Search Results (1,657)

The appearance of spin-induced ferroelectric polarization in the so-called type-II multiferroic materials has received a lot of attention. The nature and mechanisms of such polarization were intensively studied using perovskite rare-earth manganites, RMnO₃, as model systems. Later, multiferroic properties were discovered in some RFeO₃ perovskites and possibly in some RCrO₃ perovskites. However, R₂NiMnO₆ double perovskites have ferromagnetic structures that do not break the inversion symmetry. It was found recently that more complex magnetic structures are realized in A-site-ordered quadruple perovskites, RMn₃Ni₂Mn₂O₁₂. Therefore, they have the potential to be multiferroics. In this work, dielectric properties in magnetic fields up to 9 T were investigated for such perovskites as RMn₃Ni₂Mn₂O₁₂ with R = Ce to Ho and for BiMn₃Ni₂Mn₂O₁₂. The samples with R = Bi, Ce, and Nd showed no dielectric anomalies at all magnetic fields, and the dielectric constant decreases with decreasing temperature. The samples with R = Sm to Ho showed qualitatively different behavior when the dielectric constant started increasing with decreasing temperature below certain temperatures close to the magnetic ordering temperatures, T_N. This difference could suggest different magnetic ground states. The samples with R = Eu, Dy, and Ho still showed no anomalies on the dielectric constant. On the other hand, peaks emerged at T_N on the dielectric constant in the R = Sm sample from about 2 T up to the maximum available field of 9 T. The Gd sample showed peaks on dielectric constant at T_N between about 1 T and 7 T. Transition temperatures increase with increasing magnetic fields for R = Sm and decrease for R = Gd. These findings suggest the presence of magnetic-field-induced multiferroic states in the R = Sm and Gd samples with intermediate ionic radii. Dielectric properties at different magnetic fields are also reported for Lu₂NiMnO₆ for comparison. Full article

Background/Objectives: Intranasal delivery is a promising approach for targeting the central nervous system (CNS); however, most of the drugs show poor permeability through the nasal mucosa. Nanocarriers such as liposomes can improve nasal drug absorption; however, the surface charge of liposomes has a key role in the nasal mucosal uptake process. Therefore, the present study aimed to formulate and compare the intranasal applicability of oppositely charged liposomes loaded with donepezil hydrochloride (DPZ) as CNS-active model compound using two different charge inducers, the negatively charged dicethyl phosphate (DCP) and the positively charged stearylamine (SA). Methods: Liposomes were prepared with a fixed phosphatidylcholine (PC)/cholesterol (CH) 7:2 molar ratio, while the effect of DCP and SA was studied in a 0.5:2 molar ratio. The most important properties for intranasal administration were studied, e.g., colloidal parameters, drug release and permeability behavior, and mucoadhesion. Results: It has been revealed that the reduction in liposome vesicle size is directly proportional to the amount of DCP, while it is inversely proportional to the amount of SA. This was also supported by the drug release studies—the lower vesicle size resulted in faster drug release. Both charge inducers increased the drug encapsulation efficiency (~60–80%) through tighter packing or increased spacing of the lipid bilayer structure. DCP also improved the in vitro nasal permeability compared to the initial DPZ solution. The positively charged SA showed more remarkable mucoadhesive properties than DCP. Conclusions: We can conclude that both charge inducers can be useful for improving nasal absorption of liposomal carriers, DCP in higher (PC:CH:DCP 7:2:2), while SA in lower concentrations (PC:CH:SA 7:2:0.5). Full article

Amidst the global energy structure transition and intensification of climate warming, the temperature control targets of the Paris Agreement and China’s “dual carbon” goals have driven the rapid development of building-integrated photovoltaics (BIPVs). However, solar cells in BIPV systems may produce exhaust gases that affect local urban air quality if exposed to extreme environmental conditions such as high temperatures during operation. In this study, eight air quality monitoring points were established around the BIPV system at Shenyang Jianzhu University as the experimental group, along with one additional air quality monitoring point serving as a control group. The concentrations of four air pollutant indicators (PM_2.5, PM₁₀, SO₂, and NO₂) were monitored continuously for 14 days. The weight of each indicator was calculated using the principle of information entropy, and the air quality evaluation grades were determined by combining the homomorphic inverse correlation function. The Entropy-Weighted Set Pair Analysis model was applied to evaluate the air quality of the BIPV system at Shenyang Jianzhu University. The results indicated that due to the high concentrations of SO₂ and NO₂, the Air Quality Index (AQI) grade at Shenyang Jianzhu University was classified as “light pollution.” Corresponding recommendations were proposed to promote the sustainable development of urban BIPV. Simultaneously, the evaluation results of the Entropy-Weighted Set Pair Analysis model were similar to those obtained using other methods, demonstrating the feasibility of this evaluation model for assessing the impact on air quality. Full article

The direct current resistivity method holds advantages such as rapid, efficient, and automatic data acquisition. It is an important geophysical exploration technology for monitoring dynamic changes in subsurface geology. However, this method has such issues as volume effect and non-uniqueness in inversion. To meet the demand for high-resolution direct current resistivity inversion of dynamic geological models characterized by discontinuous changes, this study proposed a cross-gradient constrained time-lapse inversion method, thereby enhancing inversion imaging accuracy. A cross-gradient constraint term between models was incorporated into the objective function of time-lapse inversion to constrain the structural consistency and highlight local resistivity changes. This method avoided excessively smooth imaging as often caused by over-reliance on a reference model in time-lapse inversion, thereby significantly improving both the spatial resolution and quantitative accuracy of direct current resistivity monitoring inversion images. Numerical examples confirmed that the proposed method delivers higher inversion imaging accuracy in identifying dynamic resistivity changes, evidenced by a substantially lower normalized mean-square error (MSE). Furthermore, physical model experiments and a case study confirmed the stability of this method under actual monitoring conditions. The proposed method provides a more precise and effective inversion imaging technique for refined monitoring of dynamic changes in subsurface geologic bodies. Full article

In recent years, unmanned aerial vehicle (UAV) quantitative remote sensing technology has demonstrated significant advantages in fields such as agricultural monitoring and ecological environment assessment. However, achieving the goal of quantification still faces major challenges due to the angle effect. This effect, caused by the bidirectional reflectance distribution function (BRDF) of surface targets, leads to significant spectral response variations at different observation angles, thereby affecting the inversion accuracy of physicochemical parameters, internal components, and three-dimensional structures of ground objects. This study systematically reviewed 48 relevant publications from 2000 to the present, retrieved from the Web of Science Core Collection through keyword combinations and screening criteria. The analysis revealed a significant increase in both the number of publications and citation frequency after 2017, with research spanning multiple disciplines such as remote sensing, agriculture, and environmental science. The paper comprehensively summarizes research progress on the angle effect in UAV quantitative remote sensing. Firstly, its underlying causes based on BRDF mechanisms and radiative transfer theory are explained. Secondly, multi-angle data acquisition techniques, processing methods, and their applications across various research fields are analyzed, considering the characteristics of UAV platforms and sensors. Finally, in view of the current challenges, such as insufficient fusion of multi-source data and poor model adaptability, it is proposed that in the future, methods such as deep learning algorithms and multi-platform collaborative observation need to be combined to promote theoretical innovation and engineering application in the research of the angle effect in UAV quantitative remote sensing. This paper provides a theoretical reference for improving the inversion accuracy of surface parameters and the development of UAV remote sensing technology. Full article

To mitigate thermal cracking in concrete box girders during construction, this study introduces an inversion method for thermal parameters by integrating machine learning with finite element simulation. The research aims to accurately identify key thermal parameters—thermal conductivity k, total hydration heat Q₀, convection coefficient h, and reaction coefficient m—through an efficient and reliable data-driven approach. An orthogonal experimental design was used to construct a representative sample database, and a Bayesian-optimized XGBoost (BO-XGBoost) model was developed to establish a nonlinear mapping between temperature peaks and thermal parameters. Validated against field monitoring data from a prestressed concrete continuous rigid-frame bridge, the method demonstrated high accuracy: the inversiontemperature curves closely matched measured data, with a maximum peak temperature error of only 1.40 °C (relative error 2.5%). Compared to conventional machine learning models (DT, SVR, BP and LSTM), BO-XGBoost showed superior predictive performance and convergence efficiency. The proposed approach provides a scientific basis for real-time temperature control and crack prevention in concrete box girders and is applicable to temperature field analysis in mass concrete structures. Full article

Within the field of permanent magnet synchronous motor sensorless speed control systems, we present a novel scheme with a Multi-dimensional Taylor Network (MTN)-based nonlinear observer as the core, supplemented by two auxiliary MTN modules to realize closed-loop control: (1) MTN Model Identifier: Provides real-time PMSM nonlinear dynamic feedback for the observer; (2) MTN Adaptive Inverse Controller: Compensates for load disturbances using the observer’s estimated states. The study focuses on optimizing the MTN observer to address key limitations of existing methods (high computational complexity, lack of stability guarantees, and low estimation accuracy). Compared with the neural network observer, this MTN-based scheme stands out due to its straightforward structure and significantly reduced approximately 40% computational complexity. Specifically, the intricate calculations and high resource consumption typically associated with neural network observers are circumvented. Subsequently, by leveraging Lyapunov theory, an adaptive learning rule for the MTN weights is meticulously devised, which seamlessly bridges the theoretical proof of the nonlinear observer’s stability. Simulation results demonstrate that the proposed MTN observer achieves rapid convergence of speed and position estimation errors (with steady-state errors within ±0.5% of the rated speed and ±0.02 rad for rotor position) after a transient period of less than 0.2 s. Even when stator resistance is increased by tenfold to simulate parameter variations, the observer maintains high estimation accuracy, with speed and position errors increasing by no more than 1.2% and 0.05 rad, respectively, showcasing strong robustness. These results collectively confirm the efficacy and practical value of the proposed scheme in PMSM sensorless speed control. Full article

Novel structural materials, high-entropy alloys (HEAs), have attracted considerable interest owing to their tunable microstructural designs and adjustable mechanical properties. In the present work, the microstructural evolution and tensile deformation behavior of FeCoNiCrTi_0.2 HEA are comprehensively examined through cold rolling (with 80% thickness reduction) followed by annealing, combined with multiscale characterization techniques (EBSD/TEM) and mechanical tests. The results reveal that the as-rolled microstructure was characterized by the presence of strong Brass, Goss/Brass, and S textures, along with the formation of high-density dislocation walls (DDWs) and dislocation cells (DCs). As the annealing temperature increased, recrystallized grains preferentially nucleated at grain boundaries with higher stress concentrations and dislocation densities. The grain size decreased from 120.33 μm in the as-rolled state to 10.26 μm after annealing at 1000 °C. Low-angle grain boundaries (LAGBs) progressively transformed into high-angle grain boundaries (HAGBs), while the fraction of Σ3 twin boundaries initially decreased and subsequently increased, reaching a maximum of 43.7% after annealing at 1000 °C. At annealing temperatures exceeding 800 °C, deformed grains became equiaxed, with partial retention of primary texture components observed. After annealing at 1000 °C, the yield strength and tensile strength decreased compared to the as-rolled state, while the elongation significantly increased from 17.2% to 69.8% Simultaneously, the yield ratio decreased by 53%, and the strain-hardening capacity was enhanced. Ultimately, a constitutive model integrating the influences of dislocation mean free path and twin boundary obstruction was developed, providing microscopic explanations for the inverse relationship between strength and recrystallization fraction. Full article

Ground settlement caused by urban subway construction can significantly impact surrounding buildings and underground infrastructure, posing risks to structural safety and long-term performance. Accurate prediction of settlement trends is therefore essential for ensuring building integrity and supporting informed decision-making during construction. This study proposes a hierarchical prediction framework that incorporates waveform-based curve classification and machine learning to forecast ground settlement patterns. Monitoring data from the Urumqi Metro construction project are analyzed, and settlement curve types are identified using Fréchet distance, categorized into five distinct forms: inverse cotangent, exponential, multi-step, one-shaped, and oscillating. Each type is then matched with the most suitable predictive model, including the Autoregressive Integrated Moving Average (ARIMA), Attention Mechanism-enhanced Long Short-Term Memory (AM-LSTM), Genetic Algorithm-optimized Support Vector Regression (GA-SVR), and Particle Swarm Optimization-based Backpropagation neural network (PSO-BP). Results show that AM-LSTM achieves the best performance for inverse cotangent and large-sample exponential curves; ARIMA excels for small-sample exponential curves; PSO-BP is most effective for multi-step curves; and GA-SVR offers superior accuracy for one-shaped and oscillating curves. Validation on a newly excavated section of Urumqi Metro Line 2 confirms the model’s potential in enhancing the safety management of buildings and infrastructure in subway construction zones. Full article

Cadmium exposure has been associated with shortened telomeres, alterations in DNA methylation patterns, and increased mortality. However, the role of DNA methylation in mediating the relationship between cadmium and telomere dynamics is still unclear. Additionally, it is unknow how telomere dynamics and DNA methylation alterations may affect the association between cadmium exposure and mortality outcomes. We utilized data from 8716 National Health and Nutrition Examination Survey (NHANES) participants aged 18 and above, collected between 1999 and 2002, and linked these to mortality outcomes from the National Center for Health Statistics (NCHS) through 2019. In the final model, ln-blood cadmium was significantly and inversely associated with ln-T/S ratio (β = −0.043, 95% CI: −0.059 to −0.027, p < 0.001), while ln-Horvath DNAmTL was strongly and positively associated with ln-T/S ratio (β = 1.782, 95% CI: 1.467 to 2.097, p < 0.001). Moreover, ln-blood cadmium also showed a significant inverse association with ln-Horvath DNAmTL (β = −0.010, 95% CI: −0.014 to −0.006, p < 0.001). Structural equation modeling showed that the association between cadmium and T/S ratio was mediated by Horvath DNAmTL, with a total effect of −0.044, a direct effect of −0.027, and an indirect effect of −0.017. Furthermore, stratified analyses revealed that a 1-unit increase in ln-blood cadmium was associated with higher all-cause mortality, with hazard ratios (HR) of 1.47 for participants with T/S ratio below the median and 1.41 for those above. Similar patterns were observed for cardiovascular (HR = 1.68 vs. 1.30) and cancer mortality (HR = 1.75 vs. 1.42). For Horvath DNAmTL, the association was significant only for all-cause mortality (HR = 1.36 vs. 1.31). However, no significant interactions were detected. In conclusion, our findings suggest that Horvath DNAmTL is associated with the relationship between cadmium and telomere length, suggesting a potential DNA methylation pathway that warrants further longitudinal investigation. Individuals with lower T/S ratios or Horvath DNAmTL appear to be more susceptible to cadmium-related mortality. Further research is necessary to confirm these results. Full article

Chinese fir, as a crucial fast-growing tree species in the hilly regions of southern China, exhibits spatial structure characteristics that directly influence both the ecological functionality and productivity of its stands. This study focused on Chinese fir plantations in the Yangkou State-Owned Forest Farm, Fujian Province. Using UAV-LiDAR point cloud data, individual tree parameters such as height and crown width were extracted, and a DBH inversion model was constructed by integrating machine learning algorithms. Spatial structure parameters were quantified through weighted Voronoi diagrams. A comprehensive evaluation system was established based on the combined weighting method and fuzzy evaluation model to systematically analyze spatial structure characteristics and their evolutionary patterns across different age classes. The results demonstrated that growth environment indicators (openness and openness ratio) progressively declined with the stand’s age, reflecting deteriorating light conditions due to increasing canopy closure. Growth superiority (size ratio and angle competition index) exhibited a “V”-shaped trend, with the most intense competition occurring in the middle-aged stands before stabilizing in the over-mature stage. The resource utilization efficiency (uniform angle and forest layer index) showed continuous optimization, reaching optimal spatial configuration in over-mature stands. This study developed a spatial structure evaluation system for Chinese fir plantations by combining UAV data and cloud modeling, elucidating structural characteristics and developmental patterns across different growth stages, thereby providing theoretical foundations and technical support for close-to-nature management and the precision quality improvement of Chinese fir plantations. Full article

The surface quality of optical components plays a decisive role in advanced imaging, precision manufacturing, and high-power laser systems, where even defects can induce abnormal scattering and degrade system performance. Addressing the limitations of conventional single-view inspection methods, this study presents a panoramic multi-angle scattered light field acquisition approach integrated with deep learning-based recognition. A hemispherical synchronous imaging system is designed to capture complete scattered distributions from surface defects in a single exposure, ensuring both structural consistency and angular completeness of the measured data. To enhance the interpretation of complex scattering patterns, we develop a tailored lightweight network, SFD-YOLO, which incorporates the PSimam attention module for improved salient feature extraction and the Efficient_Mamba_CSP module for robust global semantic modeling. Using a simulated dataset of multi-width scratch defects, the proposed method achieves high classification accuracy with strong generalization and computational efficiency. Compared to the baseline YOLOv11-cls, SFD-YOLO improves Top-1 accuracy from 92.5% to 95.6%, while reducing the parameter count from 1.54 M to 1.25 M and maintaining low computational cost (Flops 4.0G). These results confirm that panoramic multi-angle scattered imaging, coupled with advanced neural architectures, provides a powerful and practical framework for optical surface defect detection, offering valuable prospects for high-precision quality evaluation and intelligent defect inversion in optical inspection. Full article

This study investigates steady-state heat conduction in materials with stepwise discontinuities in thermal conductivity, a phenomenon frequently encountered in layered composites, thermal barrier coatings, and electronic packaging. The problem is formulated for a 2D two-domain region, where each subdomain has a distinct constant conductivity. Both the direct problem—determining the temperature field from known conductivities—and the inverse problem—identifying conductivities and the internal heat source from limited temperature measurements—are addressed. To this end, three deterministic finite-difference-type models are developed: two for the standard formulation and one for a meshless formulation based on Moving Least Squares (MLS), all derived within a local framework that efficiently enforces interface conditions. In addition, two Monte Carlo models are proposed—one for the standard and one for the meshless setting—providing pointwise estimates of the solution without requiring computation over the entire domain. Finally, an algorithm for solving inverse problems is introduced, enabling the reconstruction of material parameters and internal sources. The performance of the proposed approaches is assessed through 2D benchmark problems of varying geometric complexity, including both structured grids and irregular node clouds. The numerical experiments cover convergence studies, sensitivity of inverse reconstructions to measurement noise and input parameters, and evaluations of robustness across different conductivity contrasts. The results confirm that the hybrid difference-meshless Monte Carlo framework delivers accurate temperature predictions and reliable inverse identification, highlighting its potential for engineering applications in thermal design optimization, material characterization, and failure analysis. Full article

Imaging through dynamic scattering media remains a fundamental challenge because of severe information loss and the ill-posed nature of the inversion problem. Conventional methods often struggle to strike a balance between reconstruction fidelity and efficiency in evolving environments. In this study, we present DynaFlowNet, a framework that leverages conditional flow matching theory to establish a continuous, invertible mapping from speckle patterns to target images via deterministic ordinary differential equation (ODE) integration. Central to this is the novel temporal–conditional residual attention block (TCResAttnBlock), which is designed to model spatiotemporal scattering dynamics. DynaFlowNet achieves real-time performance at 134.77 frames per second (FPS), which is 117 times faster than diffusion-based models, while maintaining state-of-the-art reconstruction quality (28.46 dB peak signal-to-noise ratio (PSNR), 0.9112 structural similarity index (SSIM), and 0.8832 Pearson correlation coefficient (PCC)). In addition, the proposed framework demonstrates exceptional geometric generalization, with only a 1.05 dB PSNR degradation across unseen geometries, significantly outperforming existing methods. This study establishes a new paradigm for real-time high-fidelity imaging using dynamic scattering media, with direct implications for biomedical imaging, remote sensing, and underwater exploration. Full article

Tire contact imprint characteristics and pressure distribution directly affect their lateral mechanical characteristics under rolling conditions, which are the key influencing factors for vehicle handling stability. Based on the nonlinear finite element method, an explicit dynamic model of radial tires is established using Abaqus, and its contact process is simulated through phased load transfer and kinematic inversion. The modified mathematical model of contact pressure distribution is introduced from the geometric evolution law of contact imprint and the nonlinear characteristics of contact pressure distribution. The corrected lateral force and aligning torque and contact imprint behavior are analyzed. The results show that in the low roll-angle range, with the increase in the roll angle, the contact imprint shrinks asymmetrically, the pressure center shifts to the outer shoulder of the roll direction, and the lateral force and aligning torque show linear growth characteristics. At the critical value ±8°, the growth rate is significantly slowed down due to the stress saturation effect of the shoulder area. The research analyzes the evolution mechanism of the lateral mechanical characteristics of the contact imprint geometry and pressure distribution drive tires under roll conditions, providing theoretical support for vehicle handling stability optimization and tire structure design. Full article