Search Results (20,944)

Soil texture is recognised as one of the key physical properties of soil. Although traditional laboratory testing methods can determine soil texture information with high accuracy, they are often considered time-consuming and costly. To achieve rapid and accurate acquisition of soil texture information, this study proposes RVFM, a hybrid deep learning model designed for soil texture detection using microscopic images. The model integrates a CNN branch for extracting multi-dimensional texture features with a Transformer branch for capturing global positional information, fused via a cross-attention module. This architecture effectively captures microscopic distribution characteristics to estimate soil composition proportions. Experimental results demonstrate high precision, with prediction coefficients (R²) for sand, silt, and clay reaching 0.971, 0.954, and 0.931, respectively. Corresponding Root Mean Square Errors (RMSE) were recorded at 3.789, 2.842, and 2.780. The test results outperform those of other classical network models, and the model shows better fitting performance in generalisation tests, demonstrating certain practical value Full article

Levees with double-layered foundations are characterized by a weakly permeable upper layer and a highly permeable sand layer beneath, which makes them susceptible to internal erosion, particularly backward erosion piping (BEP). Therefore, locating BEP channels before the failure of a levee is crucial for ensuring the safety of levee projects. In this study, a novel method is proposed for detecting BEP channels efficiently. This method involves applying the successive linear estimator (SLE) to fuse multipoint measured voltage to characterize the inner levee structure. Therefore, the BEP channels can be recognized from the details of the levee structure. This method is named three-dimensional geostatistical electrical resistivity tomography (3D GERT) in this study. To validate the performance of GERT, a custom-developed indoor sandbox device was used for physical BEP conductivity detection tests, and the results were analyzed via the SLE to assess the accuracy of channel engraving. The tests revealed that the surface sand was initially expelled from the piping exit, followed by the formation of a concentrated piping channel that extended upstream. The erosion depth at the piping exit was observed to be deeper than that of the main channel. This study demonstrated that 3D GERT, when the SLE was used as the inversion algorithm, detected BEP channels and achieved an internal erosion dimension deviation of less than 25.5% and a positional erosion dimension deviation within 16.5%. The accuracy of the SLE in mapping BEP channels improved with the use of a more comprehensive electrode distribution and an increased number of electrodes, thus yielding a more precise representation of the channel scale and pattern. The coefficient of determination (R²) between the acquired data and the simulated data generated by 3D GERT was greater than 0.85, demonstrating the capability of the simulated values to track and reproduce the variation trends observed in the acquired data. Thus, the SLE, when used as the inversion algorithm for 3D GERT, reliably represents BEP channels. Full article

In this paper, a microwave metasurface sensor integrated with artificial intelligence (AI) for breast tumor detection is presented. The sensor’s sensitivity is estimated by analyzing shifts in magnitude and the phase of the reflection coefficient ($S_{11}$) obtained from normal and abnormal breast phantoms. The ($S_{11}$) responses of 137 anatomically realistic 3D numerical breast phantoms in standard classes, C1—mostly fatty, C2—scattered fibroglandular, C3—heterogeneously dense, and C4—extremely dense, incorporating different tumor sizes are used as input features. A custom neural network is developed to detect tumor presence using the recorded ($S_{11}$) responses. The model is trained with cross-entropy loss and the AdamW optimizer. The dataset is split into training (70%), validation (15%), and test (15%) sets. The model achieves 99% accuracy, with perfect precision, recall, and F1-score across individual classes. For paired class combinations, accuracies of 71% (C1 with C2) and 65% (C2 with C3) are obtained, while performance degrades to approximately 50% when all four classes are combined. The sensor is fabricated and experimentally validated using two physical breast phantoms, demonstrating reliable detection of a 10 mm tumor. These results highlight the effectiveness of combining microwave metasurface sensing and AI for breast tumor detection. Full article

During the high-intensity mining of shallow-buried thick coal seams, the formation of a water-conducting fracture zone within the overburden is a primary cause of damage to the groundwater system. To address the challenge of balancing efficiency and cost in traditional water-retaining mining methods, this study proposes and validates a trapezoidal strip filling mining technology based on the “span reduction effect”. By developing a mechanical model of a four-sided simply supported thin plate representing the key layer, the fundamental mechanism of the filling body was elucidated. This mechanism involves the active adjustment of the support boundary, which effectively reduces the force span of the key layer. Furthermore, leveraging the fourth-power relationship (w ∝ a⁴) between deflection and span, the bending deformation of the overburden rock is exponentially mitigated. This study employs a four-tiered integrated verification system comprising theoretical modeling, physical simulation, numerical simulation, and engineering field testing: First, theoretical calculations indicate that reducing the effective span of the key layer by 40% can decrease its maximum deflection by 87%. Second, large-scale physical similarity simulations predict that implementing this filling method can significantly control the height of the water-conducting fracture zone, reducing it from 94 m under the collapse method to 58 m, which corresponds to a 45.5% reduction in surface settlement. Third, FLAC^3D numerical simulations further elucidated the mechanical mechanism by which the backfill system transforms stress distribution from “coal pillar-dominated bearing capacity” to “synergistic bearing capacity of backfill and coal pillars”. Shear failure in the critical layer was suppressed, and the development height of the plastic zone was restricted to approximately 54 m, showing high consistency with physical simulation results. Finally, actual measurements of water injection through the inverted hole underground provide direct evidence: The heights of the water-conducting fracture zones in the filling working face and the collapse working face are 59 m and 93 m, respectively, reflecting a reduction of 36.6%. Based on the consistency between measured and simulated results, the numerical model employed in this study has been effectively validated. Research indicates that employing trapezoidal strip filling technology based on principal stress dynamics regulation can effectively promote a shift in the failure mode of the overlying critical layer from “fracture–conduction” to “bending–subsidence”. This mechanism provides a clear mechanical explanation and predictable design basis for the green mining of shallow coal seams. Full article

In urban plantings, mined sand and scoria are commonly used as low-nutrient substrates to improve plant establishment and growth. However, reliance on mined materials conflicts with sustainability policies promoting resource circularity and waste reuse. Construction wastes are readily available, and while their high alkalinity and density may limit plant growth, incorporating organic matter, like pine bark, can ameliorate these issues. We investigated whether construction waste amended with pine bark can support plant growth. We evaluated physical and chemical properties of 12 substrates combining four mineral components—scoria (mined), sand (recycled), crushed concrete (recycled), and crushed rock (recycled)—with pine bark (10%, 20%, and 50% v/v). We then tested eight of these substrates in a container experiment, evaluating the growth of two woody shrubs: Alyogyne huegelii and Goodenia ovata. All mineral components were alkaline (pH 9.2–12.3), with crushed concrete remaining hyper-alkaline despite pine bark addition. Greater pine bark volumes improved air-filled porosity but reduced water retention. Substrates with 50% v/v pine bark had lower plant biomass compared to those with 10% v/v. However, plant biomass was similar across all mineral components. This demonstrates that construction waste–pine bark substrates can support plant growth in urban plantings, supporting broader sustainability goals in cities. Full article

As automotive software continues to grow in scale and timing sensitivity, hardware-independent verification in the early design phase has become increasingly important—especially for safety-critical, body-domain controllers. This study proposes a framework that integrates MBD (Model-Based Design), AUTOSAR (Automotive Open System Architecture) Classic Platform configuration, and vECU (Virtual Electronic Control Unit) execution into a single, repeatable development workflow. Control logic validated in Simulink is translated into AUTOSAR-compliant software, built into a QEMU (Quick EMUlator)-based vECU, and exercised in DRIM-SimHub using both virtual stimuli and a real sensor–actuator signal delivered through a dedicated I/O interface board. Using a seat–slide virtual limit controller as a representative case, the proposed workflow enables consistent reuse of the test scenarios across model-in-the-loop (MiL), software-in-the-loop (SiL), and virtual ECU stages, while preserving production-level timing behavior and the semantics of the AUTOSAR runtime. The experimental results show that the vECU accurately reproduces the PWM outputs, Hall sensor pulse timing, and limit–stop decisions of physical ECU, and that integration issues previously discovered only in HiL tests can be exposed much earlier. Overall, the workflow shortens verification cycles, improves the observability of timing-dependent behavior, and provides a practical basis for early validation in software-defined vehicle development. Full article

This paper presents a miniaturized, polarization-insensitive frequency-selective metasurface (FSMS) with stopband behavior for RF shielding applications. The FSMS is designed to suppress communication at 10 GHz frequency in the X-band. The design comprises a circular metallic patch with a staircase slot engraved in the center. The FSMS achieves an attenuation of 38.5 dB at the resonant frequency with a 10 dB suppression fractional bandwidth of more than 46%. The physical geometry of the unit cell makes it polarization-independent, and the angle of incidence has no effect on the stopband. The FSMS cell has overall dimensions of 0.3λ_o × 0.3λ_o × 0.05λ_o, where λ_o is free-space wavelength at the resonant frequency. Moreover, an equivalent circuit model (ECM) of the FSMS filter is developed to analyze its operation principle. An FSMS prototype is fabricated and tested for its performance, and the simulated and measured results show good agreement, making it suitable for selective electromagnetic interference (EMI) shielding applications. Full article

Wellbore instability in shale formations represents a worldwide challenge in drilling engineering. The development of high-performance shale stabilizers is crucial for enhancing wellbore stability. A core–shell structured shale stabilizer, designated AAD-CaCO₃, was synthesized via inverse emulsion polymerization using acrylamide (AM), 2-acrylamido-2-methylpropanesulfonic acid (AMPS), and dimethyl diallyl ammonium chloride (DMDAAC) as monomers. Nano-CaCO₃ was generated in situ by reacting calcium chloride and sodium carbonate. Sodium bisulfite and ammonium persulfate were used as initiators. The product was characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and thermogravimetric analysis (TGA). Its effects on the rheological properties and filtration performance of a bentonite-based mud were evaluated. The stabilizer’s efficacy in inhibiting shale hydration swelling and dispersion was evaluated through linear swelling tests and shale rolling dispersion experiments, while its plugging performance was examined via a filtration loss test with a nanoporous membrane and spontaneous imbibition tests. The results indicated that AAD-CaCO₃ possesses a core–shell structure with the nano-CaCO₃ encapsulated by the polymer. It moderately improved the rheology of the bentonite-based mud and significantly reduced both the low-temperature and low-pressure (LTLP) filtration loss and the high-temperature and high-pressure (HTHP) filtration loss. AAD-CaCO₃ could be adsorbed onto shale surfaces through electrostatic attraction, resulting in substantially reduced clay hydration swelling and an increased shale cutting recovery rate. Effective plugging of micro-nanopores in shale was achieved, demonstrating a dual mechanism of chemical inhibition and physical plugging. Full article

Noise remains a persistent limitation in coherent imaging systems, degrading image quality and hindering accurate interpretation in critical applications such as remote sensing, medical imaging, and non-destructive testing. This paper presents a physics-informed deep learning framework for effective image denoising under complex noise conditions. The proposed approach integrates nonlinear partial differential equations (PDEs), including the heat equation, diffusion models, MPMC, and the Zhichang Guo (ZG) method, into advanced neural network architectures such as ResUNet, UNet, U2Net, and Res2UNet. By embedding physical constraints directly into the training process, the framework couples data-driven learning with physics-based priors to enhance noise suppression and preserve structural details. Experimental evaluations across multiple datasets demonstrate that the proposed method consistently outperforms conventional denoising techniques, achieving higher PSNR, SSIM, ENL, and CNR values. These results confirm the effectiveness of combining physics-informed neural networks with deep architectures and highlight their potential for advanced image restoration in real-world, high-noise imaging scenarios. Full article

Carbon capture and storage (CCS) is essential for achieving net-zero emissions, yet amine-based capture systems face significant challenges including high energy penalties (20–30% of power plant output) and operational costs ($50–120/tonne CO${}_2$). This study develops and validates a novel multi-scale Digital Twin (DT) framework integrating Physics-Informed Neural Networks (PINNs) to address these challenges through real-time optimization. The framework combines molecular dynamics, process simulation, computational fluid dynamics, and deep learning to enable real-time predictive control. A key innovation is the sequential training algorithm with domain decomposition, specifically designed to handle the nonlinear transport equations governing CO${}_2$ absorption with enhanced convergence properties.The algorithm achieves prediction errors below 1% for key process variables (R${}^2$> 0.98) when validated against CFD simulations across 500 test cases. Experimental validation against pilot-scale absorber data (12 m packing, 30 wt% MEA) confirms good agreement with measured profiles, including temperature (RMSE = 1.2 K), CO${}_2$ loading (RMSE = 0.015 mol/mol), and capture efficiency (RMSE = 0.6%). The trained surrogate enables computational speedups of up to four orders of magnitude, supporting real-time inference with response times below 100 ms suitable for closed-loop control. Under the conditions studied, the framework demonstrates reboiler duty reductions of 18.5% and operational cost reductions of approximately 31%. Sensitivity analysis identifies liquid-to-gas ratio and MEA concentration as the most influential parameters, with mechanistic explanations linking these to mass transfer enhancement and reaction kinetics. Techno-economic assessment indicates favorable investment metrics, though results depend on site-specific factors. The framework architecture is designed for extensibility to alternative solvent systems, with future work planned for industrial-scale validation and uncertainty quantification through Bayesian approaches. Full article

The growing demand for sustainable materials and the valorization of waste streams have intensified research on wastewater biorefineries and bioplastics. Within this framework, this study aims to develop and characterize poly (lactic acid) (PLA)-based films partially substituted with microalgae biomass derived from wastewater treatment at different concentrations (PLA-MA: 0, 10, 20, 30, 40, and 50%). The films were produced and systematically characterized in terms of their morphological (SEM), structural (FTIR), physical (thickness, weight, swelling, and solubility), thermal (TGA), mechanical (tensile strength, elongation at break, and Young’s modulus), optical (colorimetry and UV–Vis), barrier (water vapor permeability), and biodegradability properties. FTIR analysis confirmed the successful incorporation of microalgae biomass into the polymeric matrix and indicated good compatibility at low biomass loadings, whereas higher concentrations (>20%) introduced hydrophilic functional groups associated with increasing structural incompatibility. Partial substitution of PLA with microalgae biomass significantly modulated the physical, mechanical, and optical properties of the resulting composites. Notably, biodegradability assays revealed that the PLA-MA 50% composite achieved 89% degradation within 120 days, demonstrating that microalgal biomass markedly accelerates material decomposition. Furthermore, antimicrobial tests conducted for PLA-MA 0%, 20%, and 50% confirmed the safety of wastewater-derived microalgae for incorporation into the polymer matrix. Overall, these results highlight the potential of wastewater-derived microalgae biomass as a promising and sustainable component for short-life-cycle bioplastic applications, particularly in the agricultural sector. Full article

Quantum neural networks leverage quantum computing to address machine learning problems beyond the capabilities of classical computing. In this study, we demonstrate a quantum neural network that learns the nonlinear exclusive OR function on a desktop quantum computer. The exclusive OR task is a nonlinear benchmark that cannot be solved by a single-layer perceptron, making it an excellent test for quantum machine learning. We trained a variational quantum circuit model in a simulation using the PennyLane framework to learn the two-bit exclusive OR mapping. After obtaining the circuit parameters in the simulation, the trained quantum neural network was deployed on a two-qubit Nuclear Magnetic Resonance-based desktop quantum computer operating at room temperature to evaluate the actual hardware performance. The experimental quantum state fidelity reached approximately 98.85%$\left(R_y\right)$ and 99.35%$\left(R_x\right)$, and the overall average purity was 95.16%$\left(R_y\right)$ and 97.43%$\left(R_x\right)$, indicating excellent agreement between the expected and measured results. These positive outcomes underscore the feasibility of quantum machine learning on small-scale quantum hardware, marking a minimal yet physically meaningful benchmark. Full article

This study aims to investigate the feasibility and performance effects of using waste chestnut shells (CNS), derived from agricultural biomass, as a filler replacement material in hot mix asphalt mixtures. The influence of CNS on the mechanical behavior of hot mix asphalt mixtures was evaluated through a comprehensive experimental program. Initially, the physical and conventional properties of the B50/70 asphalt binder, aggregates, and CNS material were characterized to establish a reference framework for mixture design. The optimum asphalt content (OAC) for the control mixture was established using the Marshall mix design procedure. Mixture specimens incorporating CNS were produced by introducing the material at four different proportions, corresponding to filler substitution levels ranging from 5% to 20% by weight. The prepared specimens were evaluated through a series of mechanical and durability-related tests, including Marshall stability and flow, Retained Marshall, moisture damage, dynamic creep stiffness, indirect tensile strength (ITS), fatigue performance, and indirect tensile stiffness modulus (ITSM). The results indicated that mixtures with 10% CNS replacement exhibited notable improvements in stability, water sensitivity, ITS, ITSM, dynamic creep, and fatigue resistance, suggesting that CNS has the potential to enhance the performance characteristics of hot mix asphalt pavements. Full article

Offshore wind-farm performance remains constrained by persistent wake deficits and turbulence that compound across intra-farm, intra-cluster, and inter-cluster scales, particularly under atmospheric neutral–stable stratification. A concept is advanced whereby offshore wind-farm yield may be augmented by pairing conventional horizontal-axis wind turbines (HAWTs) with lighter-than-air parafoil systems that entrain higher-momentum air and re-energise wakes, complementing yaw/induction-based wake control and enabling higher array energy density. A concise synthesis of wake physics and associated challenges motivates opportunities for active momentum re-injection, while a review of kite technologies frames design choices for lift generation and spatial keeping. Stability and control, spanning static and dynamic behaviours, tether dynamics, and response to extreme meteorological conditions, are identified as key challenges. System-integration pathways are outlined, including alignment and mounting options relative to turbine rows and prevailing shear. A staged validation programme is proposed, combining high-fidelity numerical simulation with wave-tank testing of coupled mooring–tether dynamics and wind-tunnel experiments on scaled arrays. Evaluation metrics emphasise net energy gain, fatigue loading, availability, and Levelized Cost of Energy (LCOE). The paper concludes with research directions and recommendations to guide standards and investment, and with a quantitative assessment of the techno-economic significance of kite–HAWT integration at scale. Full article

Background/Objectives: Foot mobility is considered an intrinsic risk factor for lower-limb injury, yet commonly used pronated/neutral/supinated classifications rely on arbitrary cut-points. This study aimed to develop a data-driven framework for characterizing a continuous SSNDT–injury risk gradient and deriving clinically interpretable relative-risk bands that define practical injury risk zones along the sit-to-stand navicular drop test (SSNDT) continuum. Methods: Data from 137 physically active male students (274 feet) were analyzed. Intra-rater reliability of the sit-to-stand navicular drop test (SSNDT) was assessed using ICC(3,1). A quadratic mixed-effects logistic regression model was used to characterize the SSNDT–injury relationship and derive odds-ratio-based risk bands for interpretive and screening purposes. Results: SSNDT demonstrated good intra-rater reliability (ICC(3,1) = 0.82). Model comparison supported a non-linear, U-shaped association between SSNDT and injury risk, with a minimum risk value at approximately 5.5 mm. Bootstrap analysis supported a smooth continuous risk gradient. Four representative OR levels (1.2, 1.5, 1.8, and 2.0) were selected to define SSNDT-based interpretative risk bands. Injury prevalence showed an overall increasing trend across these zones, ranging from 4.2% in the Safe zone to 52.4% in the Extreme zone. Conclusions: SSNDT provides a robust, data-driven basis for quantifying foot-mobility–related injury risk along a continuous non-linear gradient and for deriving clinically interpretable relative-risk bands grounded in a validated model. The proposed framework avoids arbitrary cut-points and supports individualized risk screening. Full article