Search Results (902)

Steels remain among the most widely used structural and engineering materials in modern infrastructure, energy systems, and industrial facilities. Their long-term reliability depends critically on the early detection of corrosion damage and microstructural degradation. This review surveys recent advances in two complementary families of non-destructive evaluation (NDE) methods: magnetic techniques, including magnetic Barkhausen noise (MBN), magnetic flux leakage (MFL), eddy current testing (ECT), and magnetic hysteresis analysis; and electrochemical methods including electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning vibrating electrode technique (SVET), and electrochemical noise (EN). Recent progress in sensor miniaturization, signal processing algorithms, and multi-technique integration is reviewed. Particular attention is given to the sensitivity of these methods to microstructural changes reported in the literature, including carbide dissolution, phase transformations, temper embrittlement, and sensitization in stainless steels, as well as to the conditions under which such sensitivity has been demonstrated. The potential synergy between magnetic and electrochemical monitoring is discussed as a possible pathway toward more robust, condition-based maintenance frameworks. Challenges related to field deployment, environmental interference, calibration, and data interpretation are identified, and future directions—including machine learning-assisted analysis and multi-physics sensor arrays—are outlined. Full article

Underwater micro-bubble detection entails multiple challenges, including diminutive target sizes, sparse pixel information, pronounced specular highlights and water scattering, indistinct bubble boundaries, and adhesion or overlap between instances. To address these issues, we propose HSMD-YOLO, an improved detector tailored for high-resolution micro-bubble detection and built upon YOLOv11. The model incorporates three novel components: the Scale Switch Block (SSB), a scale-transformation module that suppresses artifacts and background noise, thereby stabilizing edges in thin-walled bubble regions and enhancing sensitivity to geometric contours; the Global Local Refine Block (GLRB), which achieves efficient global relationship modeling with an asymptotic linear complexity ($O\left(N\right)$) in spatial dimensions while further refining local features, thereby strengthening boundary perception and improving bubble–background separability; and the Bidirectional Exponential Moving Attention Fusion (BEMAF), which accommodates the multi-scale nature of bubbles by employing a parallel multi-kernel architecture to extract spatial features across scales, coupled with a multi-stage EMA based attention mechanism to enhance detection robustness under weak boundaries and complex backgrounds. Experiments conducted on an Side-Illuminated Light Field Bubble Database (SILB-DB) and a public gas–liquid two-phase flow dataset (GTFD) demonstrate that HSMD-YOLO achieves mAP@50 scores of 0.911 and 0.854, respectively, surpassing mainstream detection methods. Ablation studies indicate that SSB, GLRB, and BEMAF contribute performance gains of 1.3%, 2.0%, and 0.4%, respectively, thereby corroborating the effectiveness of each module for micro-scale object detection. Full article

Urban rail wheels endure prolonged exposure to frequent starts and stops, heavy cyclic loads, and complex track conditions, which often lead to premature failure modes such as wear, fatigue cracking, and corrosion in conventional wheel materials. These limitations restrict their ability to meet the evolving demands of modern rail systems for enhanced durability and performance. To address this, the present study uses laser cladding to deposit high-entropy alloy coatings with systematically varied aluminium content onto wheel substrates. The study compares phase composition, microstructure, and mechanical properties across the different coatings. Results show that increasing Al content transforms the coating microstructure from a single face-centred cubic (FCC) phase to a dual-phase structure of FCC and body-centred cubic (BCC) phases, accompanied by notable grain refinement. Among the variants, the CrMnFeCoNi(Al)₈ coating has the densest microstructure and the most favourable mechanical performance. It achieves a microhardness of 399.62 HV_0.5 in the as-clad state and 450 ± 5 HV_0.5 after heat treatment, representing an increase of approximately 12.6%. This coating also demonstrates improved corrosion resistance, with an open-circuit potential 0.07 V higher than the CL60 substrate. Multi-body dynamics simulations confirm that the clad wheels maintain excellent operational stability and safety under service conditions. Full article

Noise elimination in current signals of three-phase induction motors, considered as energy systems for electromechanical conversion, is a critical preprocessing step for reliable condition monitoring and fault diagnosis. However, conventional wavelet-based denoising approaches often treat noise suppression as a generic filtering task, which may distort diagnostically relevant spectral components and inter-phase relationships. To address this limitation, this paper presents a physically constrained denoising framework that integrates the Clarke transformation with dyadic wavelet analysis to enable diagnostic-safe noise attenuation. The proposed method explicitly preserves frequency bands associated with supply harmonics, mechanical phenomena, and fault-related sidebands, while enforcing inter-phase coherence and zero-sequence stability in the Clarke domain. Wavelet parameters are selected through a diagnostic-oriented multi-criteria framework that jointly balances disturbance attenuation, harmonic fidelity, coherence retention, zero-sequence stability, and time-domain waveform integrity. Experimental validation using real three-phase induction motor current measurements under steady-state conditions shows that the proposed framework achieves noise reduction ratios of approximately 8–10 dB, while preserving the amplitudes of the main harmonic components with deviations below ${10}^{-3}$ dB. These results demonstrate that the proposed method provides a robust and physically consistent preprocessing stage for current-based monitoring of three-phase AC machines. Full article

This paper presents a systematic application of the Homotopy Perturbation Method (HPM) to the nonlinear static analysis of cantilever beams subjected simultaneously to three coplanar follower loads: an axial force H, a transverse force V, and a bending moment M₁. The studied configuration introduces complex mathematical self-coupling, as the bending moment depends on the solution of the differential equation even in its boundary conditions (γ₁), transforming the problem into a nonlinear one that is resistant to standard analytical methods. The primary methodological contribution of this work is the successful extension of the HPM framework to treat, within a unified mathematical formalism, this complete loading case, which has practical applications in compliant mechanisms, micro-electromechanical systems (MEMSs), and auxetic structures. The paper provides a complete mathematical formulation and explicit derivation of the HPM solution terms up to the third order and a rigorous demonstration of the method’s convergence, with quantitative error estimates and the establishment of a practical domain of validity, γ₁ < 30°, for an accuracy below 0.5%. As a direct consequence of this analytical advancement, we derive a series of practical engineering tools: nomograms, simplified empirical formulas, interaction diagrams, and a systematic six-step design procedure, which includes an adaptive algorithm for selecting the auxiliary parameter η to optimize convergence. The solution’s structure also lends itself to AI-based optimization frameworks, demonstrating how HPM solutions can serve as a foundation for machine learning surrogates and automated multi-objective optimizations. HPM proves to be a robust and efficient alternative, providing semi-analytical solutions in the form of convergent series without requiring an explicitly small physical parameter. This enables a direct parametric understanding of the structural response and offers rapid tools for the conceptual and preliminary sizing phases, thereby complementing the intensive numerical methods used in the final design stages. Full article

In today’s digital era, cyberattacks pose a critical threat to networks of all scales, from local systems to global infrastructures. Intrusion detection systems (IDSs) are essential for identifying and mitigating such threats. However, existing machine learning-based IDS often suffer from low detection accuracy, heavy reliance on manual feature extraction, and limited coverage of attack categories. To address these limitations, we propose a modular, deployment-ready intrusion detection framework that integrates multiple heterogeneous datasets through a hybrid transformer–multilayer perceptron (Transformer–MLP) architecture. The system employs three parallel Transformer–MLP models, each specialized for a distinct dataset, whose probabilistic outputs are fused using a weighted decision-level strategy. Unlike traditional feature-level fusion, this strategy ensures module independence, eliminates the need for global retraining when adding new components, and provides seamless modular scalability. The framework accurately identifies twenty-one traffic categories, including one benign and twenty attack classes, derived from a unified mapping across multiple heterogeneous sources to ensure a consistent cross-dataset taxonomy. By combining advanced contextual representation learning with ensemble-based probabilistic fusion, the framework demonstrates high detection accuracy and practical applicability in real-world network environments. The Transformer module captures complex contextual dependencies, while the MLP performs final classification. Class imbalance is mitigated via adaptive synthetic sampling (ADASYN), synthetic minority over-sampling technique (SMOTE), edited nearest neighbor (ENN), and class weight adjustments. Empirical evaluation demonstrates the framework’s high effectiveness: for binary classification, it achieves 99.98% on CICIDS2017, 99.19% on NSL-KDD, and 99.98% on NF-BoT-IoT-v2; for two-stage multi-class classification, 99.56%, 99.55%, and 97.75%; and for one-phase multi-class classification, 99.73%, 99.07%, and 98.23%, respectively. Moreover, the framework enables real-time deployment with 4.8–6.9 ms latency, 9800–14,200 fps throughput, and 412–458 MB memory. These results outperform existing multi-dataset IDS approaches, highlighting the architectural effectiveness, robustness, and practical applicability of the proposed framework. Full article

Calcium silicate hydrate (C(-A)-S-H) and its aluminosilicate counterpart (C-A-S-H) constitute the principal binding phases in Portland cement and blended systems, governing mechanical strength and durability. This paper presents a summary of the work related to dehydration of C(-A)-S-H and rehydration of dehydrated C(-A)-S-H. Their thermal dehydration, a key process for cement recycling, induces profound multi-scale transformations: at the atomic level, it alters calcium and aluminum coordination environments and disrupts chemical bonding; at the chain-structure level, it causes depolymerization of the silicate/aluminosilicate networks; and at the microstructural level, it leads to changes in nanoscale particle morphology, aggregation state, and pore structure, creating a metastable, defect-rich, high-energy state distinct from the original C(-A)-S-H. The subsequent rehydration of this dehydrated C(-A)-S-H, which is not a simple reversal but a distinct dissolution–precipitation process, enables microstructural reconstruction and restored reactivity upon contact with water. This rehydration capacity is fundamentally exploited in thermally activated recycled cement—a novel binder concept that leverages dehydration-induced metastability for renewed strength development. Understanding these interconnected processes, influenced by factors like temperature, humidity, rate, and aluminum content, is critical for advancing sustainable cement technology, enabling the design of high-performance recycled cement and concrete, and facilitating the recycling of cementitious materials. Full article

Given that freeze–thaw damage of prestressed concrete significantly threatens structural service life and that existing conventional simulation techniques fail to capture prestress time series, this paper proposes a deep learning prediction model based on the Transformer model. The model integrates a multi-head self-attention mechanism and positional encoding to effectively capture long-range dependencies in prestressed time series. It enhances temporal modeling capability through a 128-dimensional high-dimensional feature space (chosen to balance representation capacity and computational efficiency for the dataset scale) and a 4-layer encoder stacking structure. A dataset was constructed using time-series data from three prestressed concrete components subjected to 50 freeze–thaw cycles. The F-a component was used as the training set, while F-b and F-c served as the testing sets. During the training phase, a Noam learning rate scheduler, gradient clipping, and an early stopping strategy were employed. The results indicate that the training strategy enables the loss function to converge quickly without overfitting, demonstrating good generalization performance. The prediction model performs well on the F-a and F-c datasets, with determination coefficients (R²) of 0.8404 and 0.8425, and corresponding Mean Absolute Error (MAE) of 61.71 MPa and 57.41 MPa, respectively. It can accurately track the periodic variation trend of prestress, demonstrating the model’s effectiveness in prestress prediction. This model provides a new technical tool for the health monitoring and performance prediction of prestressed concrete structures in freeze–thaw environments. Full article

Cold recycled asphalt mixtures incorporate a high amount of reclaimed asphalt pavement (RAP), which offers more economic and environmental advantages than hot recycling techniques. Nevertheless, the presence of aged RAP binder frequently leads to reduced low-temperature performance and uncertainty in mechanical response. The influence of slack wax on full-depth reclamation (FDR) mixtures with bitumen emulsion is assessed in this study using a dual-scale approach. The approach integrates both chemical and rheological binder-scale characterization with mixture-scale mechanical performance with variability assessment. At the binder scale, the binder beam rheometer (BBR), dynamic shear rheometer (DSR), and Fourier transform spectroscopy (FTIR) indicated that the addition of 10% recycling agent improved the low-temperature properties. The improvement at lower temperatures shifted the BBR temperature from −23 °C to −30 °C, which ultimately resulted in a less negative ΔT_c, from −0.7 °C to −0.3 °C, and moderately improved high-temperature stiffness. Moreover, the FTIR analysis indicated a reduction in oxidation-related chemical markers, as evidenced by the reduced carbonyl and sulfoxide indices. At the mixture scale, complex modulus shows a systematic decrease in stiffness, particularly at lower temperatures of −25 °C and −15 °C, and a reduced phase angle, suggesting higher elastic dominance. The reduction is observed at all temperatures and frequencies. Rutting resistance of both formulations remains below 3% after 30,000 cycles. The complex modulus coefficient of variability was found to be 8–12%, comparable to that of hot mix asphalt. In conclusion, the findings suggest that the recycling agent provides a controlled restoration of viscoelastic properties in cold recycled mixtures without compromising structural integrity. This underscores the significance of multi-scale evaluation and variability assessment when characterizing high RAP recycling agents under the studied materials and dosage. Full article

Autonomous aerial refueling is a key technology for enhancing the endurance of unmanned aerial vehicles; however, the wingtip vortices generated by the tanker create a strong three-dimensional wake-vortex flow field, whose downwash and lateral airflow can impose significant rolling moments on the follower Unmanned Aerial Vehicle (UAV), posing a serious threat to flight safety. To address this issue, this study proposes an integrated framework that combines wake-vortex risk-field modeling with optimal path planning. The classical Hallock–Burnham (HB) model is first employed to predict vortex descent and lateral transport, while a two-phase model is used to characterize the temporal decay of vortex circulation. The predicted vortex parameters are then coupled with the UAV’s aerodynamic characteristics, and the rolling-moment coefficient (RMC) is introduced as a risk metric to compute its spatiotemporal distribution in three dimensions, thereby transforming the invisible wake-vortex disturbance into a visualizable and quantifiable dynamic three-dimensional risk map. On this basis, a wake-vortex-aware path-planning algorithm based on particle swarm optimization (PSO) is developed, incorporating adaptive weighting and elitist mutation strategies. A multi-objective cost function considering path length, safety, and smoothness is further constructed to search for an optimal safe path under wake-vortex influence. Simulation results indicate that, compared with the classical A* and Rapidly-Exploring Random Tree (RRT) algorithms, the proposed method reduces cumulative risk exposure by approximately 90% and 75%, respectively, while limiting the increase in path length to about 8% (significantly lower than the increases of 40% for A* and 44% for RRT). In addition, the maximum turning angle is constrained within 10°, and the computation time remains around 0.052 s, satisfying real-time requirements. These results demonstrate that the proposed method can generate safe, efficient, and dynamically feasible paths for UAV aerial refueling and provide a valuable reference for wake-vortex avoidance in similar aerospace missions. Full article

This study aimed to analyze changes in vegetation, built-up areas, and population growth in Lahore city from 1990 to 2023. The data was acquired from Google Earth Engine, and the spectral bands were retrieved from Landsat 5 and Landsat 8. The decadal analysis of the landscape was conducted from 1993 to 2001, 2001 to 2012, and from 2013 to 2023. Further analysis was conducted in ArcGIS version 10.3 to evaluate the Normalized Difference Vegetation Index and the Normalized Difference Built-up Index to assess vegetation and built-up areas, respectively. To analyze the urban population of Lahore, data were obtained from the Global Human Settlement Layer for 1990, 2000, 2010, and 2020. Results revealed that the total vegetated area of Lahore city decreased from 1453.0 km² in 1993–2001 to 788.2 km² in 2013–2023. Moreover, the urban built-up area expanded from 319.6 km² in 1993–2001 to 966.8 km² in 2013–2023. Sub-district-level analysis indicated that Model Town and Raiwind areas of Lahore depicted better vegetation recovery in this decade. The population of Lahore has been increasing steadily, with the 2010s being a particularly rapid period of growth. The projections for 2030 also depict a continuous growth pattern. This study was further developed by integrating multi-decadal averaging coupled with selected-year analysis to distinguish gradual land transformation from relatively accelerated phases of urban expansion of Lahore. Also, by combining NDVI and NDBI values on both Lahore and its tehsil level, the research provides a collective sub-district- and district-level perspective into the spatial heterogeneity of peri-urban transformations. The findings of the study explain how major infrastructural projects shape the urban growth patterns of cities like Lahore and cause a decline in the green areas of fast-growing cities in South Asia. This study further highlights the consequences of unplanned urban expansion in regions where high population growth has compromised green infrastructure and threatened ecological balance. In addition, it supports several Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), and SDG 15 (Life on Land) by providing spatial evidence of urban expansion of the city and losses of its green spaces. The findings offer empirical insights to support climate-resilient developments. The study also demonstrates the necessity of integrating green infrastructure and providing robust strategies for forthcoming urban planning projects and policy development regarding urban expansion. Full article

The lack of reliable diagnostic tools and relapse monitoring for latent tuberculosis infection (LTBI) constitutes a major obstacle to global tuberculosis (TB) control. This highlights an urgent need for robust animal models and predictive biomarkers. To address this, we report the successful establishment of a rapid murine model of recapitulating the active, latent, and relapse phases of TB within a compressed ten-week timeframe—hence termed the rapid multi-stage TB murine model. In this model, mice were first intravenously infected with Mycobacterium tuberculosis, followed by a four-week isoniazid (INH) regimen starting at two weeks post-infection. By week six, pulmonary bacterial loads in most mice dropped below the detection limit, signifying the establishment of latency. Reactivation was subsequently triggered by a four-week administration of anti-TNF-α (Tumor Necrosis Factor-α) monoclonal antibody. Leveraging this reproducible and time-efficient model, we performed transcriptomic profiling of peripheral blood and identified a distinct sixteen-gene signature (including Ets2, Fam111a, Fosl2, Gadd45b, Nfkbid, Rgs1, Bhlhe40, Il1r2, Clec2d, Kmo, Lynx1, Papd4, Trim34a, Wrb, Nlrp12, Spns1) that dynamically tracks disease progression. Collectively, these findings not only provide a valuable and efficient preclinical tool but also deliver transformable candidate biomarkers with immediate potential to guide the development of novel diagnostic strategies for LTBI surveillance and management. Full article

Background: Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), the major subtypes of non-small cell lung cancer (NSCLC), exhibit distinct molecular landscapes that demand precision in prognosis and therapy. While deep learning models can achieve high predictive accuracy, their black-box nature limits clinical translation. Methods: We introduce AttentioFuse, an interpretable deep learning framework employing a Reactome-guided mid-fusion strategy for multi-omics integration. AttentioFuse builds on three pillars: (i) dual-phase learning with omics-specific encoders to preserve modality-unique patterns, (ii) hierarchical attention mechanisms (cross-omics, feature-level, and fusion-layer) to quantify layer contributions dynamically, and (iii) integrated explainability combining DeepSHAP and global attention weights for gene-to-pathway interpretation. Two depth variants are instantiated under identical priors: a three-layer configuration (3F) for main discrimination and a five-layer configuration (AttentioFuse-5X) for deeper hierarchical interpretation; the 5X variant is trained end-to-end and yields comparable accuracy while enhancing pathway-level resolution. Results: Evaluated on The Cancer Genome Atlas (TCGA) LUAD/LUSC cohorts, AttentioFuse matches state-of-the-art performance in TNM staging while uncovering actionable biological insights, including pan-NSCLC AKT/mTOR metabolic control, histology-divergent Notch signaling roles, and additional pathways related to developmental reactivation, microbiota-associated metastasis, and extracellular matrix remodeling. Conclusions: By design, AttentioFuse-5X bridges predictive performance with hierarchical, pathway-resolved explanations, advancing oncology by transforming black-box predictions into biologically grounded decision support. Full article

The northern agro-pastoral ecotone of China faces persistent trade-offs among cultivated land (CL) protection, energy development, water constraints, and ecological restoration, posing challenges for sustainable human–land interactions. Focusing on Yulin City from 1980 to 2020, this study develops an integrated diagnostic framework coupling pattern–process–trend–mechanism modules to analyze the spatiotemporal evolution, transition pathways, and driving forces of CL change. Results show that CL dynamics over four decades were shaped by nonlinear interactions among natural conditions, policies, economic development, and technological progress. Spatially, CL changes exhibited a distinct divergence, with ecological-driven contraction in the southern region and sandy land-based compensation in the north. Temporally, the transformation evolved from a gradual, nature-dominated stage to a policy-intensive phase characterized by abrupt shifts, followed by a refined regulation stage with multi-factor synergies. Policy interventions and economic incentives emerged as dominant drivers of CL spatial heterogeneity, with interacting factors exerting bidirectional effects. Building on these findings, a Zoning–Optimization–Synergy (ZOS) framework is proposed to support adaptive land governance, emphasizing differentiated management and cross-sector coordination. This study offers a transferable diagnostic approach for understanding CL dynamics in fragile ecotones and provides insights for managing the water–energy–food nexus under ecological transition and climate change. Full article

Response latency and sensor noise are universal challenges in closed-loop control systems. In the context of semi-active suspensions, these issues also exist and manifest as critical bottlenecks. Due to the highly transient nature of road shocks, the inherent physical actuation delays of the hardware, combined with the phase lag introduced by traditional signal filtering, often cause the control response to significantly lag behind the physical excitation. To address this issue from a predictive perspective, this study proposes a Physics-Informed Gated Convolutional Neural Network (PI-GCNN) designed to predict future multi-modal energy evolution, thereby enabling feedforward control. Unlike traditional feedback mechanisms, the proposed framework employs the Continuous Wavelet Transform (CWT) to convert short-horizon inertial data into time–frequency scalograms, effectively isolating transient shock features from background vibrations. A novel physics-guided gating mechanism is embedded within the network architecture to regulate feature activation. This mechanism is trained using an asymmetric sparse physics loss, which combines L1 regularization with adaptive spectral consistency constraints to enforce noise suppression on flat roads while ensuring sensitivity to impacts. Extensive validation was conducted using high-fidelity heavy truck simulations and the public PVS 9 real-world dataset. The results confirm that the PI-GCNN achieves a predictive phase lead of approximately 100–200 ms over real-time baselines, creating a valuable actuation window for suspension dampers. Furthermore, the model demonstrates exceptional computational efficiency, with a parameter count of 0.10 M and a single-frame inference latency of 0.25 ms, making it highly suitable for deployment on resource-constrained automotive edge computing platforms. Full article