Search Results (655)

In this paper, we study direct and inverse problems for a spatial-fractional Black–Scholes equation with space-dependent volatility. For the direct problem, we provide CN-WSGD (Crank–Nicholson and the weighted and shifted Grünwald difference) scheme to solve the initial boundary value problem. The latter aims to recover the implied volatility via observable option prices. Using a linearization technique, we rigorously derive a mathematical formulation of the inverse problem in terms of a Fredholm integral equation of the first kind. Based on an integral equation, an efficient numerical reconstruction algorithm is proposed to recover the coefficient. Numerical results for both problems are provided to illustrate the validity and effectiveness of proposed methods. Full article

In the context of algorithm selection, the careful design of benchmark functions and problem instances plays a pivotal role in evaluating the performance of optimization methods. Traditional benchmark functions have been criticized for their limited resemblance to real-world problems and insufficient coverage of the problem space. Exploratory landscape analysis (ELA) offers a systematic framework for characterizing objective functions, based on quantitative landscape features. This study proposes a method for generating benchmark functions tailored to single-objective continuous optimization problems with boundary constraints using predefined ELA feature vectors to guide their construction. The process begins with the creation of random decision variables and corresponding objective values, which are iteratively adjusted using the covariance matrix adaptation evolution strategy (CMA-ES) to ensure alignment with a target ELA feature vector within a specified tolerance. Once the feature criteria are met, the resulting topological map point is used to train a neural network to produce a surrogate function that retains the desired landscape characteristics. To validate the proposed approach, functions from the well-known Black Box Optimization Benchmark (BBOB) suite are replicated, and novel functions are generated with unique ELA feature combinations not found in the original suite. The experiment results demonstrate that the synthesized landscapes closely resemble their BBOB counterparts and preserve the consistency of the algorithm rankings, thereby supporting the effectiveness of the proposed approach. Full article

Although artificial intelligence methods such as reinforcement learning (RL) show potential in optimizing the design of compressors, there are still two major challenges remaining: limited design variables and insufficient model explainability. For the initial design of compressors, this paper proposes a technical approach that incorporates deep reinforcement learning and decision tree distillation to enhance both the optimization capability and explainability. First, a pre-selection platform for the initial design scheme of the compressors is constructed based on the Deep Deterministic Policy Gradient (DDPG) algorithm. The optimization space is significantly enlarged by expanding the co-design of 25 key variables (e.g., the inlet airflow angle, the reaction, the load coefficient, etc.). Then, the initial design of six-stage axial compressors is successfully completed, with the axial efficiency increasing to 84.65% at the design speed and the surge margin extending to 10.75%. The design scheme is closer to the actual needs of engineering. Secondly, Shapley Additive Explanations (SHAP) analysis is utilized to reveal the influence of the mechanism of the key design parameters on the performance of the compressors in order to enhance the model explainability. Finally, the decision tree inspired by the black-winged kite (BKA) algorithm takes the interpretable design rules and transforms the data-driven intelligent optimization into explicit engineering experience. Through experimental validation, this method significantly improves the transparency of the design process while maintaining the high performance of the DDPG algorithm. The extracted design rules not only have clear physical meanings but also can effectively guide the initial design of the compressors, providing a new idea with both optimization capability and explainability for its intelligent design. Full article

Image binarization algorithms reduce the original color space to only two values, black and white. They are an important preprocessing step in many computer vision applications. Image binarization is typically performed using a threshold value by classifying the pixels into two categories: lower and higher than the threshold. Global thresholding uses a single threshold value for the entire image, whereas local thresholding uses different values for the different pixels. Although slower and more complex than global thresholding, local thresholding can better classify pixels in noisy areas of an image by considering not only the pixel’s value, but also its surrounding neighborhood. This study introduces a local thresholding method that uses the results of several local thresholding algorithms and other image statistics to train a decision tree ensemble. Through cross-validation, we demonstrate that the model is robust and performs well on new data. We compare the results with state-of-the-art solutions and reveal significant improvements in the average F-measure for all DIBCO datasets, obtaining an F-measure of 95.8%, whereas the previous high score was 93.1%. The proposed solution significantly outperformed the previous state-of-the-art algorithms on the DIBCO 2019 dataset, obtaining an F-measure of 95.8%, whereas the previous high score was 73.8%. 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

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

Geographic disparities threaten equitable access for children to health-promoting safe green spaces, and quality early education in the communities in which they live and grow. To address gaps in the field, we integrated the fields of developmental psychology, public health, and environmental science to examine, at the population level, associations between the environmental burden, socioeconomic vulnerability, and kindergarten readiness in a diverse urban county. Three administrative datasets were integrated through an early childhood data sharing research partnership in Miami-Dade County. The Bruner Child Raising Vulnerability Index, the five domains of the Environmental Burden module from the Environmental Justice Index, and public school kindergarten readiness scores were aggregated at the census tract level. Analysis of variance and multiple regression analyses found associations between socioeconomic vulnerability and race/ethnicity. The socioeconomic vulnerability levels were highest in census tracts with a higher percentage of Black residents, compared to all other races/ethnicities. Areas of greater social vulnerability had lower kindergarten readiness and a higher environmental burden. A higher environmental burden predicted lower kindergarten readiness scores above and beyond race/ethnicity and socioeconomic vulnerability. The findings advance our understanding of global challenges to sustainable healthy child development, such as the persistence of a disproportionate environmental burden and inequitable access to resources such as green spaces and early education programs. The present study results can inform community health improvement plans to reduce risk exposures and promote greater access to positive environmental and educational resources for all children. Full article

The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning. Full article

Image binarization is an important process in many computer-vision applications. This transforms the color space of the original image into black and white. Global thresholding is a quick and reliable way to achieve binarization, but it is inherently limited by image noise and uneven lighting. This paper introduces a global thresholding method that uses the results of classical global thresholding algorithms and other global image features to train a regression model via machine learning. We prove through nested cross-validation that the model can predict the best possible global threshold with an average F-measure of 90.86% and a confidence of 0.79%. We apply our approach to a popular computer vision problem, document image binarization, and compare popular metrics with the best possible values achievable through global thresholding and with the values obtained through the algorithms we used to train our model. Our results show a significant improvement over these classical global thresholding algorithms, achieving near-perfect scores on all the computed metrics. We also compared our results with state-of-the-art binarization algorithms and outperformed them on certain datasets. The global threshold obtained through our method closely approximates the ideal global threshold and could be used in a mixed local-global approach for better results. Full article

Image-assisted digital shade guides are increasingly popular for shade matching; however, research on their accuracy remains limited. This study aimed to compare the accuracy and reliability of color coordination in image-assisted digital shade guides constructed using calibrated images of their shade tabs captured by a mirrorless camera (Canon, Tokyo, Japan) (MC-DSG) and a smartphone camera (Samsung, Seoul, Korea) (SC-DSG), using a spectrophotometer as the reference standard. Twenty-nine VITA Linearguide 3D-Master shade tabs were photographed under controlled settings with both cameras equipped with cross-polarizing filters. Images were calibrated using Adobe Photoshop (Adobe Inc., San Jose, CA, USA). The L* (lightness), a* (red-green chromaticity), and b* (yellow-blue chromaticity) values, which represent the color attributes in the CIELAB color space, were computed at the middle third of each shade tab using Adobe Photoshop. Specifically, L* indicates the brightness of a color (ranging from black [0] to white [100]), a* denotes the position between red (+a*) and green (–a*), and b* represents the position between yellow (+b*) and blue (–b*). These values were used to quantify tooth shade and compare them to reference measurements obtained from a spectrophotometer (VITA Easyshade V, VITA Zahnfabrik, Bad Säckingen, Germany). Mean color differences (∆E₀₀) between MC-DSG and SC-DSG, relative to the spectrophotometer, were compared using a independent t-test. The ∆E₀₀ values were also evaluated against perceptibility (PT = 0.8) and acceptability (AT = 1.8) thresholds. Reliability was evaluated using intraclass correlation coefficients (ICC), and group differences were analyzed via one-way ANOVA and Bonferroni post hoc tests (α = 0.05). SC-DSG showed significantly lower ΔE₀₀ deviations than MC-DSG (p < 0.001), falling within acceptable clinical AT. The L* values from MC-DSG were significantly higher than SC-DSG (p = 0.024). All methods showed excellent reliability (ICC > 0.9). The findings support the potential of smartphone image-assisted digital shade guides for accurate and reliable tooth shade assessment. Full article

The widespread deployment of large language models (LLMs) has raised urgent demands for verifiable content attribution and misuse mitigation. Existing text watermarking techniques often struggle in black-box or sampling-based scenarios due to limitations in robustness, imperceptibility, and detection generality. These challenges are particularly critical in open-access settings, where model internals and generation logits are unavailable for attribution. To address these limitations, we propose CWS (Contrastive Watermarking with Semantic Modeling)—a novel keyless watermarking framework that integrates contrastive semantic token selection and shared embedding space alignment. CWS enables context-aware, fluent watermark embedding while supporting robust detection via a dual-branch mechanism: a lightweight z-score statistical test for public verification and a GRU-based semantic decoder for black-box adversarial robustness. Experiments on GPT-2, OPT-1.3B, and LLaMA-7B over C4 and DBpedia datasets demonstrate that CWS achieves F1 scores up to 99.9% and maintains F1 ≥ 93% under semantic rewriting, token substitution, and lossy compression (ε ≤ 0.25, δ ≤ 0.2). The GRU-based detector offers a superior speed–accuracy trade-off (0.42 s/sample) over LSTM and Transformer baselines. These results highlight CWS as a lightweight, black-box-compatible, and semantically robust watermarking method suitable for practical content attribution across LLM architectures and decoding strategies. Furthermore, CWS maintains a symmetrical architecture between embedding and detection stages via shared semantic representations, ensuring structural consistency and robustness. This semantic symmetry helps preserve detection reliability across diverse decoding strategies and adversarial conditions. Full article

Visual perception of screen elements depends on their color, font, and position in the user interface design. Objects in the central part of the screen are perceived more easily than those in the peripheral areas. However, the peripheral space is valuable for applications like advertising and promotion and should not be overlooked. Optimizing the design of elements in this area can improve user attention to peripheral visual stimuli during focused tasks. This study aims to evaluate how different combinations of text and background color affect the visibility of moving textual stimuli in the peripheral areas of the screen, while attention is focused on a central task. This study investigates how background color, combined with white or black text, affects the attention of participants. It also identifies which background color makes a specific word most noticeable in the peripheral part of the screen. We designed quizzes to present stimuli with black or white text on various background colors in the peripheral regions of the screen. The background colors tested were blue, red, yellow, green, white, and black. While saturation and brightness were kept constant, the color tone was varied. Among ten combinations of background and text color, we aimed to determine the most noticeable combination in the peripheral part of the screen. The combination of white text on a blue background resulted in the shortest detection time (1.376 s), while black text on a white background achieved the highest accuracy rate at 79%. The results offer valuable insights for improving peripheral text visibility in user interfaces across various visual communication domains such as video games, television content, and websites, where peripheral information must remain noticeable despite centrally focused user attention and complex viewing conditions. Full article

Mock modular forms, first invented by Ramanujan, provide a beautiful generalization of the usual modular forms. In recent years, it was found that they capture the generating functions of the number of microstates of BPS black holes appearing in compactifications of string theory with 8 and 16 supercharges. This review describes these results and their applications, which range from the actual computation of these generating functions for both compact and non-compact compactification manifolds (encoding, respectively, Donaldson–Thomas and Vafa–Witten topological invariants) to the construction of new non-commutative structures on moduli spaces of Calabi–Yau threefolds. Full article

The Antarctic continent hosts highly specialized microbial ecosystems, particularly within endolithic habitats, where microorganisms colonize the interior of rocks in order to withstand conditions that otherwise cannot support life. Previous studies have characterized the composition and abundance of these communities, as well as their different degrees of stress power; furthermore, the effect of different lithic substrates in shaping their associated bacterial assemblages has been extensively investigated. By contrast, how rock typology exerts fungal endolithic colonization still remains unexplored. In this study, we have considered and compared fungal communities inhabiting granite and sandstone rocks collected across Victoria Land, Antarctica, using high-throughput sequencing of the Internal Transcribed Spacer (ITS) region. Our analyses revealed that both rock types were dominated by Ascomycota, with a marked prevalence of lichen-forming fungi, particularly within the class Lecanoromycetes. However, granite-supported communities exhibited significantly higher species richness, likely driven by the structural heterogeneity of the substrate and the presence of fissures enabling chasmoendolithic colonization. In contrast, sandstone communities were more specialized and dominated by strict cryptoendolithic taxa capable of surviving within the rock’s pore spaces. Differential abundance analysis identified key species associated with each substrate, including the lichen Buellia frigida in granite and the black fungus Friedmanniomyces endolithicus in sandstone, two endemic species in Antarctica. Moreover, the use of presence/absence- versus abundance-based diversity metrics revealed contrasting ecological patterns; substrate type had a stronger influence on species presence, whereas geographic location more significantly shaped abundance profiles, highlighting the complex interplay between both factors in determining fungal community composition. Additionally, alpha diversity analyses showed significantly higher species richness in granite compared to sandstone, suggesting that structural heterogeneity and chasmoendolithism may promote a more diverse fungal assemblage. Full article

Creating grayscale images from a color reality has been an inherent human practice since ancient times, but it became a technological challenge with the advent of the first black-and-white televisions and digital image processing. Decolorization is a process that projects visual information from a three-dimensional feature space to a one-dimensional space, thus reducing the dimensionality of the image while minimizing the loss of information. To achieve this, various strategies have been developed, including the application of color channel weights and the analysis of local and global image contrast, but there is no universal solution. In this paper, we propose a bio-inspired approach that combines findings from neuroscience on the architecture of the visual system and color coding with evidence from studies in the psychology of art. The goal is to simplify the decolorization process and facilitate its control through color-related concepts that are easily understandable to humans. This new method organizes colors in a scale that links activity on the retina with a system of opponent and complementary channels, thus allowing the adjustment of the perception of warmth and coolness in the image. The results show an improvement in chromatic contrast, especially in the warmth and coolness categories, as well as an enhanced ability to preserve subtle contrasts, outperforming other approaches in the Ishihara test used in color blindness detection. In addition, the method offers a computational advantage by reducing the process through direct pixel-level operation. Full article