Search Results (9,438)

In this paper, an Artificial Neural Network (ANN) is built to predict the performance of intumescent coatings subjected to the ISO 384 fire curve. The performance metric is called the Retention Loss Onset Time (RLOT) in the structural steel. The network receives the steel and coating thicknesses as input and provides RLOT as the performance of any intumescent coating in a fire accident with substantial accuracy. The required data for obtaining the model is provided by revisiting the recent attempts in this field, which include hybrid numerical and experimental methods. It is found that the trapped gas fraction parameter and empirical expansion ratio substantially affect the accuracy of predictive modelling. Therefore, a new, comprehensive dynamic model that numerically simulates the bubble expansion process has been developed. This novel method directly determines the expansion ratio of the thermal conductivity model. The Eurocode is then used with multi-layer models to predict the steel temperature profile for a 1 h duration ISO fire. The accuracy is improved by modelling the temperatures and thermal resistances at the centre of each divided layer. The effects of different coatings and steel thicknesses are also investigated to provide the required data. The results are verified and validated by comparing them with the recent numerical and empirical results available in the literature. Full article

The growing demand for high-fidelity, multi-parameter, distributed sensing in critical domains such as structural health monitoring, oil and gas exploration, and secure perimeter surveillance is pushing traditional optical fiber sensors (OFS) to their performance limits. Although conventional multiplexing techniques such as time-division and wavelength-division multiplexing (TDM, WDM) have been commercially successful, they are rapidly approaching fundamental bottlenecks in sensor density, spatial resolution, and data capacity. This review argues that the synergistic convergence of space-division multiplexing (SDM) and artificial intelligence (AI) represents a paradigm shift, enabling a new generation of intelligent, high-dimensional sensing networks. We comprehensively survey the state of the art in SDM-based OFS, detailing the operating principles and applications of multi-core fibers (MCFs) for ultra-dense sensor arrays and 3D shape sensing, as well as few-mode fibers (FMFs) for mode-division multiplexing and enhanced multi-parameter discrimination. However, the unprecedented spatial parallelism provided by SDM introduces significant challenges, including inter-channel crosstalk, complex signal demultiplexing, and massive data volumes. This paper systematically explores how AI, particularly machine learning (ML) and deep learning (DL), is being leveraged not merely as a tool but as an indispensable core technology to mitigate these impairments. We critically analyze AI’s role in digital crosstalk suppression, intelligent mode demultiplexing, signal denoising, and solving complex inverse problems for parameter estimation. Furthermore, we highlight how this AI–SDM synergy enables capabilities beyond the reach of either technology alone, such as super-resolution sensing and predictive analytics. The discussion is extended to include the critical supporting pillars of this ecosystem, such as advanced interrogation techniques and the associated data management challenges. Finally, we provide a forward-looking perspective on the trajectory of the field, outlining a path toward cognitive sensing networks that are self-calibrating, adaptive, and capable of autonomous decision-making. This review is intended to serve as a foundational reference for researchers and engineers at the intersection of photonics and intelligent systems, illuminating the pathway toward tomorrow’s intelligent sensing infrastructure. Full article

Oil and gas exploration conducted in the main fault zone of the Fuman Oilfield has yielded large-scale and high-production results. Against this background, the non-fault zone has emerged as a new domain for oil exploration endeavors. Nevertheless, the establishment of a unified sequence division scheme for the study area remains unachieved, primarily constrained by two key factors: first, the high costs associated with ultra-deep high-density coring operations; and second, the inconspicuous response characteristics exhibited by logging curves. This absence of a standardized scheme has further impeded the progress of oil and gas exploration in the non-main fault inter-region within the study area. Consequently, the present study is based on multi-source data, including seismic data, logging data, and field outcrop data. Magnetic susceptibility measurements from the cement plant section and natural gamma-ray logging data from the Yangjikan section were systematically analyzed to establish cyclostratigraphic frameworks. A sedimentary noise model (SNM) was employed to reconstruct Holocene sea-level fluctuations, enabling precise sequence stratigraphic subdivision within the Fuman Area. Results demonstrate that the Middle-Lower Ordovician Yijianfang–Penglaiba Formations retain robust astronomical cyclicity, validated by high-fidelity orbital forcing signals. Notably, the DYNOT (Dynamic Noise After Orbital Tuning) model effectively decouples orbital-driven sea-level oscillations from local depositional noise, offering a novel approach for sequence boundary identification. This methodology reveals a hierarchical sequence architecture comprising four third-order sequences and 11 fourth-order sequences within the Yijianfang–Penglaiba Formations. Such a framework provides critical insights into facies distribution patterns and non-fault-controlled exploration potential in the Fuman Basin. Full article

During deep and ultra-deep oil and gas drilling, downhole high-temperature and high-pressure conditions significantly affect the measurement accuracy of piezoresistive pressure sensors. To improve measurement accuracy under such extreme conditions, this study proposes an intelligent temperature compensation method based on a Modified Slime Mold Algorithm (MSMA). An experimental platform covering the full operating range of 0–175 °C and 0–170 MPa was established to acquire sensor outputs, and samples were collected at various temperature and pressure points to construct a dataset. Key parameters of the compensation model were optimized using the MSMA, enhancing the model’s fitting capability. Results indicate that, after compensation, the sensor exhibits a maximum full-scale error of 0.26% and a maximum sensitivity drift of −0.019% FS/°C, significantly reducing errors compared with traditional interpolation and polynomial fitting methods. The optimized compensation model was further deployed on an embedded hardware platform, enabling high-precision temperature compensation in an engineering context. Experimental data demonstrate that the embedded implementation maintains compensation accuracy while meeting real-time application requirements, making it suitable for downhole pressure monitoring and for output correction of other intelligent sensors operating under complex field conditions. Full article

In the process of anaerobic digestion for manure treatment, adding conductive materials is one of the most used methods to enhance methane yield. Biochar, a stable conductive material, shows significant potential in facilitating direct interspecies electron transfer in anaerobic digestion systems. However, biochar’s structure and properties are influenced by its preparation method, and the mechanisms by which structural characteristics affect methane yield and microbial community structure in fermentation systems require further investigation. This study investigates the effects of pyrolysis duration (1 h for A3O and 2 h for A3T) at 550 °C using corn straw as raw material. Through characterization analyses including SEM, FTIR, conductivity, and elemental composition, we explore the impacts on gas production efficiency and key parameters in anaerobic digestion systems. By analyzing microbial community structure and changes in methanogenic functional bacteria, we elucidate the mechanisms by which biochar materials with different pyrolysis times influence anaerobic digestion processes and microbial community composition. These findings provide theoretical foundations and support for optimizing biochar preparation techniques and their targeted applications in anaerobic digestion fields. It was found that the biochar-treated group exhibited higher methane production. Compared with the CK group without biochar, the methane production of A3O and A3T increased by 8.53% and 5.16%, respectively. While methane yield differed little between A3O and A3T, longer pyrolysis time increased the biochar’s specific surface area, promoting the system’s reaction rate and enabling faster methanogenesis. High-throughput analysis showed that biochar enriched methanogenic archaea like Methanosarcina and Methanobrevibacter while upregulating methanogenesis metabolic pathways and enhancing system metabolic potential. This study elucidates the influence of pyrolysis conditions on biochar performance and its regulatory role in anaerobic digestion, providing a basis for energy recovery from organic waste and biochar application in anaerobic fermentation. Full article

In this work, a high-performance mid-wave infrared (MWIR) photodetector (PD) utilizing an InAs/GaSb Type-II superlattice absorber and a quaternary AlGaAsSb barrier is designed and analyzed based on numerical simulations aimed at determining an optimized detector structure. Through these simulations, the composition of the AlGaAsSb barrier is carefully designed to achieve lattice matching, high conduction band offset and zero valence band offset. By optimizing the barrier thickness and doping concentration, the depletion region is effectively shifted from the narrow-bandgap absorber to the wide-bandgap barrier; additionally, at 150 K and a reversed bias of 0.05 V, the dark current density in the PD with the barrier (pBn) is reduced to 1.83 × 10⁻⁵ A/cm², about two orders of magnitude lower than that of the PD without the barrier. Furthermore, the effect of the barrier on the generation–recombination (G-R) and the trap-assisted tunneling (TAT) currents are analyzed and compared in detail, and it is found that the barrier structure is much more effective in suppressing the TAT current at low reversed bias when the electric field is low in the absorber layer. These results demonstrate the efficacy of the proposed AlGaAsSb barrier design for realizing high-operating-temperature MWIR PDs. It also provides an insight into the physical mechanism that leads to the performance enhancement of InAs/GaSb PDs. Full article

During CO₂ storage in deep saline aquifers, low-permeability lenticular shale layers alter CO₂ migration and affect dissolution trapping, but their impacts remain unclear. In this study, a two-dimensional radial numerical model coupling gas–brine two-phase flow and mass transfer is developed to simulate CO₂ plume evolution and dissolution beneath discontinuous lenticular shale layers. In the model, lenticular shale interlayers are represented as discontinuous low-permeability barriers, and their geometry is characterized by radial length and vertical thickness. The blocking effect of lenticular shale layers induces bypass flow, promotes lateral plume spreading, and prolongs contact time between CO₂ and brine, which increases dissolution during 250 to 1000 days of injection. When the permeability anisotropy ratio is 0.001, upward migration of CO₂ is suppressed and a high-concentration retention zone forms beneath the lenticular shale layer. As the radial length of the lenticular shale layers increases from 150 to 250 m, the plume expands and the bypass-flow path lengthens, which strengthens lateral CO₂ spreading and redistributes dissolved CO₂ concentration. In contrast, varying the thickness of the lenticular shale layers from 6 to 10 m has a relatively limited influence on the extent of bypass flow and the morphology of the concentration field. Full article

With the shift of oil and gas exploitation to deep seas, the 35 kV high-voltage electric slip ring in Single-Point Mooring (SPM) systems faces critical challenges of insulation failure and thermal failure, threatening operational safety. This study aims to investigate its insulation strength and temperature rise characteristics. A three-dimensional electric field model and a magnetic–thermal coupling model considering the skin effect were established using the finite element method (FEM). Simulations were conducted under four high-voltage configurations and various high-current operating conditions, followed by AC breakdown tests and high-current temperature rise experiments for validation. The results show that the maximum electric field (up to 19.53 kV/mm) concentrates at the inlet polytetrafluoroethylene (PTFE) bushing, which is the insulation weak point. The maximum temperature rise at the center ring can be predicted by a power-law model. Moreover, simulation results agree well with experimental data, confirming the reliability of the computational studies. This work provides a theoretical and experimental basis for the optimal design and safe operation of high-voltage slip rings in offshore SPM systems. Full article

Plant growth regulation is orchestrated by complex hormonal networks involving gibberellin and auxin signaling pathways. In this study, a comprehensive in silico approach was employed to comparatively evaluate the plant growth regulators (PGRs) forchlorfenuron (CPPU) and strigol (STG) against two key proteins from Arabidopsis thaliana: Gibberellin 3-beta-dioxygenase 2 (GA3Ox2), a rate-limiting enzyme in the biosynthesis of bioactive gibberellins, and the auxin signaling repressor IAA7. These targets were specifically selected because they represent critical regulatory nodes in two major hormonal pathways controlling plant growth: GA3Ox2 governs the final steps of gibberellin activation, while IAA7 modulates auxin-responsive gene expression through its interaction with Auxin Response Factors. Therefore, their combined analysis enables the evaluation of potential regulatory effects of PGRs on both gibberellin biosynthesis and auxin-mediated transcriptional control. Molecular docking analyses revealed that both ligands exhibited higher binding affinity toward GA3Ox2 than IAA7, with STG showing slightly more favorable binding energies (−7.91 kcal/mol for GA3Ox2 and −5.43 kcal/mol for IAA7) compared to CPPU (−7.18 and −4.79 kcal/mol, respectively). These results suggest a structural preference of both PGRs toward the gibberellin biosynthetic pathway. To further assess complex stability under near-physiological conditions, 100 ns molecular dynamics (MD) simulations were conducted using the CHARMM36m force field. Despite its slightly lower docking scores, CPPU demonstrated greater conformational stability, lower RMSD fluctuations, and more persistent hydrogen bonding patterns, particularly in complexes with IAA7. In contrast, STG induced more pronounced conformational rearrangements, although it promoted slightly more compact protein conformations in certain systems. Fourier-transform infrared (FTIR) spectroscopy supported the computational findings by confirming the presence of key functional groups responsible for hydrogen bonding and hydrophobic interactions. Collectively, the results indicate that although STG exhibits higher initial binding affinity, CPPU forms more dynamically stable complexes with both proteins. These findings suggest that CPPU may represent a more robust candidate for sustained modulation of auxin and gibberellin signaling pathways in plant growth regulation. Full article

During a tunnel fire, the foremost priority is the safe evacuation of passengers. Extreme temperatures and toxic combustion products can quickly lead to mass casualties, so evacuation support systems require fast forecasts of how hazardous conditions will evolve in space and time. This study investigates whether sparse sensor measurements can be used to reconstruct future tunnel-wide fire conditions on two-dimensional sections that are directly relevant to structural assessment and human exposure. To this end, we develop 2D ST-FAM, a data-driven forecasting model that maps time-resolved measurements from 75 tunnel sensors to future temperature, soot, and carbon monoxide (CO) fields derived from 108 computational fluid dynamics (CFD) fire simulations. The study is organized around three questions: whether the model can accurately reconstruct future tunnel fields from sparse measurements, whether this performance is maintained on both the vertical center plane and the horizontal breathing plane, and which physical quantities remain most challenging to predict. Results show high structural agreement with the CFD reference fields over the full 1800 s prediction horizon, with average structural similarity index (SSIM) values of 0.964 for temperature, 0.984 for CO, and 0.937 for soot. These findings indicate that sparse-sensor forecasting is feasible for tunnel-scale temperature and toxic-gas field prediction, while soot prediction remains comparatively more difficult because of its sharper spatial structures. Full article

Hydrogen plasma heating, a unique method for heating and reducing iron ore, is distinguished by its high heat, rapid reduction, and high efficiency, making it a promising technique in the metallurgy field. In this study, a non-transferred arc plasma heating system was used with Ar-H₂ as the working gas and acidic pellets as the raw material. The microstructures and elemental distributions of the slag and iron phases during the reduction process were examined using electron microscopy and energy-dispersive X-ray. The variation patterns of Fe-containing phases in the reduction products were found using X-ray diffraction and full-spectrum fitting refinement. The conversion rate of the oxidized pellets and the deoxidation conversion rate per area were estimated for various gas flow rates and reduction times. A reaction kinetics model was also used to study the reaction controlling step. The results showed that during the reduction process, with an H₂ flow rate of 4.5 L min⁻¹ and a 40 min reduction, the conversion(α) reached 99.89% and the purity of the reduced metallic iron reached 99.9%, achieving the industrial-grade 3N standard. Si and Al in the melt bath generated fayalite (Fe₂SiO₄) and hercynite (FeAl₂O₄) with Fe_xO. The deoxidation conversion rate per unit area was 1.11 g (cm² min)⁻¹. A three-dimensional diffusion-controlled model was used to describe the reduction process, and the mechanism function was 2/3(1 + α)^3/2[(1 + α)^1/3]⁻¹. The values of the reduction reaction rate constant (K) were 12.6 × 10⁻² s⁻¹ and 12.8 × 10⁻² s⁻¹ when the flow rates of H₂ gas were 3 and 4.5 L min⁻¹, respectively. The apparent activation energy was 21.9 kJ mol⁻¹. The empirical equation for the specific reduction rate was calculated as ln r = −2637.5/T − 0.407. Full article

Silver nanoparticles (AgNP), which have a wide range of applications in technical and biological fields, are produced in hundreds of tons annually and are eventually released into water, air, and soil. In this study, the effects of AgNPs on Phanerochaete chrysosporium, a white-rot fungus that plays a key role in wood waste degradation, were investigated. The AgNP were synthesized at high temperature with gallic acid under different pH conditions: near-neutral pH (~7.5), notation AgNP@GA-1, and alkaline pH (~10.5), notation AgNP@GA-2, focusing on their ability to cope with oxidative stress. The samples were characterized by fine granularity (particle diameter of 12 and 11 nm, respectively), specific plasmonic features (characteristic band at 425 and 408 nm), hydrodynamic diameter of 93 and 133 nm, respectively, and Zeta potential of −34 to −44 mV, which gave them stability over a period of three months. The fungal cultures exposed to AgNP concentrations of 40–100 µL/mL (approximately 4–11 µg/mL) presented superoxide dismutase (SOD) activity, which increased by approximately 45% at 40 µL/mL for AgNP@GA-1 after 7 days, whereas AgNP@GA-2 decreased SOD activity by up to 40% at 60 µL/mL. Both AgNP types strongly stimulated catalase (CAT) biosynthesis, with two- to three-fold increased activity on the 7th day at 100 µL/mL. CAT activity remained significantly elevated for AgNP@GA-1 on the 14th day at 60–80 µL/mL, whereas for AgNP@GA-2 it decreased by 40–60% compared with the control. Variations in malondialdehyde content indicated moderate lipid peroxidation, suggesting relatively low cytotoxic effects on fungal cells. Overall, the results demonstrate that P. chrysosporium exhibits adaptive biochemical responses to AgNP-induced oxidative stress while maintaining metabolic functionality, highlighting the potential compatibility of AgNPs with white-rot fungi involved in environmental wood waste biodegradation processes. Full article

The widespread commercialization of wireless chargers for electric vehicles generally suffers from one main problem, which is the perfect alignment between the two coils, leading to a decrease in mutual inductance, which causes a drop in magnetic coupling and even a failure to transfer power. To address this persistent problem, this work proposes a comprehensive and integrated method for optimizing the coils and control architecture for reliable and safe battery charging. To address the challenges of a complex, nonlinear design space and the need for misalignment-tolerant geometries, we employ a memetic algorithm (MA) that hybridizes Particle Swarm Optimization (PSO) for broad global exploration with Mesh Adaptive Direct Search (MADS) for precise local refinement. This combination effectively avoids poor local solutions—a limitation of standalone PSO or GA approaches reported in recent studies—while efficiently converging to coil geometries that maintain strong magnetic coupling under misalignment. After the coils have been designed, electromagnetic validation is tested using finite element analysis (FEA), which allows the magnetic field distribution to be evaluated, as well as the coupling coefficient under different scenarios of misalignment and variation in the air gap between the ground side and the vehicle side. At the same time, a comprehensive control strategy for the primary side of the system has been developed. This control method ensures power management on the primary side, enabling system interoperability for charging multiple types of vehicles, as well as reducing vehicle weight for greater range. All this is combined with an innovative pulsed current charging method, chosen for its advantages in terms of thermal stability, ensuring safe and efficient recharging that is mindful of battery health. Simulation and experimental validation demonstrate that the proposed framework maintains stable wireless power transfer and achieves over 87% DC–DC efficiency under lateral misalignments up to 100 mm, fully complying with SAE J2954 alignment tolerance requirements. Full article

Deep marine shale reservoirs are controlled by multi-factor coupling effects, and the genetic mechanism of “sweet spots” exhibits strong complexity, leading to prominent difficulties in quantitative prediction and precise evaluation of sweet spots. Aiming at the problems of an unclear lithofacies-controlled sweet spot evolution law and insufficient accuracy of multi-parameter quantitative evaluation in traditional evaluation methods, this paper takes the Wufeng Formation and Long1 member of the Longmaxi Formation in the LZ block, Southern Sichuan, as the research object. Innovatively integrating machine learning (ML), grey correlation analysis (GRA), and three-dimensiona (3D) geological modeling technologies, a refined prediction model for reservoir sweet spot evaluation indicators under lithofacies constraint conditions is established, and a multi-parameter fusion quantitative evaluation method for deep marine shale gas sweet spots with high prediction accuracy is proposed. The results demonstrate that the LightGBM-based prediction model for sweet spot evaluation indicators achieved excellent performance. Based on a total of 380 preprocessed samples divided into training and test sets in a 7:3 ratio, the coefficient of determination (R²) of the model exceeded 0.9 in both the test and validation datasets. The “sweetness index”, a comprehensive evaluation index of reservoir sweet spots constructed via GRA-based multi-factor fusion, shows a correlation coefficient of 0.91 with respect to actual gas well production, presenting a high fitting degree. The 3D sweet spot geological model reveals that Class I sweet spots are mainly developed in the 1st to 3rd sub-layers of the Long1 member, while Class II sweet spots are distributed in the 5th and 6th sub-layers, which is highly consistent with the actual development law of the gas field. This study breaks through the limitations of single evaluation methods and weak lithofacies control consideration in traditional sweet spot evaluation and forms a set of innovative technical process integrating “precision prediction—multi-factor fusion—3D characterization”. It provides a new technical approach for efficient and accurate evaluation of deep marine shale reservoir sweet spots and has important guiding significance for the efficient development of shale gas. Full article

Nitrous oxide (N₂O) is recognized as a potent greenhouse gas, and 60% of atmospheric N₂O emissions come from cropland soils. Potassium (K) is an important fertilizer for rice paddy fields. K fertilizer decreased the abundance of functional genes mediating nitrification and denitrification processes, thereby mitigating N₂O emissions. However, few studies have explored the effect of K fertilization rates on N₂O emissions and grain yields, as well as the associated soil properties and aboveground N accumulation in paddy fields under different irrigation regimes. This study aimed to propose an optimum combination of K fertilization rate and irrigation regime to increase grain yield while reducing N₂O emissions. Here, a 2-year field experiment using a split-plot design with three replicates was conducted to assess the effect of three K fertilization rates (K₀: 0 kg ha⁻¹, K₇₅: 75 kg ha⁻¹, K₁₅₀: 150 kg ha⁻¹) on N₂O emissions, grain yield, aboveground N accumulation, and soil properties, including soil redox potential (Eh), NH₄⁺, NO₃⁻, soil gene abundance of AOA, AOB, nirK, nirS, nirK/nirS, and nosZ, under continuous flooding irrigation (I_CF) and alternate wetting and drying irrigation (I_AWD). The soil physicochemical properties, the gene abundance and the aboveground N accumulation were evaluated and used to explain how irrigation and K fertilization affect grain yield and N₂O emissions. We found that I_AWD significantly increased N₂O emissions by 38% compared to I_CF, and K fertilizer significantly reduced N₂O emissions by 15% relative to K₀. The effects of I_AWD and K fertilizer on N₂O emissions can be attributed to the combined impact of soil physicochemical properties and the abundance of functional genes governing N₂O emissions. Both irrigation regimes produced equivalent grain yield and aboveground N accumulation. Shifting from I_CF to I_AWD, the increase in N₂O emissions can be mitigated by K fertilization. Moreover, K₇₅ and K₁₅₀ had similar effects in reducing N₂O emissions and yield-scaled N₂O emissions, while K₇₅ had a lower K fertilizer cost and higher K partial factor productivity. Therefore, applying K fertilizer at 75 kg ha⁻¹ under I_AWD is identified as a potentially suitable rate to secure grain yield while effectively mitigating N₂O emissions. Full article