Search Results (47,622)

Prolonged exposure to landfill leachate can weaken the impermeability of liner systems, leading to leachate leakage and the contamination of surrounding soil and water. To improve loess impermeability to enable its use as a liner material, this study uses synthetic landfill leachate to investigate its effects on loess permeability via a series of laboratory tests. This study focused on the influence of varying dosages of calcium lignosulfonate (CLS) on loess permeability, along with its capacity to adsorb and immobilize heavy metal ions. Microscale characterization techniques, including Zeta potential analysis, X-ray fluorescence spectroscopy (XRF), and scanning electron microscopy (SEM), were employed to investigate the impermeability mechanisms of CLS-modified loess and its adsorption behavior toward heavy metals. The results indicate that the permeability coefficient of loess decreases significantly with increasing compaction, while higher leachate concentrations lead to a notable increase in permeability. At a compaction degree of 0.90, the permeability coefficient was reduced to 8 × 10⁻⁸ cm/s. In contrast, under conditions of maximum leachate concentration, the permeability coefficient rose markedly to 1.5 × 10⁻⁴ cm/s. Additionally, increasing the dosage of the compacted loess stabilizer (CLS) effectively reduced the permeability coefficient of the modified loess to 7.1 × 10⁻⁵ cm/s, indicating improved impermeability and enhanced resistance to contaminant migration. With the prolonged infiltration time of landfill leachate, the removal efficiency of Pb²⁺ gradually decreases and stabilizes, while the Pb²⁺ removal efficiency of the modified loess increased by approximately 40%. CLS-modified loess, through multiple mechanisms, reduces the fluid flow pathways and enhances its adsorption capacity for Pb²⁺, thereby improving the soil’s protection against heavy metal contamination. While these results demonstrate the potential of CLS-modified loess as a sustainable landfill liner material, the findings are based on controlled laboratory conditions with Pb²⁺ as the sole target contaminant. Future work should evaluate long-term performance under field conditions, including seasonal wetting–drying and freeze–thaw cycles, and investigate multi-metal systems to validate the broader applicability of this modification technique. Full article

This paper presents the formulation and simulation-based validation of a novel hierarchical fuzzy-adaptive Proportional–Integral–Derivative (PID) control framework for a rectilinear active mass damper, designed to enhance vibration suppression in structural applications. The proposed scheme utilizes a Linear–Quadratic Regulator (LQR)-optimized PID controller as the baseline regulator. To address the limitations of this baseline PID controller under varying seismic excitations, an auxiliary fuzzy adaptation layer is integrated to adjust the state-weighting matrices of the LQR performance index dynamically. The online modification of the state weightages alters the Riccati equation’s solution, thereby updating the PID gains at each sampling instant. The fuzzy adaptive mechanism modulates the said weighting parameters as nonlinear functions of the classical displacement error and normalized acceleration. Normalized acceleration provides fast, scalable, and effective feedback for vibration mitigation in structural control using AMDs. By incorporating the system’s normalized acceleration into the adaptation scheme, the controller achieves improved self-tuning, allowing it to respond efficiently and effectively to changing conditions. The hierarchical design enables robust real-time PID gain adaptation while maintaining the controller’s asymptotic stability. The effectiveness of the proposed controller is validated through customized MATLAB/SIMULINK-based simulations. Results demonstrate that the proposed adaptive PID controller significantly outperforms the baseline PID controller in mitigating structural vibrations during seismic events, confirming its suitability for intelligent structural control applications. Full article

Settlement control for tunnel–terminal co-construction projects remains undefined, despite the growing trend of integrating multiple transportation modes within large-scale transport hubs. This study investigates a large underground structure passing beneath an airport terminal, combining field investigations, statistical analyses, and finite element simulations to examine differential settlement behavior under non-uniform loading conditions. The key contribution of this work is the proposal of a differential settlement control standard, defined by the tangent of the rotation angle between adjacent column foundations, with a recommended value of 1/625. Case analysis at cross-section E–E shows that the measured maximum tangent rotation angle was 1/839, corresponding to base slab settlements of 40.5 mm and 33.1 mm for the high-speed railway and metro structures, respectively. Application of the proposed 1/625 criterion yields allowable maximum base slab settlements of 55.28 mm for the high-speed railway and 44.83 mm for the metro, with differential settlement limits of 7.5 mm and 3.13 mm. Numerical simulations confirm the validity of this standard, ensuring the structural integrity of co-constructed systems and providing practical guidance for future airport terminal–tunnel integration projects. Full article

This study introduces a real-time, non-intrusive monitoring system designed to support elderly care through vision-based pose estimation and hand gesture recognition. The proposed framework integrates convolutional neural networks (CNNs), temporal modeling using LSTM networks, and symmetry-aware keypoint analysis to enhance the accuracy and reliability of behavior detection under varied real-world conditions. By leveraging the bilateral symmetry of human anatomy, the system improves the robustness of posture and gesture classification, even in the presence of partial occlusion or variable lighting. A total of 21 hand landmarks and 33 body pose points are used to recognize predefined actions and communication gestures, enabling seamless interaction without wearable devices. Experimental evaluations across four distinct lighting environments confirm a consistent accuracy above 90%, with real-time alerts triggered via IoT messaging platforms. The system’s modular architecture, interpretability, and adaptability make it a scalable solution for intelligent elderly individual monitoring, offering a novel application of spatial symmetry and optimized deep learning in healthcare technology. Full article

Bearings are vital to rotating machinery, where undetected faults can cause severe failures. Conventional fault diagnosis methods depend on manual feature engineering and labeled data, struggling with complex industrial conditions. This study introduces an innovative unsupervised framework combining masked self-supervised learning with the Swin Transformer for bearing fault diagnosis. The novel integration leverages masked Auto Encoders to learn robust features from unlabeled vibration signals through reconstruction-based pretraining, while the Swin Transformer’s shifted window attention mechanism enhances efficient capture of fault-related patterns in long-sequence signals. This approach eliminates reliance on labeled data, enabling precise detection of unknown faults. The proposed method achieves 99.53% accuracy on the Paderborn dataset and 100% accuracy on the CWRU dataset significantly, surpassing other unsupervised Auto Encoder-based methods. This method’s innovative design offers high adaptability and substantial potential for predictive maintenance in industrial applications. Full article

In autonomous railway systems, where there is no driver acting as the primary fault detector, annoying interior noise caused by track defects can go unnoticed for long periods. One of the main contributors to this phenomenon is rail corrugation, a recurring defect that generates vibrations and acoustic emissions, directly affecting passenger comfort and accelerating infrastructure deterioration. This work presents a methodology for the automatic detection of corrugated track sections, based on the mathematical modeling of the spectral content of onboard-recorded acoustic signals. The hypothesis is that these defects produce characteristic peaks in the frequency domain, whose position depends on speed but whose wavelength remains constant. The novelty of the proposed approach lies in the formulation of two functional spectral indices—IIAPD (permissive) and EWISI (restrictive)—that combine power spectral density (PSD) and fast Fourier transform (FFT) analysis over spatial windows, incorporating adaptive frequency bands and dynamic prominence thresholds according to train speed. This enables robust detection without manual intervention or subjective interpretation. The methodology was validated under real operating conditions on a commercially operated metro line and compared with two reference techniques. The results show that the proposed approach achieved up to 19% higher diagnostic accuracy compared to the best-performing reference method, maintaining consistent detection performance across all evaluated speeds. These results demonstrate the robustness and applicability of the method for integration into autonomous trains as an onboard diagnostic system, enabling reliable, continuous monitoring of rail corrugation severity using reproducible mathematical metrics. Full article

By integrating cathodic arc evaporation (CAE) with magnetron sputtering (MS) or high-power impulse magnetron sputtering (HiPIMS), hard coatings with diverse multicomponent compositions can be fabricated. Depending on the deposition conditions, the coatings with nano-composite or nano-multilayered microstructures are produced. During the mixing deposition conditions, nano-composite coatings are fabricated, which can be tailored to possess combining properties of super hardness, low friction coefficient, and excellent thermal/chemical stability. For the deposition with larger rotating periods, layer-by-layer deposition was observed. By the nano-multilayered coating design, superior mechanical properties (hardness ≥ 35 GPa), modulated residual stresses, and enhanced high-temperature properties can be obtained. In addition, lubricious elements, low friction (friction coefficient < 0.4), and low wear (<10⁻⁵ mm³/N∙m) both at ambient temperature and high temperature can be realized. Among these coatings, some have been specifically designed to achieve outstanding cutting performance in high-speed cutting applications. Several nitride and oxide hard coatings, such as AlTiN, TiAlN/TiSiN, AlCrN/Cu, and AlCrO, were deposited using a hybrid industrial physical vapor deposition (PVD) coating system. The microstructure, mechanical properties, and cutting performance of these coatings will be discussed. Full article

Nitrate leaching from agricultural soils is a major contributor to groundwater contamination and non-point source pollution. Controlling this loss remains challenging due to the complexity of soil–water–nutrient interactions under intensive farming practices. Biochar, a porous, carbon-rich material derived from biomass pyrolysis, has emerged as a promising amendment for nitrate mitigation. This review summarizes recent advances in understanding the roles of biochar in nitrate retention and transformation in soils, including both direct mechanisms—such as surface adsorption, ion exchange, and pore entrapment—and indirect mechanisms—such as enhanced microbial activity, soil structure improvement, and root system development. Field and laboratory evidence shows that biochar can reduce NO₃⁻-N leaching by 15–70%, depending on its properties, soil conditions, and application context. However, inconsistencies in performance due to differences in biochar types, soil conditions, and environmental factors remain a major barrier to widespread adoption. This review also suggests current knowledge gaps and research needs, including long-term field validation, biochar material optimization, and integration of biochar into precision nutrient management. Overall, biochar presents a multifunctional strategy for reducing nitrate leaching and promoting sustainable nitrogen management in agroecosystems. Full article

This study experimentally investigates the wake-induced vibration (WIV) behavior of a bio-inspired harbor seal whisker model subjected to upstream propeller-generated unsteady flows. Vibration amplitudes, frequencies, and wake–whisker interactions were systematically evaluated under various flow conditions. The test matrix included propeller rotational speed N_p = 0~5000 r/min, propeller diameter D_p = 60~100 mm, incoming flow velocity U_∞ = 0~0.2 m/s, and separation distance between the whisker model and the propeller L/D = 10~30 (D = 16 mm, diameter of the whisker model). Results show that inline (IL) and crossflow (CF) vibration amplitudes increase significantly with propeller speed and decrease with increasing separation distance. Under combined inflow and wake excitation, non-monotonic trends emerge. Frequency analysis reveals transitions from periodic to subharmonic and broadband responses, depending on wake structure and coherence. A non-dimensional surface fit using L/D and the advance ratio (J = U_∞/(N_pD_p)) yielded predictive equations for RMS responses with good accuracy. Phase trajectory analysis further distinguishes stable oscillations from chaotic-like dynamics, highlighting changes in system stability. These findings offer new insight into WIV mechanisms and provide a foundation for biomimetic flow sensing and underwater tracking applications. Full article

The Haiyang-2B (HY-2B) satellite, launched on 25 October 2018, carries both active and passive microwave sensors, including a scanning microwave Radiometer (SMR), to deliver high-precision, all-weather global observations. Sea surface temperature (SST) is among its key products. We evaluated the HY-2B SMR Level-4A (L4A) SST (25 km resolution) over the North Pacific (0–60°N, 120°E–100°W) for the period 1 October 2023 to 31 March 2025 using the extended triple collocation (ETC) and dual-pairing methods. These comparisons were made against the Remote Sensing System (RSS) microwave and infrared (MWIR) fused SST product and the National Oceanic and Atmospheric Administration (NOAA) in situ SST Quality Monitor (iQuam) observations. Relative to iQuam, HY-2B SST has a mean bias of –0.002 °C and a root mean square error (RMSE) of 0.279 °C. Compared to the MWIR product, the mean bias is 0.009 °C with an RMSE of 0.270 °C, indicating high accuracy. ETC yields an equivalent standard deviation (ESD) of 0.163 °C for HY-2B, compared to 0.157 °C for iQuam and 0.196 °C for MWIR. Platform-specific ESDs are lowest for drifters (0.124 °C) and tropical moored buoys (0.088 °C) and highest for ship and coastal moored buoys (both 0.238 °C). Both the HY-2B and MWIR products exhibit increasing ESD and RMSE toward higher latitudes, primarily driven by stronger winds, higher columnar water vapor, and elevated cloud liquid water. Overall, HY-2B SST performs reliably under most conditions, but incurs larger errors under extreme environments. This analysis provides a robust basis for its application and future refinement. Full article

As the safety requirements of elevator systems continue to rise, the detection of loose bolts and the high-precision segmentation of anti-loosening lines have become critical challenges in elevator landing door inspection. Traditional manual inspection and conventional visual detection often fail to meet the requirements of high precision and robustness under real-world conditions such as oil contamination and low illumination. This paper proposes two improved algorithms for detecting loose bolts and segmenting anti-loosening lines in elevator landing doors. For small-bolt detection, we introduce the DS-EMA model, an enhanced YOLOv8 variant that integrates depthwise-separable convolutions and an Efficient Multi-scale Attention (EMA) module. The DS-EMA model achieves a 2.8 percentage point improvement in mAP over the YOLOv8n baseline on our self-collected dataset, while reducing parameters from 3.0 M to 2.8 M and maintaining real-time throughput at 126 FPS. For anti-loosening-line segmentation, we develop an improved DeepLabv3+ by adopting a MobileViT backbone, incorporating a Global Attention Mechanism (GAM) and optimizing the ASPP dilation rate. The revised model increases the mean IoU to 85.8% (a gain of 5.4 percentage points) while reducing parameters from 57.6 M to 38.5 M. Comparative experiments against mainstream lightweight models, including YOLOv5n, YOLOv6n, YOLOv7-tiny, and DeepLabv3, demonstrate that the proposed methods achieve superior accuracy while balancing efficiency and model complexity. Moreover, compared with recent lightweight variants such as YOLOv9-tiny and YOLOv11n, DS-EMA achieves comparable mAP while delivering notably higher recall, which is crucial for safety inspection. Overall, the enhanced YOLOv8 and DeepLabv3+ provide robust and efficient solutions for elevator landing door safety inspection, delivering clear practical application value. Full article

The saline–alkali soil environment profoundly influences the diversity and composition of soil microbial communities, reshaping their ecological network structures. As a vital staple crop, rice (Oryza sativa L.) plays a crucial role in global food security, highlighting the urgent need to improve its cultivation efficiency in saline–alkali soils. However, the mechanisms by which rice roots recruit beneficial microorganisms from native soils under prolonged saline–alkali stress remain largely unclear, and limited research has been conducted on the effectiveness of microbial inoculants in enhancing rice salt tolerance. This study investigated microbial communities in a saline field subjected to over a decade of continuous rice cultivation. Plant growth-promoting microorganisms were isolated and screened from the rhizosphere. The findings revealed long-term salt stress significantly altered microbial diversity and community composition, although the overall microbial network structure remained resilient. A total of 21 plant growth-promoting strains were identified, indicating that rice roots under sustained salt stress selectively recruit beneficial microbes that contribute to plant growth and stress adaptation. Further experimental validation demonstrated that synthetic microbial communities outperformed individual strains in promoting rice seedling growth under high-salinity conditions, likely due to synergistic microbe and microbe–plant interactions. In conclusion, while saline–alkali conditions disrupt native microbial communities, rice exhibits adaptive capacity by selectively enriching growth-promoting microorganisms. The application of synthetic microbial consortia presents a promising strategy to enhance rice resilience and productivity in saline–alkali environments. Full article

Understanding the dispersion of volcanic sulphur dioxide (SO₂) plumes is crucial for assessing their environmental and climatic impacts. This study integrates satellite-based and reanalysis datasets to simulate as well as visualise the dispersion patterns of volcanic SO₂ under diverse atmospheric conditions. By incorporating data from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2), CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), and OMI (Ozone Monitoring Instrument) datasets, we are able to provide comprehensive insights into the vertical and horizontal trajectories of SO₂ plumes. The methodology involves modelling SO₂ dispersion across various atmospheric pressure surfaces, incorporating wind directions, wind speeds, and vertical column mass densities. This approach allows us to trace the evolution of SO₂ plumes from their source through varying meteorological conditions, capturing detailed vertical distributions and plume paths. Combining these datasets allows for a comprehensive analysis of both natural and human-induced factors affecting SO₂ dispersion. Visual and statistical interpretations in the paper reveal overall SO₂ concentrations, first injection dates, and dissipation patterns detected across altitudes of up to ±20 km in the stratosphere. This work highlights the significance of combining satellite-based and global atmospheric reanalysis datasets to validate and enhance the accuracy of plume dispersion models while having a general agreement that OMI daily data and MERRA-2 reanalysis hourly data are capable of accurately accounting for SO₂ plume dispersion patterns under varying meteorological conditions. Full article

This work develops a novel phosphate-functionalized extraction resin (HEHEHP + Cyanex272)/SiO₂-P via the vacuum impregnation method for efficient separation of light rare earth element impurities from lanthanum (La³⁺) in nitric medium through synergistic extraction. Batch experiments have demonstrated superior adsorption selectivity toward impurity ions over La³⁺ in a pH 4 nitric acid solution. Column studies confirmed exceptional performance under ambient conditions, achieving a lanthanum treatment capacity of 120.6 mg/g and over 98% impurity removal, which surpasses most reported values. Notably, this purification process enables direct production of purified La³⁺ solutions through a single-column system without desorption, significantly enhancing efficiency and reducing costs. Mechanistic insights revealed combined ion exchange and coordination interactions between metal ions and P-OH/P=O groups, corroborated by advanced characterization and density functional theory calculations. These findings indicate a higher binding affinity of light rare earth compared with La³⁺. This strategy provides a scalable approach for ultra-high-purity lanthanum compound production in advanced optical and electronic applications. Full article

This paper proposes a design and control methodology for a Quasi-Y-Source impedance source inverter (QS-YSI) as a power electronics interface for Vehicle-to-Grid (V2G) and Grid-to-Vehicle (G2V) applications in the context of virtual power plants (VPPs). The work presents an analysis of bidirectional power transfer using Electric Vehicles (EVs) to supply power to the utility grid, businesses, and homes, thereby acting as distributed energy resources. The proposed QS-YSI topology supports both V2G and G2V operation while providing reactive power compensation and enabling the decoupled tracking of active power (P) and reactive power (Q), demonstrating the capability of EVs to return energy to the grid and to provide ancillary services such as power factor correction. The key contributions are a detailed control design methodology that includes pulsating DC-link voltage regulation, inverter output current reference tracking in the synchronous $dq$ reference frame considering DC-link voltage dynamics, and a modified Pulse Width Modulation (PWM) technique for effective decoupling of DC link and inverter output current control. Finally, the feasibility and validity of the proposed approach are demonstrated through simulations of the complete system under nominal conditions and experiments conducted considering a small-scale prototype. Full article