Search Results (23)

Fractures exhibit complex spatial structures and multiscale geometric characteristics, and their accurate characterization is fundamental to reservoir evaluation, fluid migration analysis, and rock mechanics. To address the limitations of single-scale local fractal methods in simultaneously capturing fracture details and global structures, as well as the dependence of supervised learning on labeled data, this study proposes an unsupervised fracture identification method integrating Complexity-Adaptive Box-Height Differential Box-Counting (CABH-DBC) with a self-organizing map (SOM). Local fractal features are extracted using fixed multiscale windows, while the box height along the gray-level dimension is adaptively refined according to the local grayscale standard deviation. The multiscale features are then fed into the SOM for clustering, with grayscale information assisting in fracture-cluster determination. Experiments on borehole image logs from ten depth intervals of the CCSD main borehole yield mean F1 and IoU values of 0.659 and 0.493, respectively. Compared with DBC-Kmeans, the proposed method improves F1 and IoU by 39.0% and 58.0%, respectively; compared with DBC-SOM, the strongest baseline in this study, the improvements are 16.6% and 24.8%. Ablation experiments further demonstrate the complementary contributions of complexity-adaptive box-height refinement, fixed multiscale fractal features, and SOM clustering. Full article

Glaucoma is a leading cause of irreversible vision loss, and its early diagnosis remains critically important yet challenging. Traditional assessment based on the cup-to-disc ratio is often insufficient at early stages, whereas the retinal vascular network can provide additional quantitative biomarkers. This study develops and validates a binary classification method for distinguishing healthy from glaucomatous fundus images by combining deep-learning-based vessel segmentation, fractal and multifractal analysis, and textural features. The public ORIGA dataset is utilized. Images are converted to grayscale using three alternative approaches, followed by Gray-Level Co-occurrence Matrix texture analysis and fractal analysis based on the differential box-counting method. Vessel segmentation is implemented via a U-Net neural network trained on a combination of public datasets, after which multifractal analysis is performed on the resulting binary masks. The extracted features are used to train and compare several machine learning models with hyperparameter optimization. The best-performing model among ONH-based features (Random Forest) achieves 75.00%; however, a logistic regression model using multifractal parameters and CDR reaches 86.17%, substantially outperforming the CDR-only baseline (66.15%). Notably, while classical fractal dimension shows only marginal differences (1–2% relative change) between groups, multifractal parameters reveal distinct changes: the multifractal spectrum width $\Delta \alpha$ increases markedly and the minimum singularity exponent ${\alpha }_{\min }$ decreases in glaucomatous eyes, indicating increased heterogeneity of the vascular network. These findings suggest that multifractal characteristics of the vascular network can serve as reliable and sensitive biomarkers for automated glaucoma screening, offering clear advantages over classical fractal analysis. Full article

This study analyzed the spatial distribution characteristics of the foot-end trajectory of a robotic leg mechanism during different gait phases. Based on this analysis, a task space partition-based dimensional parameter optimization method was proposed. To further evaluate the spatial distribution of the high-performance transmission regions after optimization, a box-counting dimension and lacunarity were introduced as supplementary characterization indices. First, according to the functional requirements of different gait phases, the task space of the mechanism is partitioned into stance, mid-swing, and swing-transition regions. A unified kinematic model and singularity criterion are then established for the planar five-bar mechanism, and mechanism performance indices for different task regions are constructed based on the Jacobian matrix to characterize the force and velocity transmission capabilities of the mechanism, as well as its singularity margin. A genetic algorithm is used to perform dimensional synthesis optimization of the mechanism parameters. Furthermore, a task space transmission performance field is introduced, and the area ratio, box-counting dimension, and lacunarity of regions with high performance are used to characterize the spatial structure of high-performance transmission regions before and after optimization. Finally, a series of theoretical calculations and physical experiments are conducted to verify that the differential characteristics of the mechanism have a significant influence on both its static and dynamic performance. The experimental results show that the optimized mechanism achieves lower normalized objective values in all task regions and outperforms the reference mechanism in load capacity, static power consumption, positioning accuracy, and trajectory consistency. The maximum static load capacity reaches 1.29 times that of the reference mechanism, while the static power consumption is reduced to approximately one half of that of the reference mechanism. Full article

Background: Diarrheal disease remains a major and persistent cause of morbidity and mortality in Rwanda, with substantial seasonal surges that strain routine services; however, transparent and operationally interpretable national forecasting has been underused for age-stratified burden. Methods: We analyzed the Rwanda Health Management Information System (HMIS) monthly diarrhea case counts (January 2015–December 2025), stratified by age group (under-five and five-and-above), and developed validated Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasts for January 2026–December 2027. Stationarity was assessed using the Augmented Dickey–Fuller test and addressed through differencing. Candidate models were selected via rolling 5-fold cross-validation: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Mean Absolute Percentage Error (MAPE) and confirmed via Ljung–Box residual diagnostics, and benchmarked against seasonal naïve, Exponential Smoothing State-Space (ETS), and Seasonal-Trend decomposition using Loess (STL) + drift reference models. Results: Rwanda recorded 6,309,098 diarrhea cases during 2015–2025, with 49.2% among under-fives; while absolute counts were higher in those aged ≥5 years, risk remained consistently higher in under-fives (91.7–229.5 per 1000) than in those ≥5 years (17.9–34.3 per 1000). Both series showed strong annual seasonality with recurrent peaks in August–November, and forecasts suggest this pattern will persist through 2026–2027. Conclusions: These findings suggest a provisional seasonal (pre-peak, peak, and post-peak) preparedness framework and age-differentiated planning signals, underscoring that burden and risk are not inter changeable across age groups. Full article

Complex biological and medical systems often exhibit irregular and self-similar structures that can be effectively analyzed using fractal and multifractal frameworks. This study aims to provide a comprehensive overview of multifractal analysis as a mathematical tool for characterizing complex biomedical patterns and improving disease diagnosis. The methods discussed include the Wavelet Transform Modulus Maxima (WTMM) and box-counting techniques, which quantify local scaling behaviors and heterogeneity within medical images. A review of recent studies demonstrates that multifractal parameters have successfully differentiated between normal and pathological tissues in diseases such as cancer, cardiac disorders, and Alzheimer’s disease. This paper also examines the integration of artificial intelligence, particularly machine learning algorithms, with multifractal features to enhance diagnostic accuracy and automate image interpretation. The results indicate that this hybrid approach improves the reliability and sensitivity of early disease detection. In conclusion, multifractal analysis, when systematically applied and combined with AI, offers a promising complementary framework for advancing precision medicine and supporting clinical decision-making. Full article

To meet the requirement of real-time bridge crack detection, this paper proposes a lightweight detection model based on YOLOv7-tiny. First, an edge-preserved image enhancement method is proposed. It effectively enhances the image contrast and preserves the structural features of crack edges. This provides a high-quality data foundation for the detection network. Second, a LWCSP module is introduced. This module integrates hybrid convolution and shuffle operations. It reduces the model’s parameter count and computation. Simultaneously, it maintains strong feature representation capability. A good balance between detection performance and efficiency is achieved. Finally, an improved SWise-IoU is proposed to optimize the bounding box regression in YOLOv7-tiny. This method dynamically evaluates sample quality. It enables differentiated gradient adjustment for samples of different qualities. This promotes sufficient learning of sample features by the model, thereby improving detection accuracy. Experimental results show that the proposed model delivers strong performance on a public bridge crack dataset. Compared to the baseline, the mAP@0.5 is 12.1 higher, and model size, parameter count, and FLOPs are reduced by 7.3%, 8.03%, and 10%, respectively. The final model size is only 11.4 MB, and mAP@0.5 is 86.1%, suitable for a real-time crack detection task. Full article

The accurate evaluation of the geometric morphology of rock and rock-like material joint surfaces was considered crucial for studying the mechanical properties of joint surfaces. A method based on grayscale surface and differential box-counting for evaluating the consistency of rock joint surface morphology was proposed in this study. The fractal dimensions of natural red sandstone joint surfaces and 3D printed restored joint surfaces were quantitatively identified. The consistency of the joint surface morphology of the samples was validated on both the image scale and the macroscopic mechanical scale using two approaches: CNN feature extraction and variable-angle shear tests. The validation results demonstrated a high degree of convergence, thereby confirming the accuracy of the proposed method. This study could provide a reference for the determination of the fractal dimension of joint surface morphology and the consistency research. Full article

This study presents a comprehensive grayscale texture analysis framework for investigating the microstructural evolution of cement-based materials during hydration. High-resolution X-ray computed tomography (X-CT) slice images were analyzed across five hydration ages (12 h, 1 d, 3 d, 7 d, and 31 d) using three complementary methods: grayscale histogram statistics, fractal dimension calculation via differential box-counting, and texture feature extraction based on the gray-level co-occurrence matrix (GLCM). The average value of the mean grayscale value of slice (Mean_{G_AVE}) shows a trend of increasing and then decreasing. Average fractal dimension values (D_{B_AVE}) decreased logarithmically from 2.48 (12 h) to 2.41 (31 d), quantifying progressive microstructural homogenization. The trend reflects pore refinement and gel network consolidation. GLCM texture parameters—including energy, entropy, contrast, and correlation—captured the directional statistical patterns and phase transitions during hydration. Energy increased with hydration time, reflecting greater spatial homogeneity and phase continuity, while entropy and contrast declined, signaling reduced structural complexity and interfacial sharpness. A quantitative evaluation of parameter performance based on intra-sample stability, inter-sample discrimination, and signal-to-noise ratio (SNR) revealed energy, entropy, and contrast as the most effective descriptors for tracking hydration-induced microstructural evolution. This work demonstrates a novel, integrative, and segmentation-free methodology for texture quantification, offering robust insights into the microstructural mechanisms of cement hydration. The findings provide a scalable basis for performance prediction, material optimization, and intelligent cementitious design. Full article

To address the inefficiency of traditional concrete crack detection methods and the heavy reliance of supervised learning on extensive labeled data, in this study, an intelligent assessment method of concrete damage based on pseudo-label semi-supervised learning and fractal geometry theory is proposed to solve two core tasks: one is binary classification of pixel-level cracks, and the other is multi-category assessment of damage state based on crack morphology. Using three-channel RGB images as input, a dual-path collaborative training framework based on U-Net encoder–decoder architecture is constructed, and a binary segmentation mask of the same size is output to achieve the accurate segmentation of cracks at the pixel level. By constructing a dual-path collaborative training framework and employing a dynamic pseudo-label refinement mechanism, the model achieves an F1-score of 0.883 using only 50% labeled data—a mere 1.3% decrease compared to the fully supervised benchmark DeepCrack (F1 = 0.896)—while reducing manual annotation costs by over 60%. Furthermore, a quantitative correlation model between crack fractal characteristics and structural damage severity is established by combining a U-Net segmentation network with the differential box-counting algorithm. The experimental results demonstrate that under a cyclic loading of 147.6–221.4 kN, the fractal dimension monotonically increases from 1.073 (moderate damage) to 1.189 (failure), with 100% accuracy in damage state identification, closely aligning with the degradation trend of macroscopic mechanical properties. In complex crack scenarios, the model attains a recall rate (Re = 0.882), surpassing U-Net by 13.9%, with significantly enhanced edge reconstruction precision. Compared with the mainstream models, this method effectively alleviates the problem of data annotation dependence through a semi-supervised strategy while maintaining high accuracy. It provides an efficient structural health monitoring solution for engineering practice, which is of great value to promote the application of intelligent detection technology in infrastructure operation and maintenance. Full article

Differential cryptanalysis is one of the fundamental cryptanalysis techniques to evaluate the security of the block cipher. In many cases, resistance to differential cryptanalysis is proven through the upper bound of the differential characteristic probability, not the differential probability. Since the attacker uses a differential rather than a differential characteristic, resistance based on a differential characteristic tends to overestimate the security level of the block cipher. Such an overestimation is notably observed in lightweight block ciphers SKINNY, Midori, and CRAFT. In this paper, we examine the gap between the differential characteristics and the differential probability of lightweight block ciphers. We present practical methods for computing differential probability using a multistage graph. Using these methods, we count the exact number of maximum differential characteristics with fixed plaintext/ciphertext difference and activity pattern. By the exact number of maximum differential characteristics, we can calculate the probability that is closer to the real differential probability. In addition, by modifying the method, we compute a more accurate differential probability by considering the characteristics of the lower probability. We find differential distinguishers of 9-round Midori64 with probability $2^{-61.58}$, 9-round SKINNY64 with $2^{-58.67}$ and 14-round CRAFT with $2^{-60.32}$. Furthermore, we find a related-tweakey differential distinguisher of 11-round SKINNY64-64 with $2^{-55.93}$ and a related-tweak differential distinguisher of 17-round CRAFT with probability $2^{-63.37}$. Finally, we explain why these gaps are notable in Midori64, SKINNY64 and CRAFT by relating the S-box differential distribution table. Full article

Substitution boxes (S-Boxes) function as essential nonlinear elements in contemporary cryptographic systems, offering robust protection against cryptanalytic attacks. This study presents a novel technique for generating compact 8-bit S-Boxes based on multiplication in the Galois Field $GF\left(2^4\right)$. The goal of this method is to create S-Boxes with low hardware implementation cost while ensuring cryptographic properties. Experimental results indicate that the suggested S-Boxes achieve a nonlinearity value of 112, matching the AES S-Box. They also maintain other cryptographic properties, such as the Bit Independence Criterion (BIC), the Strict Avalanche Criterion (SAC), Differential Approximation Probability, and Linear Approximation Probability, within acceptable security thresholds. Notably, compared to existing studies, the proposed S-Box architecture demonstrates enhanced hardware efficiency, significantly reducing resource utilization in implementations. Specifically, the implementation cost of the S-Box consists of 31 XOR gates, 32 two-input AND gates, 6 two-input OR gates, and 2 MUX21s. Moreover, this work provides a thorough assessment of the S-Box, covering cryptographic properties, side channel attacks, and implementation aspects. Furthermore, the study estimates the quantum resource requirements for implementing the S-Box, including an analysis of CNOT, Toffoli, and NOT gate counts. Full article

Volcanic rocks, as a widely distributed rock type on the earth, are mostly buried deep within basins, and their internal structures possess characteristics by irregularity and self-similarity. In the study of volcanic rocks, accurately identifying the lithology of volcanic rocks is significant for reservoir description and reservoir evaluation. The accuracy of lithology identification can improve the success rate of petroleum exploration and development as well as the safety of engineering construction. In this study, we took the electron microscope images of four types of volcanic rocks in the Liaohe Basin as the research objects and comprehensively used the differential box-counting dimension (DBC) and the gray-level co-occurrence matrix (GLCM) to identify the lithology of volcanic rocks. Obtain the images of volcanic rocks in the research area and conduct preprocessing so that the images can meet the requirements of calculations. Firstly, calculate the different box-counting dimension. Divide the grayscale image into boxes of different scales and determine the differential box-counting dimension based on the variation of grayscale values within each box. The differential box-counting dimension of basalt ranges from 1.7 to 1.75, that of trachyte ranges from 1.82 to 1.87, that of gabbro ranges from 1.76 to 1.79, and that of diabase ranges from 1.78 to 1.82. Then, the gray-level co-occurrence matrix is utilized to extract four image texture features of volcanic rock images, namely contrast, energy, entropy, and variance. The recognition of four types of volcanic rock images is achieved by combining the different box-counting dimension and the gray-level co-occurrence matrix. This method has been experimentally verified by volcanic rock image samples. It has a relatively high accuracy in identifying the lithology of volcanic rocks and can effectively distinguish four different types of volcanic rocks. Compared with single-feature recognition methods, this approach significantly improves recognition accuracy, offers reliable technical support and a data basis for volcanic rock-related geological analyses, and drives the further development of volcanic rock research. Full article

Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA’s effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector. Full article

Microtus thomasi (Rodentia: Arvicolinae), a fossorial vole endemic to the SW Balkans, uses a variety of substrates but its underground behavior remains poorly understood. This study examines the architecture and utilization of M. thomasi burrow systems in NW Peloponnese, Greece. In particular, eight burrow systems were meticulously excavated and studied, with comprehensive measurements taken of key characteristics, including length, depth, soil mounds, and surface openings. Key coordinates were recorded using a differential GPS device for detailed mapping and fractal dimension analysis using the box-counting method was employed to assess burrow system complexity. Soil samples were analyzed for particle size and chemical composition, and vegetation types at each site were identified. We did not find statistically significant correlations between size and complexity of the burrow systems and soil composition, altitude, or specific soil components. On the other hand, we did observe statistically significant differences in tunnel diameter between two burrow systems and in tunnel depth between more. Moreover, our study showed that more than one same-sex individual can occupy a single burrow system and not just an adult male-female pair, that was previously recorded, indicating the need for further study of the social behavior of this vole species. This study provides valuable insights into the underground behavior of M. thomasi by providing information on the features of its burrow systems, thus contributing to our understanding of its biology. Full article

The tunnel is one of the most important components in modern underground engineering. Due to long and narrow shape constraints, it very easily results in large-scale fire and explosion when deflagration is caused by the accidents of vehicles that transport dangerous goods in the tunnel. Previously, the studies on the damage to tunnel lining caused by high-temperature impacts in these kinds of disasters were often limited to a discussion of only one influencing factor, either fire or explosion, but they rarely considered the two factors simultaneously. In this work, the damage properties of full-size tunnel lining induced by high temperature and impact were evaluated, and the concrete samples from the whole lining arch were selected for CT scanning. The improved differential box-counting method was used for the fractal analysis of the CT images to obtain the damage-distribution properties of the tunnel lining structure under the two coupled influencing factors: the high temperature caused by fire, and the impact caused by deflagration. Full article