Search Results (4,509)

Rapid population growth and accelerating urbanization are intensifying the demand for construction materials, particularly concrete, which is predominantly produced with Portland cement and natural aggregates. This reliance imposes substantial environmental burdens through resource depletion and greenhouse gas emissions. Within the framework of sustainable construction, recycled aggregates and industrial by-products such as fly ash, slags, crushed glass, and other secondary raw materials have emerged as viable substitutes in concrete production. At the same time, three-dimensional concrete printing (3DCP) offers opportunities to optimize material use and minimize waste, yet it requires tailored mix designs with controlled rheological and mechanical performance. This review synthesizes current knowledge on the use of recycled construction and demolition waste, industrial by-products, and geopolymers in concrete mixtures for 3D printing applications. Particular attention is given to pozzolanic activity, particle size effects, mechanical strength, rheology, thermal conductivity, and fire resistance of recycled-based composites. The environmental assessment is considered through life-cycle analysis (LCA), emphasizing carbon footprint reduction strategies enabled by recycled constituents and low-clinker formulations. The analysis demonstrates that recycled-based 3D printable concretes can maintain or enhance structural performance while mix-level (cradle-to-gate, A1–A3) LCAs of printable mixes report CO₂ reductions typically in the range of ~20–50% depending on clinker substitution and recycled constituents—with up to ~48% for fine recycled aggregates when accompanied by cement reduction and up to ~62% for mixes with recycled concrete powder, subject to preserved printability. This work highlights both opportunities and challenges, outlining pathways for advancing durable, energy-efficient, and environmentally responsible 3D-printed construction materials. Full article

Three-dimensional ground-penetrating radar can quickly visualize the internal condition of the road; however, it faces challenges such as data splicing difficulties and image noise interference. Scanning antenna and lane size differences, as well as equipment and environmental interference, make the radar image difficult to interpret, which affects disease identification accuracy. For this reason, this paper focuses on road radar image splicing and noise reduction. The primary research includes the following: (1) We make use of backward projection imaging algorithms to visualize the internal information of the road, combined with a high-precision positioning system, splicing of multi-lane data, and the use of bilinear interpolation algorithms to make the three-dimensional radar data uniformly distributed. (2) Aiming at the defects of the low computational efficiency of the traditional adaptive median filter sliding window, a Deep Q-learning algorithm is introduced to construct a reward and punishment mechanism, and the feedback reward function quickly determines the filter window size. The results show that the method is outstanding in improving the peak signal-to-noise ratio, compared with the traditional algorithm, improving the denoising performance by 2–7 times. It effectively suppresses multiple noise types while precisely preserving fine details such as 0.1–0.5 mm microcrack edges, significantly enhancing image clarity. After processing, images were automatically recognized using YOLOv8x. The detection rate for transverse cracks in images improved significantly from being undetectable in mixed noise and original images to exceeding 90% in damage detection. This effectively validates the critical role of denoising in enhancing the automatic interpretation capability of internal road cracks. Full article

Xiamen Bay (XMB), a representative semi-enclosed bay, demonstrates hydrodynamic conditions and water exchange characteristics that are significantly influenced by alterations in the coastline. The three-dimensional hydrodynamic model and remote sensing interpretation techniques were utilized to examine coastline changes and evaluated the spatio-temporal variations in water residence time in XMB from 1955 to 2021. The results indicate that the coastline of the XMB has been considerably modified by extensive reclamation activities. The total reclaimed area reached up to 188.08 km² during the period of 1955–2021, resulting in a 17.8% reduction in the total bay area. The average residence time increased from 13.28 days in 1955 to 16.94 days in 2003 and then decreased to 16.12 days because of ecological restoration initiatives. Spatially, water residence time increased from the outer sea towards the inner bay, with the high value observed in the northwest part of XMB while the low value was observed in the southeastern region. Among the various sub-regions, Tong’an Bay experienced the most significant change in residence time, followed by the West Sea. Conversely, the Dadeng Waters and Jiulong River Estuary showed relatively minor increases in residence time. The primary factors influencing variations in water residence time are large-scale reclamation projects and ecological restoration measures. These findings provide a significant scientific foundation and technical support for the integrated management of the coastal zone and ecological restoration construction in XMB. Full article

Maize (Zea mays) is a key crop, with its growth impacted by drought stress. Accurate, non-destructive assessment of drought severity is crucial for precision agriculture. VIS/NIR reflectance spectroscopy is widely used for estimating plant parameters and detecting stress. However, the relationship between key parameters—such as chlorophyll and water content—and VIS/NIR spectra under drought conditions in maize remains unclear, lacking comprehensive models and validation. This study aims to develop a non-destructive and accurate method for predicting chlorophyll and water content in maize leaves under drought stress using VIS/NIR spectroscopy. Specifically, maize leaf reflectance spectra were collected under varying drought stress conditions, and the effects of different spectral preprocessing methods, dimensionality reduction techniques, and machine learning algorithms were evaluated. An optimal data processing pipeline was systematically established and deployed on an edge computing unit to enable rapid, non-destructive prediction of chlorophyll and water content in maize leaves. The experimental results demonstrated that the combination of stepwise regression (SR) for feature selection and a stacking regression model achieved the best performance for chlorophyll content prediction (${\mathrm{R}}_{\mathrm{p}}^2$ = 0.8740, RMSE_p = 0.2768). For leaf water content prediction, random forest (RF) feature selection combined with a stacking model yielded the highest accuracy (${\mathrm{R}}_{\mathrm{p}}^2$ = 0.7626, RMSE_p = 4.12%). This study confirms the effectiveness and potential of integrating VIS/NIR spectroscopy with machine learning algorithms for monitoring drought stress in maize, offering a valuable theoretical foundation and practical reference for non-destructive crop physiological monitoring in precision agriculture. Full article

Twin-roll strip casting (TRSC) is a key development in near-net-shape casting technology, offering the potential for high-efficiency and low-cost production. During the TRSC process, the solidification characteristics of the strip are largely governed by the configuration of the melt delivery system as well as by various process parameters. In this study, a three-dimensional model of low-carbon steel strip casting was developed using ProCAST software to investigate microstructure evolution under industrial-scale conditions. Simulation results revealed that the solidified strip exhibits a typical three-layer structure: a surface equiaxed grain zone in contact with the cooling rolls, a subsurface columnar grain zone, and a central equiaxed grain zone. Introducing side holes into the delivery system promoted the formation of a distinct columnar grain region near the side dams, resulting in a reduction in the average grain size in this region from 43.7 μm to 38.2 μm compared to the delivery system without side holes. Increasing the heat transfer coefficient at the interface between the molten pool and the cooling rolls significantly enlarged the columnar grain zone. This change had little effect on the average grain size and grain density, with the average grain size remaining close to 37 μm and the grain density variation being less than 0.7%. In contrast, when the casting speed was raised from 50 m min⁻¹ to 70 m min⁻¹, a reduction in the area of the columnar grain zone was observed, while the average grain size decreased slightly (by less than 0.5 μm), and the grain density increased accordingly. This study provides valuable insights for optimizing process parameters and designing more effective melt delivery systems in industrial twin-roll strip casting. Full article

This study proposes a dimensionality reduction method based on proper orthogonal decomposition (POD) for numerically solving the Schrödinger equation by optimizing the coefficient vectors of the Crank–Nicolson finite element (CNFE) solution. The fully discrete CNFE scheme is derived from the Schrödinger equation, with the stability and convergence of the CNFE solution rigorously established. Utilizing the POD technology, POD basis functions are constructed and a reduced-dimension model is formulated. The uniqueness and unconditional stability are proved, and the error estimate of the reduced-dimension solution is derived. With a $100\times 100$ spatial grid, the reduced-dimension CNFE (RDCNFE) method employing POD technology reduces the degrees of freedom each time step from ${101}^2$ to 6. Numerical results show that for each additional second of simulation time, the CPU runtime of the standard CNFE method increases by several seconds, while the RDCNFE method increase remains below one second. This demonstrates that the reduced-dimension method significantly enhances computational efficiency for the Schrödinger equation while preserving numerical accuracy. Full article

The adverse propagation environment in underground coal mine tunnels caused by enclosed spaces, rough surfaces, and dense scatterers severely degrades reliable wireless signal transmission, which further impedes the deployment of IoT applications such as gas monitors and personnel positioning terminals. However, the conventional power enhancement solutions are infeasible for the underground coal mine scenario due to strict explosion-proof safety regulations and battery-powered IoT devices. To address this challenge, we propose singular value decomposition-based Lagrangian optimization (SVD-LOP) to minimize transmit power at the mining base station (MBS) for IRS-assisted coal mine wireless communication systems. In particular, we first establish a three-dimensional twin cluster geometry-based stochastic model (3D-TCGBSM) to accurately characterize the underground coal mine channel. On this basis, we formulate the MBS transmit power minimization problem constrained by user signal-to-noise ratio (SNR) target and IRS phase shifts. To solve this non-convex problem, we propose the SVD-LOP algorithm that performs SVD on the channel matrix to decouple the complex channel coupling and introduces the Lagrange multipliers. Furthermore, we develop a low-complexity successive convex approximation (LC-SCA) algorithm to reduce computational complexity, which constructs a convex approximation of the objective function based on a first-order Taylor expansion and enables suboptimal solutions. Simulation results demonstrate that the proposed SVD-LOP and LC-SCA algorithms achieve transmit power peaks of $20.8\mathrm{dBm}$ and $21.4\mathrm{dBm}$, respectively, which are slightly lower than the $21.8\mathrm{dBm}$ observed for the SDR algorithm. It is evident that these algorithms remain well below the explosion-proof safety threshold, which achieves significant power reduction. However, computational complexity analysis reveals that the proposed SVD-LOP and LC-SCA algorithms achieve $\mathcal{O}\left(N^3\right)$ and $\mathcal{O}\left(N^2\right)$ respectively, which offers substantial reductions compared to the SDR algorithm’s $\mathcal{O}\left(N^7\right)$. Moreover, both proposed algorithms exhibit robust convergence across varying user SNR targets while maintaining stable performance gains under different tunnel roughness scenarios. Full article

Mechanical exfoliation remains the most common method for producing high-quality two-dimensional (2D) materials, but its inherently low yield requires screening large numbers of samples to identify usable flakes. Efficient optimization of the exfoliation process demands scalable methods to analyze deposited material across extensive datasets. While machine learning clustering techniques have demonstrated ~95% accuracy in classifying 2D material thicknesses from optical microscopy images, current tools are limited by slow processing speeds and heavy reliance on manual user input. This work presents an open-source, GPU-accelerated software platform that builds upon existing classification methods to enable high-throughput analysis of 2D material samples. By leveraging parallel computation, optimizing core algorithms, and automating preprocessing steps, the software can quantify flake coverage and thickness across uncompressed optical images at scale. Benchmark comparisons show that this implementation processes over 200× more pixel data with a 60× reduction in processing time relative to the original software. Specifically, a full dataset of2916 uncompressed images can be classified in 35 min, compared to an estimated 32 h required by the baseline method using compressed images. This platform enables rapid evaluation of exfoliation results across multiple trials, providing a practical tool for optimizing deposition techniques and improving the yield of high-quality 2D materials. Full article

Conventional fracturing fluids, while essential for large-volume stimulation of unconventional reservoirs, often induce significant reservoir damage through water retention and capillary trapping. To address this problem, this study developed a novel superhydrophobic nano-viscous drag reducer (SN-DR), synthesized through a multi-monomer copolymerization and silane modification strategy, which enhances structural stability and minimizes reservoir damage. The structure and thermal stability of SN-DR were characterized by FT-IR, ¹H NMR, and TGA. Rheological evaluations demonstrated that the gel fracturing fluid exhibits a highly stable three-dimensional network structure, with a G′ maintained at approximately 3000 Pa and excellent shear recovery under cyclic stress. Performance tests showed that a 0.15% SN-DR achieved a drag reduction rate of 78.1% at 40 L/min, reduced oil–water interfacial tension to 0.91 mN·m⁻¹, and yielded a water contact angle of 152.07°, confirming strong hydrophobicity. Core flooding tests revealed a flowback rate exceeding 50% and an average permeability recovery of 86%. SEM and EDS indicated that the gel formed nanoscale, tightly packed papillary structures on core surfaces, enhancing roughness and reducing water intrusion. The study demonstrates that gel fracturing fluid enhances structural stability, alters wettability, and mitigates water-blocking damage. These findings offer a new strategy for designing high-performance fracturing fluids with integrated drag reduction and reservoir protection properties, providing significant theoretical insights for improving hydraulic fracturing efficiency. Full article

Mango is considered a high-value tropical fruit, and its commercial and consumer acceptance depends on internal and external quality attributes such as Total Soluble Solids (TSS), Dry Matter Content (DMC), firmness, ripeness, and surface defects. In recent years, non-destructive sensing technologies such as Near-Infrared Spectroscopy (NIRS) and Hyperspectral Imaging (HSI) have gained prominence for their ability to quickly and accurately evaluate mango quality. In this study, 101 articles published within the last ten years, were systematically retrieved, and 85 research papers were selected for detailed analysis. The review focuses on statistical analysis, conventional machine learning, deep learning, and transformer-based methods applied to mango quality assessment. The objective is to systematically review and analyse data-driven models for non-destructive mango grading using NIRS and HSI technologies, with particular emphasis on data collection methods, preprocessing techniques, dimensionality reduction, and predictive modelling approaches. This review aims to identify the most effective and widely adopted machine learning and deep learning methods, especially transformer models, for accurate and real-time mango quality assessment. Furthermore, it highlights key quality traits evaluated, current research gaps, and future opportunities to advance intelligent, real-time, and automated mango grading systems for practical use in the fruit industry. Full article

Background and Objectives: Cancer pain continues to be a major clinical problem nowadays. This study aims to evaluate the World Health Organization (WHO) analgesic ladder effectiveness in patients with colorectal cancer and develop machine learning models to predict treatment response for precision pain management. Materials and Methods: In a retrospective observational study, a total of 107 oncological patients were analyzed, with a detailed subgroup analysis of 42 patients with colorectal cancer, hospitalized between July and September in 2022. The pain assessment used numerical rating scales at baseline and 2–3 weeks follow-up. Clinical variables included demographics, disease staging, metastatic patterns, analgesic progression, and medication usage. Machine learning algorithms (e.g., Random Forest, CatBoost, XGBoost, and Neural Network) were used to predict pain reduction outcomes. The UMAP dimensionality reduction and clustering identified the patient phenotypes. Results: Statistical analyses included descriptive methods, Chi-square and Mann–Whitney tests, and the models’ performance was evaluated by AUC. Among patients with colorectal cancer, 73.8% achieved clinically pain improvement, with a mean reduction of 2.62 points and median improvement of 3.00 points. The metastatic site significantly affected outcomes: visceral metastases patients showed median improvement of 3.00 points with high variability, patients with bone metastases demonstrated heterogeneous responses (range: −2.00 to +8.00 points), while non-metastatic patients exhibited consistent improvement. Random Forest achieved optimal predictive performance (AUC: 0.9167), identifying the baseline pain score, bone metastases, Fentanyl usage, anticonvulsants, and antispasmodics as key predictive features. The clustering analysis revealed two distinct phenotypes, requiring different analgesic intensities. Conclusions: This study validates the WHO analgesic ladder effectiveness while demonstrating superior outcomes in patients with colorectal cancer. The machine learning models successfully predict the treatment response with excellent discriminative ability, supporting precision medicine implementation in cancer pain management. Full article

Near-wall flow around circular cylinders is commonly encountered in various engineering applications, such as submarine pipelines and river-crossing conduits. The wake structure significantly influences local flow stability and has become a critical focus in fluid dynamics research. Specifically, when the gap ratio (G/D) ranges from 0.1 to 1.0, the interaction mechanism between the wall and the wake structure remains poorly understood. Moreover, the combined effects of the Reynolds number (Re) and gap ratio on the flow field require further investigation. In this study, a series of experimental measurements were conducted using two-dimensional, two-component particle image velocimetry (2D–2C PIV) to examine the influence of G/D and Re on the near-wall wake characteristics. The results indicate that, at a gap ratio of G/D = 0.1, the gap flow exhibits pronounced curling into the recirculation region, where the lower vortex is entrained and actively participates in wake evolution. When G/D ≥ 0.3, an increase in Re leads to a reduction in the lengths of both the upper and lower shear layers, a delayed attenuation of the wall-side shear layer, and a gradual symmetrization and contraction of the recirculation region behind the cylinder. Further analysis reveals that the evolution of the secondary vortex is strongly influenced by the combined effects of Re and G/D. Specifically, at Re = 3300 and G/D ≤ 0.3, the secondary vortex migrates away from the wall toward the upper shear layer, where it merges with the upper vortex. For 0.5 ≤ G/D ≤ 0.7, it interacts with the lower vortex, while at G/D = 1.0, it evolves independently downstream along the wall. At G/D = 0.5, the secondary vortex merges with the upper vortex at Re = 1100, whereas at Re = 5500, it interacts with the lower vortex instead. These findings contribute to a deeper understanding of the complex flow structures associated with near-wall cylinder wakes and offer valuable theoretical insights for engineering applications involving submarine pipelines in bottom-mounted or partially suspended configurations. Full article

Soundscape monitoring has become an increasingly important tool for studying ecological processes and supporting habitat conservation. While many recent advances focus on identifying species through supervised learning, there is growing interest in understanding the soundscape as a whole while considering patterns that extend beyond individual vocalizations. This broader view requires unsupervised approaches capable of capturing meaningful structures related to temporal dynamics, frequency content, spatial distribution, and ecological variability. In this study, we present a fully unsupervised framework for analyzing large-scale soundscape data using deep learning. We applied a convolutional autoencoder (Soundscape-Net) to extract acoustic representations from over 60,000 recordings collected across a grid-based sampling design in the Rey Zamuro Reserve in Colombia. These features were initially compared with other audio characterization methods, showing superior performance in multiclass classification, with accuracies of 0.85 for habitat cover identification and 0.89 for time-of-day classification across 13 days. For the unsupervised study, optimized dimensionality reduction methods (Uniform Manifold Approximation and Projection and Pairwise Controlled Manifold Approximation and Projection) were applied to project the learned features, achieving trustworthiness scores above 0.96. Subsequently, clustering was performed using KMeans and Density-Based Spatial Clustering of Applications with Noise (DBSCAN), with evaluations based on metrics such as the silhouette, where scores above 0.45 were obtained, thus supporting the robustness of the discovered latent acoustic structures. To interpret and validate the resulting clusters, we combined multiple strategies: spatial mapping through interpolation, analysis of acoustic index variance to understand the cluster structure, and graph-based connectivity analysis to identify ecological relationships between the recording sites. Our results demonstrate that this approach can uncover both local and broad-scale patterns in the soundscape, providing a flexible and interpretable pathway for unsupervised ecological monitoring. Full article

This study investigates slope stability in an open-pit mining area by integrating engineering geological surveys, field investigations, and laboratory rock mechanics tests. A coordinated multimethod analysis was carried out using finite element-based numerical simulations from both two-dimensional and three-dimensional perspectives. The integrated approach revealed deformation patterns across the slopes and established a multiscale analytical framework. The results indicate that the slope failure modes primarily include circular and compound types, with existing step slopes showing a potential risk of wedge failure. While the designed slope meets safety requirements under three working conditions overall, the strongly weathered layer in profile XL3 requires a slope angle reduction from 38° to 37° to comply with standards. Three-dimensional simulations identify the main deformations in the middle-lower sections of the western area and zones B and C, with faults located at the core of the deformation zone. Rainfall and blasting vibrations significantly increase surface tensile stress, accelerating deformation. Although wedges in profiles XL1 and XL4 remain generally stable, coupled blasting–rainfall effects may still induce potential collapse in fractured areas, necessitating preventive measures such as concrete support and bolt support, along with real-time monitoring to dynamically optimize reinforcement strategies for precise risk control. Full article

Poor code locality in virtual memory systems significantly contributes to page faults, leading to degraded system performance. Although many solutions aim to minimize page faults, most rely on clustering techniques that do not quantify the approximation error relative to the optimal solution. In this work, we develop a novel mathematical model based on the Working Set strategy combined with a geometric interpretation of the computational process via a Hasse diagram. This approach enables the reduction of the problem dimensionality and facilitates identification of critical control states under realistic constraints. We formalize the minimization of expected page faults as a discrete optimization problem with well-defined functionals and constraints. Experimental evaluation demonstrates that our model achieves lower average page faults and execution times compared to classical algorithms, especially under poor code locality conditions. Our method also provides a foundation for obtaining ε-optimal solutions and paves the way for designing efficient and cost-effective page replacement algorithms with provable guarantees. These contributions establish both theoretical and practical advances in virtual memory management. Full article