Search Results (9,030)

The reinforcement effect of single-reinforced soil support under external loading has limitations, and it is difficult for it to meet engineering stability requirements. Therefore, the stability analysis of slopes supported by a combination of anti-slip piles and reinforced soil under the seismic loading effect needs an in-depth study. Based on the upper-bound theorem of limit analysis and the strength-reduction technique, this study establishes an upper-bound stability model for high–steep slopes that simultaneously considers seismic action and the combined reinforcement of anti-slide piles and reinforced soil. A closed-form safety factor is derived. The theoretical results are validated against published data, demonstrating satisfactory agreement. Finally, the MATLAB R2022a sequential quadratic programming method is used to optimize the objective function, and the Optum G2 2023 software is employed to analyze the factors influencing slope stability due to the interaction between anti-slide piles and geogrids. The research indicates that the horizontal seismic acceleration coefficient k_h exhibits a significant negative correlation with the safety factor F_s. Increases in the tensile strength T of the reinforcing materials, the number of layers n, and the length l all significantly improve the safety factor F_s of the reinforced-soil slope. Additionally, as l increases, the potential slip plane of the slope shifts backward. For slope support systems combining anti-slide piles and reinforced soil, when the length of the geogrid is the same, adding anti-slide piles can significantly improve the slope’s safety factor. As anti-slide piles move from the toe to the crest of the slope, the safety factor first decreases and then increases, indicating that the optimal reinforcement position for anti-slide piles should be in the middle to lower part of the slope body. The length of the anti-slip piles should exceed the lowest layer of the geogrid to more effectively utilize the blocking effect of the pile ends on the slip surface. The research findings can provide a theoretical basis and practical guidance for parameter optimization in high–steep slope support engineering. Full article

Lost circulation, a prevalent challenge in drilling engineering, poses significant risks including drilling fluid loss, wellbore instability, and environmental contamination. Conventional plugging materials often exhibit an inadequate performance under high-temperature, high-pressure (HTHP), and complex formation conditions. To address that, this study developed a high-performance gel–resin composite plugging material resistant to HTHP environments. By optimizing the formulation of bisphenol-A epoxy resin (20%), hexamethylenetetramine (3%), and hydroxyethyl cellulose (1%), and incorporating fillers such as nano-silica and walnut shell particles, a controllable high-strength plugging system was constructed. Fourier-transform infrared spectroscopy (FTIR) and thermogravimetric analysis (TGA) confirmed the structural stability of the resin, with an initial decomposition temperature of 220 °C and a compressive strength retention of 14.4 MPa after 45 days of aging at 140 °C. Rheological tests revealed shear-thinning behavior (initial viscosity: 300–350 mPa·s), with viscosity increasing marginally to 51 mPa·s after 10 h of stirring at ambient temperature, demonstrating superior pumpability. Experimental results indicated excellent adaptability of the system to drilling fluid contamination (compressive strength: 5.04 MPa at 20% dosage), high salinity (formation water salinity: 166.5 g/L), and elevated temperatures (140 °C). In pressure-bearing plugging tests, the resin achieved a breakthrough pressure of 15.19 MPa in wedge-shaped fractures (inlet: 7 mm/outlet: 5 mm) and a sand-packed tube sealing pressure of 11.25 MPa. Acid solubility tests further demonstrated outstanding degradability, with a 97.69% degradation rate after 24 h in 15% hydrochloric acid at 140 °C. This study provides an efficient, stable, and environmentally friendly solution for mitigating drilling fluid loss in complex formations, exhibiting significant potential for engineering applications. Full article

Observing building changes in remote sensing images plays a crucial role in monitoring urban development and promoting sustainable urbanization. Mainstream change detection methods have demonstrated promising performance in identifying building changes. However, buildings have large intra-class variance and high similarity with other objects, limiting the generalization ability of models in diverse scenarios. Moreover, most existing methods only detect whether changes have occurred but ignore change types, such as new construction and demolition. To address these issues, we present a building change-type detection network (BCTDNet) based on the Segment Anything Model (SAM) to identify newly constructed and demolished buildings. We first construct a dual-feature interaction encoder that employs SAM to extract image features, which are then refined through trainable multi-scale adapters for learning architectural structures and semantic patterns. Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. Furthermore, we develop a change-aware attribute decoder that integrates building semantics into the change detection process via an extraction decoding network. Subsequently, an attribute-aware strategy is adopted to explicitly generate distinct maps for newly constructed and demolished buildings, thereby establishing clear temporal relationships among different change types. To evaluate BCTDNet’s performance, we construct the JINAN-MCD dataset, which covers Jinan’s urban core area over a six-year period, capturing diverse change scenarios. Moreover, we adapt the WHU-CD dataset into WHU-MCD to include multiple types of changing. Experimental results on both datasets demonstrate the superiority of BCTDNet. On JINAN-MCD, BCTDNet achieves improvements of 12.64% in IoU and 11.95% in F1 compared to suboptimal methods. Similarly, on WHU-MCD, it outperforms second-best approaches by 2.71% in IoU and 1.62% in F1. BCTDNet’s effectiveness and robustness in complex urban scenarios highlight its potential for applications in land-use analysis and urban planning. Full article

Satellite sea surface salinity (SSS) products suffer from coarse spatiotemporal resolution, limiting their utility for mesoscale ocean monitoring. To address this, we proposed the Transformer-based satellite SSS super-resolution (SR) model (TSR) coupled with a progressive transfer learning (PTL) strategy. TSR improved the resolution of the salinity satellite SMOS from 1/4° and 10 days to 1/12° and daily. Leveraging Transformer, TSR captured long-range dependencies critical for reconstructing fine-scale structures. PTL effectively balanced structural detail acquisition and local accuracy correction by combining the gridded reanalysis products with scattered in situ observations as training labels. Validated against independent in situ measurements, TSR outperformed existing L3 salinity satellite products, as well as convolutional neural network and generative adversarial network-based SR models, particularly reducing the root mean square error (RMSE) by 33% and the mean bias (MB) by 81% compared to the SMOS input. More importantly, TSR demonstrated an enhanced capability in resolving mesoscale eddies, which were previously obscured by noise in salinity satellite products. Compared to training with a single label type or switching label types non-progressively, PTL achieved a 3%–66% lower RMSE and a 73–92% lower MB. TSR enables higher-resolution satellite monitoring of SSS, contributing to the study of ocean dynamics and climate change. Full article

Urban street canyons in high-density areas exacerbate PM_2.5 accumulation, posing significant public health risks. Through integrated empirical and computational methods—including empirical PM_2.5 and microclimate measurements, multivariate regression analysis, and high-resolution ENVI-met5.1 simulations—this study quantifies the threshold effects of pedestrian-oriented morphological indicators on PM_2.5 exposure in east–west-oriented residential streets. Key findings include the following: (1) the height-to-width ratio (H/W) negatively correlates with exposure, where H/W = 2.0 reduces the peak concentrations by 37–41% relative to H/W = 0.5 through enhanced vertical advection; (2) the Build-To-Line ratio (BTR) exhibits a positive correlation with exposure, with BTR = 63.2% mitigating exposure by 12–15% compared to BTR = 76.8% by reducing aerodynamic stagnation; (3) pollution exposure can be mitigated by enhancing airflow ventilation within street canyons through architectural facade design. These evidence-based morphological thresholds (H/W ≥ 1.5, BTR ≤ 70%) provide actionable strategies for reducing health risks in polluted urban corridors, supporting China to meet its national air quality improvement targets. Full article

Precipitable water vapor (PWV) is a critical component of the Earth’s atmosphere, playing a pivotal role in weather systems, climate dynamics, and hydrological cycles. Accurate estimation of PWV is essential for numerical weather prediction, climate modeling, and atmospheric correction in remote sensing. Ground-based observation stations can only provide PWV measurements at discrete points, whereas spaceborne infrared remote sensing enables spatially continuous coverage, but its retrieval algorithm is restricted to clear-sky conditions. This study proposes an innovative approach that uses ensemble learning models to integrate infrared and microwave satellite data and other geographic features to achieve all-weather PWV retrieval. The proposed product shows strong consistency with IGRA radiosonde data, with correlation coefficients (R) of 0.96 for the ascending orbit and 0.95 for the descending orbit, and corresponding RMSE values of 5.65 and 5.68, respectively. Spatiotemporal analysis revealed that the retrieved PWV product exhibits a clear latitudinal gradient and seasonal variability, consistent with physical expectations. Unlike MODIS PWV products, which suffer from cloud-induced data gaps, the proposed method provides seamless spatial coverage, particularly in regions with frequent cloud cover, such as southern China. Temporal consistency was further validated across four east Asian climate zones, with correlation coefficients exceeding 0.88 and low error metrics. This algorithm establishes a novel all-weather approach for atmospheric water vapor retrieval that does not rely on ground-based PWV measurements for model training, thereby offering a new solution for estimating water vapor in regions lacking ground observation stations. Full article

The large yellow croaker (Larimichthys crocea) is susceptible to microbial contamination during storage due to its high protein and moisture contents. This study was designed to find a new way to reduce bacteria in large yellow croakers by combining slightly acidic electrolyzed water (SAEW) with nano-bubble (NB) technology. Exploring the effects of available chlorine concentration (ACC), processing time, and water temperature on the bacteria reduction effect of the SAEW-NB treatment for large yellow croakers. Also, the effects of the SAEW-NB combined treatment on sensory evaluation, total viable counts (TVCs), total volatile basic nitrogen (TVB-N), texture, taste profile, and volatile flavor compounds of large yellow croakers were analyzed during the storage period at 4 °C. The results show that the SAEW-NB treatment achieved significantly enhanced microbial reduction compared to individual treatments. Under the conditions of a 4 °C water temperature, 40 mg/L ACC, and 15 min treatment, the SAEW-NB treatment inhibited the increases in physical and chemical indexes such as TVC and TVB-N, maintained the fish texture, and delayed the production of off-flavor volatiles such as aldehydes, alcohols, esters, and ketones, compared with the control group (CG) during storage at 4 °C. In conclusion, the SAEW-NB treatment could better retard fish spoilage, extending the shelf life by approximately 2 days. It might be a promising new industrial approach for large yellow croakers’ storage. Full article

Traffic safety is a significant global concern, and traffic sign recognition (TSR) is essential for the advancement of intelligent transportation systems. Traditional YOLO11s-based methods often struggle to balance detection accuracy and processing speed, particularly in the context of small traffic signs within complex environments. To address these challenges, this study presents Ghost-YOLO-GBH, an innovative lightweight model that incorporates three key enhancements: (1) the integration of a GhostNet backbone, which substitutes the conventional YOLO11s architecture and utilizes Ghost modules to exploit feature redundancy, resulting in a 40.6% reduction in computational load while ensuring effective feature extraction for small targets; (2) the development of a HybridFocus module that combines large separable kernel attention with multi-scale pooling, effectively minimizing background interference and improving contextual feature aggregation by 4.3% in isolated tests; and (3) the implementation of a Bidirectional Dynamic Multi-Scale Feature Pyramid Network (BiDMS-FPN) that allows for bidirectional cross-stage feature fusion, significantly enhancing the accuracy of small target detection. Experimental results on the TT100K dataset indicate that Ghost-YOLO-GBH achieves an impressive 81.10% mean Average Precision (mAP) at a threshold of 0.5, along with an 11.7% increase in processing speed (45 FPS) and an 18.2% reduction in model parameters (7.74 M) compared to the baseline YOLO11s. Overall, Ghost-YOLO-GBH effectively balances accuracy, efficiency, and lightweight deployment, demonstrating superior performance in real-world applications characterized by small signs and cluttered backgrounds. This research provides a novel framework for resource-constrained TSR applications, contributing to the evolution of intelligent transportation systems. Full article

This study focuses on cultural and urban development policies under China’s 14th Five-Year Plan, exploring the content and semantic structure of discussions on the “East Asian Cultural Capital” project on the Weibo platform. It analyzes how national cultural development policies are reflected in the discourse system related to the “East Asian Cultural Capital” on social media and emphasizes the guiding role of policies in the dissemination of online culture. When China announced the 14th Five-Year Plan in 2021, the strategic direction and policy framework for cultural development over the five-year period from 2021 to 2025 were clearly outlined. This study employs text mining and semantic network analysis methods to analyze user-generated content on Weibo from 2023 to 2024, aiming to understand public perception and discourse trends. Word frequency and TF-IDF analyses identify key terms and issues, while centrality and CONCOR clustering analyses reveal the semantic structure and discourse communities. MR-QAP regression is employed to compare network changes across the two years. Findings highlight that urban cultural development, heritage preservation, and regional exchange are central themes, with digital media, cultural branding, trilateral cooperation, and cultural–economic integration emerging as key factors in regional collaboration. Full article

Obtaining the echo of individual fish is an important prerequisite for fisheries acoustic applications, such as in situ measurement of fish target strength and assessment of fish abundance using the counting method. It is also the foundation for evaluating the growth status of farmed fish and managing aquaculture risks. The density of farmed fish populations is typically higher, and such high-density aquaculture further increases the difficulty of obtaining individual fish echoes in practical applications. Building upon previous research and considering the behavioral characteristics of fish in aquaculture settings, this study conducted performance simulations, live fish experiments in simulated aquaculture cages, and comparative evaluations of three individual fish broadband echo detection methods based on a broadband signal system: the amplitude pulse width method (APM) based on echo envelopes, the peak detection and time delay estimation method (PDM), and the peak time delay combined with instantaneous frequency method (PDIM). This study assumed a dorsolateral fish orientation, which limits its research scope and applicability. The results showed that the PDIM achieved a detection accuracy of 78.34% and a false recognition rate of 1.32%. The APM based on echo envelopes was insensitive to individual fish echoes and had lower recognition accuracy. The PDM exhibited better individual fish echo capture capabilities, while the PDIM demonstrated superior overlapping echo rejection capabilities. Full article

Rolling bearings are prone to failure, meaning that research on intelligent fault diagnosis is crucial in relation to this key transmission component in rotating machinery. The application of deep learning (DL) has significantly advanced the development of intelligent fault diagnosis. This paper proposes a novel method for rolling bearing fault diagnosis based on the fractional constant Q non-stationary Gabor transform (FCO-NSGT) and VMamba-Conv. Firstly, a rolling bearing fault experimental platform is established and the vibration signals of rolling bearings under various working conditions are collected using an acceleration sensor. Secondly, a kurtosis-to-entropy ratio (KER) method and the rotational kernel function of the fractional Fourier transform (FRFT) are proposed and applied to the original CO-NSGT to overcome the limitations of the original CO-NSGT, such as the unsatisfactory time–frequency representation due to manual parameter setting and the energy dispersion problem of frequency-modulated signals that vary with time. A lightweight fault diagnosis model, VMamba-Conv, is proposed, which is a restructured version of VMamba. It integrates an efficient selective scanning mechanism, a state space model, and a convolutional network based on SimAX into a dual-branch architecture and uses inverted residual blocks to achieve a lightweight design while maintaining strong feature extraction capabilities. Finally, the time–frequency graph is inputted into VMamba-Conv to diagnose rolling bearing faults. This approach reduces the number of parameters, as well as the computational complexity, while ensuring high accuracy and excellent noise resistance. The results show that the proposed method has excellent fault diagnosis capabilities, with an average accuracy of 99.81%. By comparing the Adjusted Rand Index, Normalized Mutual Information, F1 Score, and accuracy, it is concluded that the proposed method outperforms other comparison methods, demonstrating its effectiveness and superiority. Full article

Traditionally, the surface suspended particulate matter (SPM) and freshwater fluxes have been computed using in situ SPM, salinity, and current measurements or through the numerical modeling. In this study, satellite-derived SPM concentration, ocean current, and sea surface salinity (SSS) are used to demonstrate the capability to characterize and quantify the surface SPM flux and freshwater flux in the Yellow Sea (YS) and East China Sea (ECS). The different routes for SPM and freshwater to transport from the coastal region to the interior ECS are identified. The seasonal and interannual SPM and freshwater fluxes from the coastal region of the ECS are further characterized and quantified. The average SPM flux reaches ~0.3–0.4 g m⁻² s⁻¹ along the route. The SPM and the freshwater fluxes in the region show different seasonality. The intensified SPM flux from the ECS coast to the offshore in winter is one order higher than the SPM flux in summer, while the offshore freshwater flux peaks in summer and weakens significantly in winter. Particularly, we found that the SPM and SSS features in the ECS changed in response to the 2020 summer Yangtze River flood event. These spatial and temporal changes for SPM and SSS in the ECS in the 2020 summer and early autumn were attributed to the anomalous surface SPM and freshwater fluxes in the same period. Full article

To reveal the influence mechanisms of seasonal climatic factors (wind speed, wind direction, temperature) and leakage direction on hydrogen dispersion and explosion behavior from single-source leaks at typical risk locations (hydrogen storage tanks, compressors, dispensers) in hydrogen refueling stations (HRSs), this work established a full-scale 1:1 three-dimensional numerical model using the FLACS v22.2 software based on the actual layout of an HRS in Xichang, Sichuan Province. Through systematic simulations of 72 leakage scenarios (3 equipment types × 4 seasons × 6 leakage directions), the coupled effects of climatic conditions, equipment layout, and leakage direction on hydrogen dispersion patterns and explosion risks were quantitatively analyzed. The key findings indicate the following: (1) Downward leaks (−Z direction) from storage tanks tend to form large-area ground-hugging hydrogen clouds, representing the highest explosion risk (overpressure peak: 0.25 barg; flame temperature: >2500 K). Leakage from compressors (±X/−Z directions) readily affects adjacent equipment. Dispenser leaks pose relatively lower risks, but specific directions (−Y direction) coupled with wind fields may drive significant hydrogen dispersion toward station buildings. (2) Southeast/south winds during spring/summer promote outward migration of hydrogen clouds, reducing overall station risk but causing localized accumulation near storage tanks. Conversely, north/northwest winds in autumn/winter intensify hydrogen concentrations in compressor and station building areas. (3) An empirical formula integrating climatic parameters, leakage conditions, and spatial coordinates was proposed to predict hydrogen concentration (error < 20%). This model provides theoretical and data support for optimizing sensor placement, dynamically adjusting ventilation strategies, and enhancing safety design in HRSs. Full article

Constrained multi-objective optimization problems (CMOPs) require optimizing multiple conflicting objectives while satisfying complex constraints. These constraints generate infeasible regions that challenge traditional algorithms in balancing feasibility and Pareto frontier diversity. chemical reaction optimization (CRO) effectively balances global exploration and local exploitation through molecular collision reactions and energy management, thereby enhancing search efficiency. However, standard CRO variants often struggle with CMOPs due to the absence of specialized constraint-handling mechanisms. To address these challenges, this paper integrates the CRO collision reaction mechanism with an existing evolutionary computational framework to design a dual-stage and dual-population chemical reaction optimization (DDCRO) algorithm. This approach employs a staged optimization strategy, which divides population evolution into two phases. The first phase focuses on objective optimization to enhance population diversity, and the second prioritizes constraint satisfaction to accelerate convergence toward the constrained Pareto front. Furthermore, to leverage the infeasible solutions’ guiding potential during the search, DDCRO adopts a two-population strategy. At each stage, the main population tackles the original constrained problem, while the auxiliary population addresses the corresponding unconstrained version. A weak complementary mechanism facilitates information sharing between populations, which enhances search efficiency and algorithmic robustness. Comparative tests on multiple test suites reveal that DDCRO achieves optimal IGD/HV values in 53% of test problems. The proposed algorithm outperforms other state-of-the-art algorithms in both convergence and population diversity. Full article

China has identified the high-quality development of its green manufacturing transition as the top priority for upgrading their industrial structure system which will lead to the sustainable development of an innovation ecosystem. To assess their progress in this area, this study selects the panel data of 31 provinces in China from 2011 to 2021 and constructs an evaluation index system for the green transformation of the manufacturing industry from four dimensions: environment, resources, economy, and industrial structure. This not only comprehensively and systematically reflects the dynamic changes in the green transformation of the manufacturing industry but also addresses the limitations of currently used indices. The entropy value method is used to calculate the comprehensive score of the green transformation of the manufacturing industry, while the key factors influencing the convergence of the green transformation of the manufacturing industry are further explored. The results show that first, the overall level of the green transformation of the manufacturing industry has significantly improved as evidenced by an approximate 32% increase. Second, regional differences are significant with the eastern region experiencing significantly higher levels of transformation compared to the central and western regions, along with a decreasing trend from the east to the central and western regions. From a policy perspective, the findings suggest that tailored production methods for each region should be adopted with a greater emphasis on knowledge exchanges to promote green transition in less developed regions. In addition, further regulations are required which, in part, focus on increasing the degree of openness to the outside world to promote the level of green manufacturing transition. Full article