Search Results (24)

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

Stamens are vital reproductive organs in angiosperms, essential for plant growth, reproduction, and development. The genetic regulation and molecular mechanisms underlying stamen development are, however, complex and varied among different plant species. MSL-lncRNAs, a gene specific to the Y chromosome of Populus deltoides, is predominantly expressed in male flower buds. Heterologous expression of MSL-lncRNAs in Arabidopsis thaliana resulted in an increase in both stamen and anther count, without affecting pistil development or seed set. To reveal the molecular regulatory network influenced by MSL-lncRNAs on stamen development, we conducted transcriptome sequencing of flowers from both wild-type and MSL-lncRNAs-overexpressing Arabidopsis. A total of 678 differentially expressed genes were identified between wild-type and transgenic Arabidopsis. Among these, 20 were classified as transcription factors, suggesting a role for these regulatory proteins in stamen development. GO enrichment analysis revealed that the differentially expressed genes were significantly associated with processes such as pollen formation, polysaccharide catabolic processes, and secondary metabolism. KEGG pathway analysis indicated that MSL-lncRNAs might promote stamen development by upregulating genes involved in the phenylpropanoid biosynthesis pathway. The top three upregulated genes, all featuring the DUF295 domain, were found to harbor an F-box motif at their N-termini, which is implicated in stamen development. Additionally, in transgenic Arabidopsis flowers, genes implicated in tapetum formation and anther development were also observed to be upregulated, implying a potential role for MSL-lncRNAs in modulating pollen development through the positive regulation of these genes. The findings from this study establish a theoretical framework for elucidating the genetic control exerted by MSL-lncRNAs over stamen and pollen development. 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

In the realm of smart communication systems, where the ubiquity of 5G/6G networks and IoT applications demands robust data confidentiality, the cryptographic integrity of block and stream cipher mechanisms plays a pivotal role. This paper focuses on the enhancement of cryptographic strength in these systems through an innovative approach to generating substitution boxes (S-boxes), which are integral in achieving confusion and diffusion properties in substitution–permutation networks. These properties are critical in thwarting statistical, differential, linear, and other forms of cryptanalysis, and are equally vital in pseudorandom number generation and cryptographic hashing algorithms. The paper addresses the challenge of rapidly producing random S-boxes with desired cryptographic attributes, a task notably arduous given the complexity of existing generation algorithms. We delve into the hill climbing algorithm, exploring various cost functions and their impact on computational complexity for generating S-boxes with a target nonlinearity of 104. Our contribution lies in proposing a new cost function that markedly reduces the generation complexity, bringing down the iteration count to under 50,000 for achieving the desired S-box. This advancement is particularly significant in the context of smart communication environments, where the balance between security and performance is paramount. 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

The differential box-counting (DBC) method is useful for determining the fractal dimension of grayscale images. It is simple to learn and implement and has been extensively utilized. However, this approach has several problems, such as over- or undercounting the number of boxes due to inappropriate parameter choices, limiting the calculation accuracy. Many studies have been conducted to increase the algorithm’s computational accuracy by improving the calculating parameters of the differential box-counting method. The grid size is a crucial parameter for the DBC method. Generally, there are two typical ways for selecting the grid size in relevant studies: consecutive integer and divisors of image size. However, both methods for grid size selection are problematic. The consecutive integer method cannot partition the image entirely and will result in the undercounting of boxes; the divisors of image size can partition the image completely. However, this method uses fewer grid sizes to compute fractal dimensions and has a relatively huge distance error (DE). To address the shortcomings of the above-mentioned two approaches, this research presents an improved grid size selection strategy. The improved method enhances computational accuracy by computing the discarded image edge areas in the consecutive integer method, allowing the original image information to be used as thoroughly as the divisor strategy. Based on fractional Brownian motion (FBM), Brodatz, and Aerials image sets, the accuracy of the three grid size selection techniques (consecutive integer method, divisors of image size method, and the improved algorithm) to compute the fractal dimension is then compared. The results reveal that, compared to the two prior techniques, the revised algorithm described in this study minimizes the distance error and increases the accuracy of the fractal dimension computation. Full article

Successive flashover would result in carbonized tracking on insulator surface and cause deterioration to the insulation. Thus, investigation of the tracking can be beneficial in understanding flashover characteristics during long-term operation. In this paper, DC flashover was operated on the insulator, and the image of tracking after successive discharge were captured. Improved differential box-counting method (IDBM) was applied to analyze these images based on fractal theory. Weighted item was suggested during the counting procedure for rectangle image with margin covered by cut-size box. Fractal dimension of the tracking was calculated according to the suggested method. It is claimed that the suggested method could estimate the discharge propagation property and deterioration characteristics on the insulator surface. Moreover, IDBM showed advantages in image pre-processing and deterioration property revealed compared to traditional box-counting method attributing to the consideration of color depth. This image analysis method shows universality in dealing with tracking image and could provide additional information to flashover voltage. This paper suggested a potential approach for the investigation of discharge mechanism and corresponding deterioration in future research. Full article

Understanding the urban land-cover spatial patterns is of particular significance for sustainable development planning. Due to the nonlinear characteristics related to the spatial pattern for land cover, it is essential to provide a new analysis method to analyze them across remote sensing imagery. This paper is devoted to exploring the fractals and fractal dimension properties of land-cover spatial patterns in Shenzhen city, China. Land-cover information was extracted using a supervised classification method with ArcGIS technology from cloud-free Landsat TM/ETM+/OLI imagery, covering 1988–2015. The box-counting method and the least squares regression method are combined to estimate fractal dimensions of the land-cover spatial pattern. The information entropy was used to verify our fractal dimension results. The results show the fractal dimension changes for each land cover type from 1988 to 2015: (1) the land-cover spatial form of Shenzhen city has a clear fractal structure, but fractal dimension values vary in different land cover types; (2) the fractal dimension of build-up land increases and reaches a stable value, while grassland and cultivated land decrease; The fractal structure of grassland and bare land showed a bifractals trend increasing year by year; (3) the information entropy dimension growth is approaching its maximum capacity before 2011. We integrated the information entropy index and fractal dimension to analyze the complexity in land-cover spatial evolution from space-filling, space balance, and space complexity. It can be concluded that driven by policies, the land-cover spatial form in Shenzhen experienced a process from a hierarchical spatial structure with a low evolution intensity to a higher evolution intensity with multiscale differential development. The fractal dimension has been becoming better through self-organization, and its land resources are reaching the growth limits. Full article

The design of a building façade has a significant impact on the way people respond to it physiologically and behaviourally. Few methods are available to assist an architect to understand such impacts during the design process. Thus, this paper examines the viability of using two computational methods to examine potential visual stimulus-sensation relationships in facade design. The first method, fractal analysis, is used to holistically measure the visual stimuli of a design. This paper describes both the box counting (density) and differential box counting (intensity) approaches to determining fractal dimension (D) in architecture. The second method, visual attention simulation, is used to explore pre-attentive processing and sensation in vision. Four measures—D-density (D_d), D-intensity (D_i), heat map and gaze sequence—are used to provide quantitative and qualitative indicators of the ways people read different design options. Using two façade designs as examples, the results of this application reveal that the D values of a façade image have a relationship with the pre-attentive processing shown in heat map and gaze sequence simulations. The findings are framed as a methodological contribution to the field, but also to the disciplinary knowledge gap about the stimulus-sensation relationship and visual reasoning in design. Full article

Cytokines are mediators of inflammation induced in the course of Clostridioides difficile infection (CDI). High Mobility Group Box 1 (HMGB1) is a cytokine playing an important role in the pathogenesis of numerous inflammatory and autoimmune diseases. The aim of the study was to assess the HMGB1 gene expression in the course of CDI. We have performed a prospective case-control study- including 55 adult patients, among them 27 with CDI, who were hospitalized from October 2018 to February 2020 and 28 healthy volunteers. We assessed: a complete blood count with differential leukocyte count, blood creatinine, albumin, and C-reactive protein (CRP) levels. Then, the expression of the HMGB1 gene was evaluated using quantitative Real-Time PCR. Patients with CDI were found to have a significant increase in white blood cells (WBC), neutrophil count, and CRP levels, they also exhibited decreased levels of albumin compared with controls. The HMGB1 gene expression was significantly lower among patients with CDI compared with the control group and significantly, inversely correlated with CRP level in blood. In conclusion, we have observed a decreased expression of the HMGB1 gene in peripheral blood of patients with mild or moderate CDI, which hypothetically could reflect their diminished capability to fight the pathogen. Full article