonlinear inertia weight, and a two-stage Cauchy mutation strategy. SAPSO is used to adaptively determine the VMD parameters and the key GRU hyperparameters in different modeling stages. Experiments based on the Bayintun landslide dataset show that, by utilizing the past 5 days of multi-source historical data, including GNSS displacement, rainfall, and lagged soil moisture and temperature, as inputs to forecast the next-day displacement, the proposed framework achieved $R^2$ values above 0.95 on the chronological hold-out validation subset at all three GNSS monitoring stations. These results indicate that the proposed framework can effectively capture lagged triggering effects and improve displacement prediction accuracy under complex, noisy, and non-stationary monitoring conditions. Full article

The Brazilian Amazon is a contested frontier, shaped by destructive and conservationist forces. Most forest clearing is driven by agro-extractivism, an agrarian pathway based on raw commodity production. The socio-biodiversity economy (SBE) has emerged in response, widely regarded as a transformative agrarian pathway capable of reconciling environmental conservation and rural livelihoods. However, recent research suggests that, as socio-biodiversity products scale up, agro-extractivist dynamics can be reproduced within the SBE. We examine this tension in the cocoa frontier of the Transamazon, where cocoa is institutionally promoted as an SBE alternative. We conduct an exploratory, mixed-methods study combining a geospatial analysis of land use and land cover (LULC) change (2020–2025, random forest classification) with a qualitative analysis drawing on participatory mapping and 87 semi-structured interviews with farmers, cooperatives, buyers, and institutional actors. Using a gradient framework, we read LULC transitions and farmers’ land use practices along an agro-extractivism–SBE continuum. The cocoa frontier emerges as a hybrid geography. The landscape is predominantly stable but internally reorganizing: anthropogenic forest declines while full-sun monoculture expands over pasture, with intensification concentrated in peri-urban areas and restoration in remote ones. Land use practices form five recurring configurations, two firmly anchored at the socio-biodiversity or agro-extractivist poles and three whose alignment with the SBE depends on access to markets, knowledge, and institutions. We argue that the frontier contestation unfolds not only between distinct economies but within the cocoa economy itself, and we identify the policy areas relevant to sustaining SBE-oriented practices in the Transamazon. More broadly, this study suggests that the classification of Amazonian forest-based economies as inherent alternatives to agro-extractivism should be treated as an empirical question rather than an assumption. Full article