Search Results (414)

The presence of Active Galaxy Nuclei (AGN) can affect the morphological classification of galaxies. This work aims to determine how the contribution of AGN affects the most-used morphological parameters down to the redshift of z ∼ 2 in COSMOS-like conditions. We use a sample of >2000 local non-active galaxies, with a well-known visual morphological classification, and add an AGN as an unresolved component that contributes to the total galaxy flux with 5–75%. We moved all the galaxies to lower magnitudes (higher redshifts) to map the conditions in the COSMOS field, and we measured six morphological parameters. The greatest impact on morphology occurs when considering the combined effect of magnitude, redshift, and AGN, with spiral galaxies being the most affected. In general, all the concentration parameters change significantly if the AGN contribution is >25% and the magnitude > 23. We find that the GINI coefficient is the most stable in terms of AGN and magnitude/redshift, followed by the moment of light (M20), Conselice–Bershady (CCON), and finally the Abraham (CABR) concentration indexes. We find that, when using morphological parameters, the combination of CABR, CCON, and asymmetry is the most effective in classifying active galaxies at high-redshift, followed by a combination of CABR and GINI. Full article

The Qinghai–Xizang Plateau and its surrounding regions have experienced intense tectonic activity, resulting in complex geostress environments that cause frequent and distinctive rockburst disasters in plateau tunnel engineering. In this study, numerical simulations were conducted to investigate the distribution characteristics and patterns of tunnel rockbursts in high-altitude regions, using geostress orientation, lateral pressure coefficient, and tunnel depth as the primary independent variables. Secondary development of FLAC3D 7.00.126 was carried out using FISH language to enable the recording and visualization of tangential stress, the Russense rockburst criterion, and elastic strain energy. Based on this, the influence mechanisms of these key geostress parameters on the location, extent, and intensity of rockbursts within tunnel cross sections were analyzed. Results indicate that geostress orientation predominantly affects the location of rockbursts, with the surrounding rock in the direction of the minimum principal stress on the tunnel cross section being particularly prone to rockburst risks. The lateral pressure coefficient primarily influences the rockburst intensity and pit range within local stress concentration zones, with higher values leading to greater rockburst intensity. Notably, when structural stress is sufficiently large, rockbursts may occur even in tunnels with shallow burial depths. Tunnel depth determines the magnitude of geostress, mainly affecting the overall risk and potential extent of rockbursts within the cross section, with greater depths leading to higher rockburst intensities and a wider affected area. Full article

Various parameters and observation conditions contribute to the emergence of color. This phenomenon poses a challenge in modern visual communication systems, which are continuously being enhanced through new insights gained from research into specific psychophysical effects. One such effect is the psychophysical phenomenon of simultaneous contrast. Nearly 90 years ago, Kurt Koffka described one of the earliest illusions related to simultaneous contrast. This study examined the perception of gray tone variations in the Koffka ring against different background color combinations (red, blue, green) displayed on a computer screen. The intensity of the effect was measured using lightness difference ΔL₀₀ across light-, medium-, and dark-gray tones. The results were analyzed using descriptive statistics, while statistically significant differences were determined using the Friedman ANOVA and post hoc Wilcox tests. The strongest visual effect was observed the for dark-gray tones of the Koffka ring on blue/green and red/green backgrounds, indicating that perceptual organization and spatial parameters influence the illusion’s magnitude. The findings suggest important implications for digital media design, where understanding these effects can help avoid unintended color tone distortions caused by simultaneous contrast. Full article

This study explores the (1+1)-dimensional Klein–Fock–Gordon equation, a distinct third-order nonlinear differential equation of significant theoretical interest. The Klein–Fock–Gordon equation (KFGE) plays a pivotal role in theoretical physics, modeling high-energy particles and providing a fundamental framework for simulating relativistic wave phenomena. To find the exact solution of the proposed model, for this purpose, we utilized two effective techniques, including the sine-Gordon equation method and a new extended direct algebraic method. The novelty of these approaches lies in the form of different solutions such as hyperbolic, trigonometric, and rational functions, and their graphical representations demonstrate the different form of solitons like kink solitons, bright solitons, dark solitons, and periodic waves. To illustrate the characteristics of these solutions, we provide two-dimensional, three-dimensional, and contour plots that visualize the magnitude of the (1+1)-dimensional Klein–Fock–Gordon equation. By selecting suitable values for physical parameters, we demonstrate the diversity of soliton structures and their behaviors. The results highlighted the effectiveness and versatility of the sine-Gordon equation method and a new extended direct algebraic method, providing analytical solutions that deepen our insight into the dynamics of nonlinear models. These results contribute to the advancement of soliton theory in nonlinear optics and mathematical physics. Full article

Extraneous details in visual representations can prompt children to use well-rehearsed, yet inappropriate, strategies that can hinder mathematics learning. Prior domain knowledge can reduce the negative effects of extraneous details in instructional materials. The present study tested whether prior knowledge of fractions and instruction on measurement division (MD) could overcome children’s inappropriate counting strategies when solving fraction division problems with images of LEGO^® bricks. Fourth and fifth graders (N = 39) were randomly assigned to two instructional conditions: one that demonstrated how to solve fraction division problems using LEGO bricks that included explanations on MD concepts, and the other with the same demonstrations but without explanations. All participants then completed a task that measured whether the studs on the bricks prompted inappropriate counting when solving the problems. Almost one-third of the sample counted the studs to some degree. Greater prior knowledge of fractions concepts and knowledge of how to represent fractions with LEGO bricks were related to fewer inappropriate counting strategies, but contrary to expectations, fraction magnitude was not related. The two conditions did not differ on participants’ counting strategies. Extraneous details on LEGO bricks are related to the application of well-practiced counting strategies for children with lower domain knowledge. Full article

Compared to other multi-source image fusion tasks, visible and SAR image fusion faces a lack of training data in deep learning-based methods. Introducing structural priors to design fusion networks is a viable solution. We incorporated the feature hierarchy concept from computer vision, dividing deep features into low-, mid-, and high-level tiers. Based on the complementary modal characteristics of SAR and visible, we designed a fusion architecture that fully analyze and utilize the difference of hierarchical features. Specifically, our framework has two stages. In the cross-modal enhancement stage, a CycleGAN generator-based method for cross-modal interaction and input data enhancement is employed to generate pseudo-modal images. In the fusion stage, we have three innovations: (1) We designed feature extraction branches and fusion strategies differently for each level based on the features of different levels and the complementary modal features of SAR and visible to fully utilize cross-modal complementary features. (2) We proposed the Layered Strictly Nested Framework (LSNF), which emphasizes hierarchical differences and uses hierarchical characteristics, to reduce feature redundancy. (3) Based on visual saliency theory, we proposed a Gradient-weighted Pixel Loss (GWPL), which dynamically assigns higher weights to regions with significant gradient magnitudes, emphasizing high-frequency detail preservation during fusion. Experiments on the YYX-OPT-SAR and WHU-OPT-SAR datasets show that our method outperforms 11 state-of-the-art methods. Ablation studies confirm each component’s contribution. This framework effectively meets remote sensing applications’ high-precision image fusion needs. Full article

Background/Objectives: The early detection of retinal ganglion cell (RGC) dysfunction is critical for timely intervention in glaucoma suspects (GSs). The combined structure–function index (CSFI), which uses visual field and optical coherence tomography (OCT) data to estimate RGC counts, may be of limited utility in GSs. This study evaluates whether steady-state pattern electroretinogram (ssPERG)-derived estimates better predict early structural changes in GSs. Methods: Fifty eyes from 25 glaucoma suspects underwent ssPERG and spectral-domain OCT. Estimated RGC counts (eRGCC) were calculated using three parameters: ssPERG-Magnitude (eRGCC_Mag), ssPERG-MagnitudeD (eRGCC_MagD), and CSFI (eRGCC_CSFI). Linear regression and multivariable models were used to assess each model’s ability to predict the average retinal nerve fiber layer thickness (AvRNFLT), average ganglion cell layer–inner plexiform layer thickness (AvGCL-IPLT), and rim area. Results: eRGCC_Mag and eRGCC_MagD were significantly correlated with eRGCC_CSFI. Both PERG-derived models outperformed eRGCC_CSFI in predicting AvRNFLT and AvGCL-IPLT, with eRGCC_MagD showing the strongest association with AvGCL-IPLT. Conversely, the rim area was best predicted by eRGCC_Mag and eRGCC_CSFI. These findings support a linear relationship between ssPERG parameters and early RGC structural changes, while the logarithmic nature of visual field loss may limit eRGCC_CSFI’s predictive accuracy in GSs. Conclusions: ssPERG-derived estimates, particularly eRGCC_MagD, better predict early structural changes in GSs than eRGCC_CSFI. eRGCC_MagD’s superior performance in predicting GCL-IPLT highlights its potential utility as an early biomarker of glaucomatous damage. ssPERG-based models offer a simpler and more sensitive tool for early glaucoma risk stratification, and may provide a clinical benchmark for tracking recoverable RGC dysfunction and treatment response. Full article

Because of their potential “smart” applications, multifunctional stimuli-responsive polymers are gaining increasing scientific interest. The present work explores the possibility of developing such materials based on the hydrolytically stable N-3-dimethylamino propyl methacrylamide), DMAPMA. To this end, the properties in aqueous solution of the homopolymer PDMAPMA and copolymers P(DMAPMA-co-MMA_x) of DMAPMA with the hydrophobic monomer methyl methacrylate, MMA, were explored. Two copolymers were prepared with a molar content x = 20% and 35%, as determined by Proton Nuclear Magnetic Resonance (¹H NMR). Turbidimetry studies revealed that, in contrast to the homopolymer exhibiting a lower critical solution temperature (LCST) behavior only at pH 14 in the absence of salt, the LCST of the copolymers covers a wider pH range (pH > 8.5) and can be tuned within the whole temperature range studied (from room temperature up to ~70 °C) through the use of salt. The copolymers self-assemble in water above a critical aggregation Concentration (CAC), as determined by Nile Red probing, and form nanostructures with a size of ~15 nm (for P(DMAPMA-co-MMA₃₅)), as revealed by transmission electron microscopy (TEM) and dynamic light scattering (DLS). The combination of turbidimetry with ¹H NMR and automatic total organic carbon/total nitrogen (TOC/TN) results revealed the potential of the copolymers as visual CO₂ sensors. Finally, the alkylation of the copolymers with dodecyl groups lead to cationic amphiphilic materials with an order of magnitude lower CAC (as compared to the unmodified precursor), effectively stabilized in water as larger aggregates (~200 nm) over a wide temperature range, due to their increased ζ potential (+15 mV). Such alkylated products show promising biocidal properties against microorganisms such as Escherichia coli and Staphylococcus aureus. Full article

This study aimed to evaluate how wearable hip exoskeleton assistance affects phase-dependent gait stability in healthy adults using a novel visualization technique known as gait tube analysis. Hip exoskeletons offer significant potential to enhance human locomotion through joint torque augmentation, yet their effects on gait stability across the gait cycle remain underexplored. This study introduces gait tube analysis, a novel method for visualizing center of mass velocity trajectories in three-dimensional state space, to quantify phase-dependent gait stability under hip exoskeleton assistance. We analyzed data from ten healthy adults walking under twelve conditions (ten powered with varying torque magnitude and timing, one passive, and one unassisted), assessing variability via covariance-based ellipsoid volumes. Powered conditions, notably HighLater and HighLatest, significantly increased vertical variability (VT) during early-to-mid stance (10–50% of the gait cycle), with HighLater showing the highest mean ellipsoid volume (99,937 mm³/s³; z = 2.3). Conversely, the passive PowerOff condition exhibited the lowest variability (47,285 mm³/s³; z = –1.7) but higher metabolic cost, highlighting a stability-efficiency trade-off. VT was elevated in 11 of 12 conditions (p ≤ 0.0059), and strong correlations (r ≥ 0.65) between ellipsoid volume and total variability validated the method’s robustness. These findings reveal phase-specific stability challenges and metabolic cost variations induced by exoskeleton assistance, providing a foundation for designing adaptive controllers to balance stability and efficiency in rehabilitation and performance enhancement contexts. Full article

Our study aimed to assess the anatomical changes in the retina, including the assessment of the reduction of diabetic macular edema (DME) on optical coherence tomography (OCT) and the improvement of retinal microvascular parameters, defined by the reduction of nonperfusion areas on OCT angiography (OCTA) after intravitreal injections of 6 mg faricimab, an anti-VEGF drug used in the treatment of DME. The study included twenty-two patients aged between 61 and 74 years, each of whom received four loading doses of 6 mg faricimab at 1-month intervals, as described in the summary of product characteristics. Hemodynamic parameters were analyzed by OCT angiography before the first intravitreal injection of faricimab and one month after each subsequent injection. The following parameters were analyzed: non-perfusion area (NPA), superficial capillary plexus (SCP) and deep capillary plexus (DCP), outer retinal flow area (ORFA), choriocapillaris flow area (CCFA) and foveal avascular zone (FAZ). Despite differences in the magnitude of improvement and time to improvement from the start of treatment with intravitreal injections of 6 mg faricimab, reductions in DME and improvements in OCTA parameters resulted in increased retinal blood flow and better visual acuity. Full article

Fracture energy plays a pivotal role in ensuring the safe design of concrete structures. Currently, experimental testing remains the predominant methodology for exploring fracture energy in concrete. Nevertheless, this approach is hindered by protracted sample production cycles and test loading conditions that contribute to elevated expenses. Moreover, owing to the complex nonlinear behavior exhibited by concrete during the fracturing process, existing empirical formulas exhibit restricted precision when forecasting fracture energy. Therefore, in order to swiftly and accurately predict the fracture energy of concrete and investigate the impact of various factors on it, this study employs a deep learning algorithm to establish the correlation between parameters and fracture energy. Additionally, an interpretable deep learning prediction model for fracture energy is proposed, which is then compared with existing empirical formulas. Finally, the SHapley Additive exPlanations (SHAP) interpretability method is utilized to interpret and analyze the prediction results. The SHAP method can identify and visualize the contribution direction (positive/negative) and magnitude of the input features and reveal the relative importance of parameters at both local and global levels simultaneously. This analysis effectively explains the decision-making mechanism of the “black box” model and significantly improves the problem of insufficient interpretability that is common in traditional machine learning methods. The findings demonstrate that over 87% of the prediction results from the deep learning model in this study exhibit a relative error of less than 10% on the test set. The model effectively captures the intricate nonlinear relationship among characteristic parameters, exhibiting superior accuracy and generalization capabilities compared to empirical formulas. The SHAP values of the input parameters are visualized to assess their influence on fracture energy: initially, fracture energy increases and then decreases with increasing compressive strength, age, and coarse aggregate proportion; fracture energy increases with increasing maximum particle size of aggregate until it reaches 20 mm, after which it stabilizes; a high water–binder ratio reduces fracture energy; within the range of 400 mm, fracture energy increases with height, exhibiting a noticeable size effect; fracture energy increases with specimen width, but the size effect diminishes beyond 150 mm width; fracture energy decreases as span–height ratio increases; seam height ratio exhibits an initial increase followed by a decrease in fracture energy, with larger ratios showing a more pronounced size effect; an increase in ligament height enhances fracture energy while maintaining a significant size effect. Full article

Due to the complexity of planning and constructing underground lines, construction challenges—such as close proximity and multi-line interactions—are increasingly being recognized, along with their associated safety hazards. The visual observation of tunnel deformation and changes in the surrounding strata is difficult. In this study, laboratory model experiments were conducted using a mixture of liquid paraffin, n-tridecane, and silica gel powder, combined in specific proportions to create a transparent material that simulates natural soft rock. The new tunnel was designed to simultaneously cross over and under two existing tunnels. The impact of the new tunnel on the existing tunnels was examined, with excavation length and soil layer thickness considered as the primary influencing factors. The results indicate that excavating the new tunnel causes settlement deformation in the tunnels above and heave deformation in the tunnels below. The magnitude of deformation increases as excavation progresses but decreases with the greater thickness of the soil interlayer. For an existing tunnel, variations in the thickness of the soil interlayer not only affect its own deformation but also disturb the tunnel on the opposite side. Therefore, to ensure safer and orderly urban tunnel construction and to address the “black box” effect, it is essential to study the deformation characteristics of existing tunnels and their surrounding rock during the construction of new tunnels. Full article

Steel fiber reinforced concrete (SFRC) exhibits excellent material enhancement and toughening properties. It is widely used in applications such as airport runways, highway pavements, and bridge deck overlays. In order to predict the compressive strength of SFRC efficiently and accurately, this study proposes a deep learning-based prediction model, trained and tested on a large set of experimental data. Additionally, the SHapley Additive exPlanations (SHAP) interpretability method is employed to analyze and interpret the prediction outcomes. SHAP facilitates the identification and visualization of both positive and negative correlations among input features, along with their magnitudes and overall importance from local and global perspectives. This analysis sheds light on the decision-making logic of the “black-box” model and addresses the transparency challenges typically associated with conventional machine learning (ML) approaches. Fourteen physical parameters, including steel fiber content, length, diameter, cement dosage, coarse aggregate content, and fly ash content, are selected as input features. The SHAP values of these parameters are visualized to assess their importance, impact, and influencing patterns on compressive strength prediction. The results show that the optimized deep learning model has higher prediction accuracy and generalization ability compared to other traditional ML models. The SHAP analysis results are consistent with the experimental results, and the predictive model well reflects the complex nonlinear relationship between various characteristic parameters, which can provide a basis and reference for the engineering design of SFRC materials. Full article

Background/Objectives: Recent research has correlated an inability to integrate sensory information with several adverse clinical outcomes, including slow gait, poor balance, and falls. For this reason, a digital health iPhone app (CatchU^® v3.1.2) has been strategically designed to bring the measurement of visual–somatosensory integration into clinical settings. The purpose of this study was to determine whether CatchU could reliably capture the phenomenon of multisensory integration compared to a validated piece of laboratory apparatus (“tristimulator”). Methods: Using both the established tristimulator and CatchU, 50 participants (76.5 ± 6.2 years of age, 60% female) completed a simple reaction time test in response to visual, somatosensory, and combined visual–somatosensory stimulation. A reaction time cumulative distribution frequency (CDF) curve was calculated for each stimulus condition, and together these were used to calculate the CDF difference function (the multisensory visual–somatosensory CDF minus a magnitude-limited sum of the unisensory visual and somatosensory CDFs). From this, the magnitude of visual–somatosensory integration (VSI) was obtained. Results: CatchU captured multisensory integration in both average reaction times and the CDF difference function. It also produced a similar magnitude of VSI and showed no systematic bias compared to the laboratory stimulator. Additionally, CatchU responses were significantly less variable than responses recorded using the tristimulator. Conclusions: Despite using different forms of stimulation and different methods to record responses, these results reveal that CatchU can be used to produce the same inferences as laboratory apparatus. This confirms the ability of CatchU to reliably capture VSI. Full article

During high-speed navigation, boat propellers often become partially exposed due to elevated sailing speeds. This condition results in a unique operational scenario where propellers are only partially submerged. Conducting computational studies on the excitation of propellers under such circumstances is essential for optimizing the dynamic performance of the shafting system. A theoretical calculation method for propeller performance was developed based on the principles of fluid dynamics relevant to water entry, leading to a computational method for determining excitation forces in this specific operational condition. This method was subsequently refined through appropriate adjustments using ANSYS Fluent software to simulate the behavior of partially submerged propellers. The findings highlighted the accuracy of the proposed model in predicting the pulsation of six force components across three distinct directions: along the propeller shaft, vertical, and lateral. Specifically, for a single blade (Blade 1), the pulsation amplitude of the vertical force (Fx) constituted 82.1% of its maximum peak magnitude and equated to 57.5% of the blade’s mean thrust. Analogously, the lateral force (Fz) pulsation amplitude represented 53.3% of its maximum peak magnitude and 40.0% of the mean thrust. These findings indicate the presence of significant unsteady hydrodynamic loads. Furthermore, a visualization approach was presented to analyze blade load phasing, offering insights relevant to the arrangement of blades on partially submerged propellers. Full article