Search Results (1,586)

Chaotic systems have demonstrated significant potential in the field of image encryption due to their extreme sensitivity to initial conditions, inherent unpredictability, and pseudo-random behavior. However, existing chaos-based encryption schemes still face several limitations, including narrow chaotic regions, discontinuous chaotic ranges, uneven trajectory distributions, and fixed pixel processing sequences. These issues substantially hinder the security and efficiency of such algorithms. To address these challenges, this paper proposes a novel hyperchaotic map, termed the two-dimensional cross-coupled chaotic map (2D-CFCM), derived from a newly designed 2D cross-coupled chaotic system. The proposed 2D-CFCM exhibits enhanced randomness, greater sensitivity to initial values, a broader chaotic region, and a more uniform trajectory distribution, thereby offering stronger security guarantees for image encryption applications. Based on the 2D-CFCM, an innovative image encryption method was further developed, incorporating efficient scrambling and forward and reverse random multidirectional diffusion operations with symmetrical properties. Through simulation tests on images of varying sizes and resolutions, including color images, the results demonstrate the strong security performance of the proposed method. This method has several remarkable features, including an extremely large key space (greater than $2^{912}$), extremely high key sensitivity, nearly ideal entropy value (greater than 7.997), extremely low pixel correlation (less than 0.04), and excellent resistance to differential attacks (with the average values of NPCR and UACI being $99.6050\%$ and $33.4643\%$, respectively). Compared to existing encryption algorithms, the proposed method provides significantly enhanced security. Full article

Large soil pores critically influence water and solute transport in soils. The presence of preferential flow paths created by soil macropores can profoundly impact water quality, underscoring the necessity of accurately assessing the characteristics of these macropores. However, it remains unclear whether variations in macropore structure exist between different altitudes and positions of terraced paddy fields. The primary objective of this research was to utilize X-ray computed tomography (CT) and image analysis techniques to characterize the soil pore structure at both the inner field and ridge positions across different altitude levels (high, medium, and low altitude) within terraced paddy fields. The results indicate that there are significant differences in the distribution of large soil pores at different altitudes, with large pores concentrated in the surface layer (0–10 cm) in low-altitude areas, while in high-altitude areas, the distribution of large pores is more uniform. Additionally, as altitude increases, the porosity of large pores shows an increasing trend. The three-dimensional equivalent diameter and large pore volume are primarily characterized by large pores ranging from 1 to 2 mm and 0 to 5 mm³, respectively, with their morphology predominantly appearing spherical or ellipsoidal. The connectivity of large pores in the surface layer of paddy soil is stronger than that in the bunds. However, this connectivity gradually weakens with increasing soil depth. The findings from this study provide valuable quantitative insights into the unique characteristics of soil macropores that vary according to the altitude and position in terraced paddy fields. Moreover, this study emphasizes the necessity for future research that encompasses a broader range of soil types, altitudes, and terraced paddy locations to validate and further explore the identified relationships between altitude and macropore characteristics. Full article

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article

Cylinder-to-cylinder variation (CTCV) is a prevalent issue for natural gas (NG) premixed engines with port fuel injection (PFI), which significantly impacts the engine’s power performance, fuel economy, and reliability. Focusing on this issue, this study established a three-dimensional simulation platform based on a six-cylinder natural gas premixed engine. Quantitative analysis was conducted to discuss the differences in the main boundaries, combustion process, and engine power between cylinders. Additionally, influencing factors of CTCV were explored in terms of mixture uniformity and distribution uniformity. The results indicate that, for the NG premixed engine, many parameters vary significantly between cylinders even under the economical operating condition of 1200 rpm. For example, the difference rate in the peak cylinder pressure and peak phase between cylinder 3 and cylinder 2 can reach 23.5% and 24.3%, respectively. Through the design of simulation cases, it was found that improving the mixture uniformity had a more significant impact on CTCV than improving the distribution uniformity. For example, the relative standard deviation (RSD) of peak pressure decreased by 2.15% through mixture uniformity improvement, while it only decreased by 0.39% through distribution uniformity improvement. At a high speed of 1800 rpm, the influence of distribution uniformity on CTCV increased notably, but the influence of mixture uniformity still remained greater than that of distribution uniformity. Full article

Static analysis is conducted for functionally graded material (FGM) spherical shells under thermo-mechanical loads, based on the three-dimensional thermo-elasticity theory. The material properties, which vary with both the radial coordinate and temperature, introduce nonlinearity to the problem. To address this, a layer model is proposed, wherein the shell is discretized into numerous concentric spherical layers, each possessing uniform material properties. Within this framework, the nonlinear heat conduction equations are first solved iteratively. The resulting temperature field is then applied to the thermo-elastic equations, which are subsequently solved using a combined state space and transfer matrix method to obtain displacement and stress solutions. Comparison with existing literature results demonstrates good agreement. Finally, a parametric study is presented to investigate the effects of material temperature dependence and gradient index on the thermo-mechanical behaviors of the FGM spherical shells. Full article

In the rapidly evolving fields of materials science, catalysis, electronics, drug delivery, and environmental remediation, the development of effective substrates for molecular deposition has become increasingly crucial. Ordered mesoporous silica materials have garnered significant attention due to their unique structural properties and exceptional potential as substrates for molecular immobilization across these diverse applications. This study compares three mesoporous silica powders: MCM-41, SBA-15, and SBA-16. A multi-technique characterization approach was employed, utilizing low- and wide-angle X-ray diffraction (XRD), nitrogen physisorption, and transmission electron microscopy (TEM) to elucidate the structure–property relationships of these materials. XRD analysis confirmed the amorphous nature of silica frameworks and revealed distinct pore symmetries: a two-dimensional hexagonal (P6mm) structure for MCM-41 and SBA-15, and three-dimensional cubic (Im$\overline{3}$m) structure for SBA-16. Nitrogen sorption measurements demonstrated significant variations in textural properties, with MCM-41 exhibiting uniform cylindrical mesopores and the highest surface area, SBA-15 displaying hierarchical meso- and microporosity confirmed by NLDFT analysis, and SBA-16 showing a complex 3D interconnected cage-like structure with broad pore size distribution. TEM imaging provided direct visualization of particle morphology and internal pore architecture, enabling estimation of lattice parameters and identification of structural gradients within individual particles. The integration of these complementary techniques proved essential for comprehensive material characterization, particularly for MCM-41, where its small particle size (45–75 nm) contributed to apparent structural inconsistencies between XRD and sorption data. This integrated analytical approach provides valuable insights into the fundamental structure–property relationships governing ordered mesoporous silica materials and demonstrates the necessity of combined characterization strategies for accurate structural determination. Full article

The dimensional accuracy of the intaglio surface of complete dentures fabricated using liquid crystal display (LCD) three-dimensional (3D) printing might be influenced by the build angle and post-processing storage conditions. This study evaluated the effect of build angle and natural light exposure duration on the intaglio surface trueness of maxillary complete denture bases. Standardized denture base designs (2 mm uniform thickness) were fabricated using an LCD 3D printer (Lilivis Print; Huvitz, Seoul, Republic of Korea) at build angles of 0°, 45°, and 90° (n = 7 per group). All specimens were printed using the same photopolymer resin (Tera Harz Denture; Graphy, Seoul, Republic of Korea) and identical printing parameters, followed by ultrasonic cleaning and ultraviolet post-curing. Specimens were stored under controlled light-emitting diode lighting and exposed to natural light (400–800 lux) for 0, 14, or 30 days. The intaglio surfaces were scanned and superimposed on the original design data, following the International Organization for Standardization 12836. Quantitative assessment included root mean square deviation, mean deviation, and tolerance percentage. Statistical analyses were performed using one-way analysis of variance and paired t-tests (α = 0.05). Build angle and light exposure duration significantly affected surface trueness (p < 0.05). The 90° build angle group exhibited the highest accuracy and dimensional stability, while the 0° group showed the greatest deviations (p < 0.05). These findings underscore the importance of optimizing build orientation and storage conditions in denture 3D printing. Full article

Introduction: Breast density is a well-recognized factor in breast cancer risk assessment, with higher density linked to increased malignancy risk and reduced sensitivity of conventional mammography. Background parenchymal enhancement (BPE), observed in contrast-enhanced imaging, reflects physiological contrast uptake in non-pathologic breast tissue. While extensively characterized in breast MRI, the role of BPE in contrast-enhanced mammography (CEM) remains uncertain due to inconsistent findings regarding its correlation with breast density and cancer risk. Unlike breast density—standardized through the ACR BI-RADS lexicon—BPE lacks a uniform classification system in CEM, leading to variability in clinical interpretation and research outcomes. To address this gap, we introduce the BPE-CEM Standard Scale (BCSS), a structured four-tiered classification system specifically tailored to the two-dimensional characteristics of CEM, aiming to improve consistency and diagnostic alignment in BPE evaluation. Materials and Methods: In this retrospective single-center study, 213 patients who underwent mammography (MG), ultrasound (US), and contrast-enhanced mammography (CEM) between May 2022 and June 2023 at the “A. Perrino” Hospital in Brindisi were included. Breast density was classified according to ACR BI-RADS (categories A–D). BPE was categorized into four levels: Minimal (< 10% enhancement), Light (10–25%), Moderate (25–50%), and Marked (> 50%). Three radiologists independently assessed BPE in a subset of 50 randomly selected cases to evaluate inter-observer agreement using Cohen’s kappa. Correlations between BPE, breast density, and age were examined through regression analysis. Results: BPE was Minimal in 57% of patients, Light in 31%, Moderate in 10%, and Marked in 2%. A significant positive association was found between higher breast density (BI-RADS C–D) and increased BPE (p < 0.05), whereas lower-density breasts (A–B) were predominantly associated with minimal or light BPE. Regression analysis confirmed a modest but statistically significant association between breast density and BPE (R² = 0.144), while age showed no significant effect. Inter-observer agreement for BPE categorization using the BCSS was excellent (κ = 0.85; 95% CI: 0.78–0.92), supporting its reproducibility. Conclusions: Our findings indicate that breast density is a key determinant of BPE in CEM. The proposed BCSS offers a reproducible, four-level framework for standardized BPE assessment tailored to the imaging characteristics of CEM. By reducing variability in interpretation, the BCSS has the potential to improve diagnostic consistency and facilitate integration of BPE into personalized breast cancer risk models. Further prospective multicenter studies are needed to validate this classification and assess its clinical impact. Full article

Depending on the feedstock type and the pyrolysis conditions, biochars exhibit different physical, chemical, and structural properties, which highly influence their performance in various applications. This study presents a comprehensive characterization of biochar materials derived from the waste wood of pine (Pinus sylvestris L.) and beech (Fagus sylvatica) after low-temperature pyrolysis at 270 °C, followed by chemical activation using zinc chloride. The resulting materials were thoroughly analyzed in terms of their chemical composition (FTIR), thermal behavior (TGA/DTG), structural morphology (SEM and XRD), elemental analysis, and particle size distribution. The successful modification of raw biomass into carbon-rich structures of increased aromaticity and thermal stability was confirmed. Particle size analysis revealed that the activated carbon of Fagus sylvatica (FSAC) exhibited a monomodal distribution, indicating high homogeneity, whereas Pinus sylvestris-activated carbon showed a distinct bimodal distribution. This heterogeneity was supported by elemental analysis, revealing a higher inorganic content in pine-activated carbon, likely contributing to its dimensional instability during activation. These findings suggest that the uniform morphology of beech-activated carbon may be advantageous in filtration and adsorption applications, while pine-activated carbon’s heterogeneous structure could be beneficial for multifunctional systems requiring variable pore architectures. Overall, this study underscored the potential of chemically activated biochar from lignocellulosic residues for customized applications in environmental and material science domains. Full article

The Pennes bioheat equation is the most widely used model for describing heat transfer in living tissue during thermal exposure. It is derived from the classical Fourier law of heat conduction and assumes energy exchange between blood vessels and surrounding tissues. The literature presents various numerical methods for solving the bioheat equation, with exact solutions developed for different boundary conditions and geometries. However, analytical models based on this framework are rarely reported. This study aims to develop an analytical three-dimensional model using MATHEMATICA software, with subsequent mathematical validation performed through COMSOL simulations, to characterize heat transfer in biological tissues induced by laser irradiation under various therapeutic conditions. The objective is to refine the conventional bioheat equation by introducing three key improvements: (a) incorporating a non-Fourier framework for the Pennes equation, thereby accounting for the relaxation time in thermal response; (b) integrating Dirac functions and the telegraph equation into the bioheat model to simulate localized point heating of diseased tissue; and (c) deriving a closed-form analytical solution for the Pennes equation in both its classical (Fourier-based) and improved (non-Fourier-based) formulations. This paper investigates the nuanced relationship between the relaxation time parameter in the telegraph equation and the thermal relaxation time employed in the bioheat transfer equation. Considering all these aspects, the optimal thermal relaxation time determined for these simulations was 1.16 s, while the investigated thermal exposure time ranged from 0.01 s to 120 s. This study introduces a generalized version of the model, providing a more realistic representation of heat exchange between biological tissue and blood flow by accounting for non-uniform temperature distribution. It is important to note that a reasonable agreement was observed between the two modeling approaches: analytical (MATHEMATICA) and numerical (COMSOL) simulations. As a result, this research paves the way for advancements in laser-based medical treatments and thermal therapies, ultimately contributing to more optimized therapeutic outcomes. Full article

The retinal pigment epithelium (RPE), a monolayer of pigmented cells, is critical for visual function through its interaction with the neural retina. In healthy eyes, RPE cells exhibit a uniform hexagonal arrangement, but under stress or disease, such as age-related macular degeneration (AMD), dysmorphic traits like cell enlargement and apparent multinucleation emerge. Multinucleation has been hypothesized to result from cellular fusion, a compensatory mechanism to maintain cell-to-cell contact and barrier function, as well as conserve resources in unhealthy tissue. However, traditional two-dimensional (2D) imaging using apical border markers alone may misrepresent multinucleation due to the lack of lateral markers. We present high-resolution confocal images enabling three-dimensional (3D) visualization of apical (ZO-1) and lateral (α-catenin) markers alongside nuclei. In two RPE damage models, we find that seemingly multinucleated cells are often single cells with displaced neighboring nuclei and lateral membranes. This emphasizes the need for 3D analyses to avoid misidentifying multinucleation and underlying fusion mechanisms. Lastly, images from the NaIO₃ oxidative damage model reveal variability in RPE damage, with elongated, dysmorphic cells showing increased ZsGreen reporter protein expression driven by EMT-linked CAG promoter activity, while more regular RPE cells displayed somewhat reduced green signal more typical of epithelial phenotypes. Full article

The subject of ship structural dynamics has faced new technological obstacles due to scientific and technological advancements, and one of the main concerns in related sectors is how to effectively reduce the vibration levels of different ships. This article focuses on the application scenarios of ship floating raft isolation systems, establishing a wave propagation model for acoustic black hole (ABH) structures based on the idea of the ABH effect. Then, a transfer matrix model for serially connected ABH structures is derived, which serves as a basis for subsequent structural designs. Second, the finite element method is used to study the energy distribution and vibration characteristics of a symmetrically distributed periodic non-uniform multi-level ABH structure. Meanwhile, it examines its bandgap distribution under a one-dimensional periodic arrangement and then investigates the vibration properties of non-uniform multi-level ABH thin-plate constructions with different periods from the perspective of engineering applications. Moreover, parameter optimization studies of non-uniform multi-level ABH structures with finite periods are carried out with an emphasis on engineering applications. The first step is to use the design space to determine the range of values for the parameters that need to be optimized. The hyper Latin cubic sampling method is then employed to select samples, and the EI criterion and PSO optimization algorithm are applied to add new samples to improve the Kriging surrogate model’s accuracy. When the optimal structural parameters have been determined, they are applied to the raft rib plate to verify the isolation effect of the non-uniform multi-level ABH structure by analyzing the vibration level difference at specific raft positions before and after embedding it. Full article

In response to increasing sustainability demands, the packaging industry is shifting toward paper-based alternatives to replace polymer packaging. However, achieving complex, three-dimensional geometries comparable to plastics remains challenging due to the limited stretchability of paper. This study investigates advanced preconditioning techniques to enhance the formability of paper materials for deep-draw packaging applications. A custom-built test rig was developed at Syntegon Technology GmbH to systematically evaluate the effects of ultrasound-assisted moistening and segmented radiative heating. Under optimized conditions, 2.67 s moistening, 70.00 °C punch temperature, and 2999 W radiation power, maximum stretchability increased from 13.00% to 26.93%. The results confirm the effectiveness of ultrasound in accelerating moisture uptake and radiation heating in achieving uniform thermal distribution across the paper substrate. Although prototype constraints, such as the absence of inline conditioning and real-time measurement, limit process stability and scalability, the findings provide a strong foundation for developing industrial 3D paper forming processes that support sustainable packaging innovation. Full article

This paper presents a neural network model, PINN-AeroFlow-U, for reconstructing full-field aerodynamic quantities around three-dimensional compressor blades, including regions near the wall. This model is based on structured CFD training data and physics-informed loss functions and is proposed for direct 3D compressor flow prediction. It maps flow data from the physical domain to a uniform computational domain and employs a U-Net-based neural network capable of capturing the sharp local transitions induced by fluid acceleration near the blade leading edge, as well as learning flow features associated with internal boundaries (e.g., the wall boundary). The inputs to PINN-AeroFlow-U are the flow-field coordinate data from high-fidelity multi-geometry blade solutions, the 3D blade geometry, and the first-order metric coefficients obtained via mesh transformation. Its outputs include the pressure field, temperature field, and velocity vector field within the blade passage. To enhance physical interpretability, the network’s loss function incorporates both the Euler equations and gradient constraints. PINN-AeroFlow-U achieves prediction errors of 1.063% for the pressure field and 2.02% for the velocity field, demonstrating high accuracy. Full article