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Semi-flexible pavements (SFP) are extensively used in high-traffic zones owing to their outstanding resistance against rutting. Nonetheless, interface cracking persists as a prominent issue within SFP composites. This study establishes a finite element model of SFP using a computer vision algorithm to analyze its mechanical properties at the internal interface. Two interface components, namely the aggregate-asphalt and asphalt-grout interfaces, were developed to simulate stress distribution, crack initiation, and extension within the multiphase composite of SFP. The examination of transition zone properties within the asphalt-grout interface shed light on damage morphology and mechanical response. The results demonstrate that incorporating the interface layer significantly enhances the accuracy of force behavior analysis in simulating SFP materials. Furthermore, reinforcing the interface transition zone boosts the overall peak compressive strain strength of SFP materials in tandem with increased interface strength. Moreover, the grout joints and asphalt-grout interfaces within SFP act as vulnerable points where cracks propagate swiftly, leading to the detachment of cementitious grout from the base asphalt mixture. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Architecture & Civil Engineering is the property of Chang'an Daxue Zazhishe and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of Artificial Intelligence (AI) to detect defects such as concrete cracks in civil and transport infrastructure has the potential to make inspections less expensive, quicker, safer and more objective by reducing the need for on-site human labour. One deployment scenario involves using a drone to carry an embedded device and camera, with the device making localised predictions at the edge about the existence of defects using a trained convolutional neural network (CNN) for image classification. In this paper, we trained six CNNs, namely Resnet18, Resnet50, GoogLeNet, MobileNetV2, MobileNetV3-Small and MobileNetV3-Large, using transfer learning technology to classify images of concrete structures as containing a crack or not. To enhance the model's robustness, the original dataset, comprising 3000 images of concrete structures, was augmented using salt and pepper noise, as well as motion blur, separately. The results show that Resnet50 generally provides the highest validation accuracy (96% with the original dataset and a batch size of 16) and the highest validation F1-score (95% with the original dataset and a batch size of 16). The trained model was then deployed on an Nvidia Jetson Nano device for real-time inference, demonstrating its capability to accurately detect cracks in both laboratory and field settings. This study highlights the potential of using transfer learning on Edge AI devices for Structural Health Monitoring, providing a cost-effective and efficient solution for automated crack detection in concrete structures. [ABSTRACT FROM AUTHOR]

Purpose: This investigation sought to validate the clinical precision and practical applicability of AI-enhanced three-dimensional sonographic imaging for the identification of anterior urethral stricture. Methods: The study enrolled 63 male patients with diagnosed anterior urethral strictures alongside 10 healthy volunteers to serve as controls. The imaging protocol utilized a high-frequency 3D ultrasound system combined with a linear stepper motor, which enabled precise and rapid image acquisition. For image analysis, an advanced AI-based segmentation process using a modified U-net algorithm was implemented to perform real-time, high-resolution segmentation and three-dimensional reconstruction of the urethra. A comparative analysis was performed against the surgically measured stricture lengths. Spearman's correlation analysis was executed to assess the findings. Results: The AI model completed the entire processing sequence, encompassing recognition, segmentation, and reconstruction, within approximately 5 min. The mean intraoperative length of urethral stricture was determined to be 14.4 ± 8.4 mm. Notably, the mean lengths of the urethral strictures reconstructed by manual and AI models were 13.1 ± 7.5 mm and 13.4 ± 7.2 mm, respectively. Interestingly, no statistically significant disparity in urethral stricture length between manually reconstructed and AI-reconstructed images was observed. Spearman's correlation analysis underscored a more robust association of AI-reconstructed images with intraoperative urethral stricture length than manually reconstructed 3D images (0.870 vs. 0.820). Furthermore, AI-reconstructed images provided detailed views of the corpus spongiosum fibrosis from multiple perspectives. Conclusions: The research heralds the inception of an innovative, efficient AI-driven sonographic approach for three-dimensional visualization of urethral strictures, substantiating its viability and superiority in clinical application. [ABSTRACT FROM AUTHOR]

This research introduces an innovative algorithm for the encryption and decryption of greyscale digital imaging and communications in medicine images utilizing Laplace transforms. The proposed method presents a ground breaking approach to image encryption, effectively concealing visual information and ensuring a robust, secure, and reliable encryption process. By leveraging the inherent strengths of Laplace transform, the algorithm guarantees the complete retrieval of the original image without any loss, provided the correct decryption key is used. To thoroughly evaluate the performance of the algorithm, multiple tests were conducted, including extensive statistical analyses and assessments of encryption quality. Key performance metrics were carefully measured, including correlation coefficients and entropy values, which ranged from 7.89 to 7.99. Additionally, the algorithm's effectiveness was demonstrated through peak signal‐to‐noise ratio values, which spanned from 7.597 to 9.915, indicating the degree of similarity between the original and encrypted images. Furthermore, the number of pixels change rate values, ranging from 99.519241 to 99.609375, highlighted the algorithm's ability to produce significantly different encrypted images from the original. The unified average changing intensity values, falling between 35.72345678 and 35.78233456, further underscored the algorithm's proficiency in altering pixel intensities uniformly. Overall, this research offers a significant advancement in the field of image encryption, combining theoretical robustness with practical efficiency. [ABSTRACT FROM AUTHOR]

Native and Joper chickens are types of chickens whose meat is difficult to distinguish in terms of texture and color. The aim of this study is to develop an information system capable of detecting the type of chicken meat (native or Joper) based on image analysis using the Gray Level Co-Occurrence Matrix (GLCM) method combined with the K-Nearest Neighbour (K-NN) algorithm. In this research, 200 training data samples were used to extract color and texture features and perform calculations using five GLCM parameters (energy, entropy, homogeneity, contrast, and correlation) with four texture distribution directions: 0°, 45°, 90°, and 135°. Classification was then conducted to determine the type of chicken meat using the K-NN algorithm. The results of this study include a system capable of identifying chicken types based on meat, specifically distinguishing between Joper chicken meat and native chicken meat. The system consists of two main processes: calculating gray-level co-occurrence values and determining proximity using the K-Nearest Neighbor algorithm. Based on testing results, the system can perform detection using the GLCM and K-NN methods with an accuracy rate of 80%, as evaluated by 8 out of 10 respondents in this study.

The paper discusses the communicative techniques of reputation management of the executive power bodies in the Russian media space. Comparing the concepts of digital reputation and digital image, the authors argue that, unlike image which is purposefully implemented in the media environment, reputation reflects the internet users’ real opinion about the activities performed by state institutions. The essential features of digital reputation are accumulative character, unpredictability and inertia. The authors define the discursive factors affecting reputation: ambiguously wide audience of the internet, distribution of target groups on different web sites, the unpredictability of users’ assessments, some citizens’ aggressive speech behaviour, etc. More than 2.5 thousand posts by the Ministry of Science and Higher Education of the Russian Federation published in the VKontakte social network and Telegram messenger were selected as the material for the research. The impact of content on the internet audience was evaluated with the ERpost rate at the Popsters service. The authors argue that effective techniques of digital reputation management are the following: communication on a particular internet platform with regard to its target audience, attractive hashtags, the tone of involvement with the interlocutor, rejection of formal style clichés, and some others. The discursive factors of new formal communication are established, and the techniques of managing governmental bodies’ positive digital reputation are described.

This paper continues a series in which we study deficiencies in previously published works concerning fixed point assertions for digital images.

Abstract Digital image steganography serves as a technology facilitating covert communication through digital images by subtly incorporating secret data into a cover image. This practice poses a potential threat, as criminals exploit steganography to transmit illicit content, thereby jeopardizing information security. Consequently, it becomes imperative to implement defensive strategies against steganographic techniques. This paper proposes a novel defense mechanism termed “image vaccine” to safeguard digital images from steganography. The process of “vaccinating” an image renders it immune to steganographic manipulation. Notably, when criminals attempt to embed secret data into vaccinated images, the presence of such hidden information can be detected with a 100% probability, ensuring the consistent identification of stego images. This proactive approach enables the interception of stego image transmission, thereby neutralizing covert communication channels.

With the widespread use of lithium‐ion batteries in electric vehicles, energy storage, and mobile terminals, there is an urgent need to develop cathode materials with specific properties. However, existing material control synthesis routes based on repetitive experiments are often costly and inefficient, which is unsuitable for the broader application of novel materials. The development of machine learning and its combination with materials design offers a potential pathway for optimizing materials. Here, we present a design synthesis paradigm for developing high energy Ni‐rich cathodes with thermal/kinetic simulation and propose a coupled image‐morphology machine learning model. The paradigm can accurately predict the reaction conditions required for synthesizing cathode precursors with specific morphologies, helping to shorten the experimental duration and costs. After the model‐guided design synthesis, cathode materials with different morphological characteristics can be obtained, and the best shows a high discharge capacity of 206 mAh g−1 at 0.1C and 83% capacity retention after 200 cycles. This work provides guidance for designing cathode materials for lithium‐ion batteries, which may point the way to a fast and cost‐effective direction for controlling the morphology of all types of particles. [ABSTRACT FROM AUTHOR]

Background: EDTA-induced pseudothrombocytopenia (PTCP) during whole blood collection requires significant laboratory resources to obtain accurate results. We evaluated platelet-deaggregation function in EDTA-induced PTCP and platelet-clump flagging by the BC-6800Plus hematology analyzer using integrated digital image analysis. Methods: We prospectively collected 132 whole blood samples suspected of platelet clumping (102 in EDTA and 30 in sodium citrate) from 88 individuals. We compared platelet counts determined using the platelet count by impedance (PLT-I) function of the DxH 900 hematology analyzer and the PLT-I or optical platelet count (PLT-O) function of the BC6800Plus. Platelet clumping was verified through manual inspection and the MC-80 digital image analyzer. Results: Among the 132 whole blood samples, 43 EDTA samples showed platelet clumping. The DxH 900 PLT-I and BC-6800Plus PLT-I results demonstrated a strong correlation (r=0.711) for the EDTA samples but only a moderate correlation with the BC-6800Plus PLT-O results (r=0.506 and 0.545, respectively). The BC-6800Plus PLT-O results were consistent with the sodium citrate platelet counts, with a median dissociation rate of 102.5% (range, 74.9%–123.1%). The DxH 900 and BC-6800Plus analyzers had sensitivity values of 0.79 and 0.72, respectively, for platelet-clump flagging. When integrating the MC-80 digital image analysis results, the sensitivity of BC-6800Plus improved to 0.89 (standard mode) or 1.0 (PLT-Pro mode). Conclusions: BC-6800Plus PLT-O measurement results are close to the actual values obtained by platelet deaggregation with PTCP samples. Integrating the BC-6800Plus with a digital imaging analyzer effectively improved the diagnosis of PTCP and reduced the requirement for additional laboratory procedures. [ABSTRACT FROM AUTHOR]

The initial step in fabric pilling rating is to segment and localize the pilling region, which is a crucial and challenging task. This paper presents a fabric puckering image segmentation method that is integrated into a U-Net network with a dual-attention mechanism. We have enhanced the fully convolutional neural network (U-Net) model by incorporating the dual-attention mechanism. This modification has resulted in a powerful feature extraction capability, enabling the objective and accurate segmentation of the fabric puckering region. We refer to this improved model as the dual-attention U-Net. The network model for fabric pilling feature extraction adopts the improved VGG16 model architecture as its encoding part. The model parameters are initialized with VGG16 pre-training weights to accelerate convergence speed. Second, the feature fusion between the corresponding layers of the encoding part and the decoding part was fed into the dual-attention mechanism module to strengthen the weight values of the fabric pilling region adaptively, which made the model more focused on the fabric pilling target region; Third, the dual-attention U-Net model was trained by data augmentation and migration learning strategies to prevent overfitting; Finally, the performance of the model was evaluated with the collected fabric pilling data set. The results of the experiments indicate that the claimed dual-attention U-Net model is superior to the typical U-Net model in terms of accuracy and precision. The dual-attention U-Net model achieved 99.03% accuracy for fabric pilling segmentation. [ABSTRACT FROM AUTHOR]

We define a second (higher) homotopy group for digital images. Namely, we construct a functor from digital images to abelian groups, which closely resembles the ordinary second homotopy group from algebraic topology. We illustrate that our approach can be effective by computing this (digital) second homotopy group for a digital 2-sphere. [ABSTRACT FROM AUTHOR]

The current study describes the development of simple, low-cost, and high-throughput digital image colorimetric methods to determine the total iron concentration in river sediment using the spot-test reactions of iron with 1,10-phenanthroline and thiocyanate. The colorimetric assay was done on 96-microzone plates, and a flatbed scanner was applied to acquire the images. The proposed methods offered a linear range from 0.2 to 14.0 mg/L, with a detection limit of 0.11 mg/kg for the 1,10-phenanthroline method, and, for the thiocyanate method, the linear range comprises 2.0–10.0 mg/L, with a detection limit of 0.28 mg/kg. It was observed that both proposed digital image colorimetric methods (1,10-phenanthroline and thiocyanate) yielded statistically similar results to the reference procedures at a 95% confidence level. A standard reference material (NIST 8704) also was utilized for accuracy assessment and the results were statistically equivalent to the certified values within the 95% confidence level. The digital image colorimetric methods can be an alternative method for iron determination in sediment samples, allowing fast sample screening at a low cost. [Display omitted] [ABSTRACT FROM AUTHOR]

Digital image steganography serves as a technology facilitating covert communication through digital images by subtly incorporating secret data into a cover image. This practice poses a potential threat, as criminals exploit steganography to transmit illicit content, thereby jeopardizing information security. Consequently, it becomes imperative to implement defensive strategies against steganographic techniques. This paper proposes a novel defense mechanism termed "image vaccine" to safeguard digital images from steganography. The process of "vaccinating" an image renders it immune to steganographic manipulation. Notably, when criminals attempt to embed secret data into vaccinated images, the presence of such hidden information can be detected with a 100% probability, ensuring the consistent identification of stego images. This proactive approach enables the interception of stego image transmission, thereby neutralizing covert communication channels. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The photograph of the so-called Tank Man, who in 1989 stood in the street and stopped a line of Chinese army tanks after the forcible dissolution of pro-democracy demonstrations in Beijing's Tiananmen Square, is one of the twentieth century's most famous images of unarmed protest. This article explores how the iconic Tank Man image performs in Lucy Kirkwood's Chimerica (2013) and Lauren Yee's The Great Leap (2018). By expanding performance studies scholarship that frames his actions as theatrical, this article adds Meredith Conti's notion of the "crisis actor" to the discourse around Tank Man: an activist who responds to moments of political urgency by performing resistance for contemporary audiences seeking authenticity in the noisy digital landscape. To track Kirkwood and Yee's dramatization of the Tank Man icon, I analyse how their stage directions locate Tank Man in theatrical worlds that merge history and fiction, past and present, live and mediated. Arguing that Kirkwood and Yee make Tank Man legible on a human scale through domestic drama, this article traces how each characterizes their fictional Tank Man as a husband, father, and reluctant political actor compelled to radical action by the domestic tragedy of a dead wife and a lost or absent child. This allows the playwrights to humanize and reanimate the historical protest performance of Tank Man as they use the figure's iconicity to bolster their characters' subjectivity as crisis actors. In Chimerica and The Great Leap, Tank Man as icon and crisis actor offers a hopeful model of utopian politics for contemporary audiences, newly legible as a mediatized twenty-first-century hero. [ABSTRACT FROM AUTHOR]

This article explores the use of creative approaches in digital making as a platform for developing both creativity and digital skills in art students. It illustrates the ways in which digital media enhance students' creative approaches, exploring their impact on their artistic development. Using specific examples of solved problems, it introduces possible ways of creative approaches to working with the medium of digital image. The article offers insights into the potential benefits and ways of creative experimentation with the medium of the digital image in art education. [ABSTRACT FROM AUTHOR]

Abstract This research introduces an innovative algorithm for the encryption and decryption of greyscale digital imaging and communications in medicine images utilizing Laplace transforms. The proposed method presents a ground breaking approach to image encryption, effectively concealing visual information and ensuring a robust, secure, and reliable encryption process. By leveraging the inherent strengths of Laplace transform, the algorithm guarantees the complete retrieval of the original image without any loss, provided the correct decryption key is used. To thoroughly evaluate the performance of the algorithm, multiple tests were conducted, including extensive statistical analyses and assessments of encryption quality. Key performance metrics were carefully measured, including correlation coefficients and entropy values, which ranged from 7.89 to 7.99. Additionally, the algorithm's effectiveness was demonstrated through peak signal‐to‐noise ratio values, which spanned from 7.597 to 9.915, indicating the degree of similarity between the original and encrypted images. Furthermore, the number of pixels change rate values, ranging from 99.519241 to 99.609375, highlighted the algorithm's ability to produce significantly different encrypted images from the original. The unified average changing intensity values, falling between 35.72345678 and 35.78233456, further underscored the algorithm's proficiency in altering pixel intensities uniformly. Overall, this research offers a significant advancement in the field of image encryption, combining theoretical robustness with practical efficiency.

Digital security become increasingly important particularly in medical field as impact of patient privacy and the protection of patient data. This attempt for this research will be made to use elliptic curve cryptography to hide messages in the form of digital images using multiplication matrix modified hill chipper and count entropy and time encryption and decryption. The encryption process, which utilizes matrix multiplication, ensures that the images achieve near-ideal entropy values, close to 8, indicating a high degree of randomness and security. The result is entropy for encrypted image near 8 it means that randomness of image is quite random. Meanwhile for computational time encrypted and decrypted image for one block is around 400000 nano second for encrypt image and 1500000000 nano second for decrypt image.

This research presents a novel method for measuring ethanol concentrations using a smartphone. The method involves an oxidation reaction with potassium dichromate and concentrated sulfuric acid, resulting in a green-blue color formation. The color intensity, corresponding to ethanol concentrations ranging from 0 to 100%, was captured using a smartphone camera within a specialized photography box. The images were then analyzed using a specific application, converting the color signal into an absorbance value. The calibration curve demonstrated excellent linearity in the range of 0-0.55 v/v % and its detection limit is 0.01 v/v%, with a correlation coefficient exceeding 0.995. The method was successfully applied to measure ethanol in real samples, including ordinary rose water and a bitter wheat drink. • The method is inexpensive. • The method is rapid. • The method is green.

Abstract Particle geometric is a key parameter that defines the eometric attributes of calcareous sand particles and is intricately related to their mechanical traits, such as compression and shear. The scanning electron microscopy and digital imaging were applied to capture the microscopic properties and geometric projections of calcareous sand. The qualitative analysis, conventional statistical methods and fractal theory were employed to describe the geometric morphology of sand particles. Additionally, we analyzed the structural and physical traits of calcareous sand based on its unique biological genesis. We developed a hypothetical structural-physical model for calcareous sand. Our findings revealed the interwoven reticulation on the surface of calcareous gravel particles, along with an uneven distribution of pores on the external surface. As the particle size increased, the global profile factor decreased and the angularity increased. The critical threshold for the variations in flatness, surface roughness, and circularity was observed at a particle size of 5 mm, with the particle size having a relatively minor effect on these characteristics for particles smaller than 5 mm. The shape of the calcareous sand particles exhibited fractal characteristics, with fractal dimension serving as a measure of surface smoothness, particle breakage, and strength. These experimental results could significantly enhance our understanding of the mechanical behavior of calcareous sand.

The Internet of Things (IoT), which connects and automates numerous systems and gadgets, has completely changed how we live and work. One such application of IoT technology is in fire detection systems, which can help prevent and mitigate the devastating effects of fires on different types of facilities. The research presents a n IoT architecture for a fire detection system using small, low-cost cameras to collect surveillance feeds from large buildings. The data is uploaded to the cloud, where a Machine Learning algorithm detects fires in digital images. The proposed architecture consists of cameras, cloud, and clients, using an inexpensive camera for surveillance feeds and a convolutional neural network for image classification based on large datasets. However, the architecture's cloud component processes surveillance feeds and runs a Machine Learning (ML) model, utilizing computing resources for real-time data processing and continuous training for improved accuracy. Clients can subscribe to the data from the cloud and receive alerts in real-time when the ML model detects a fire in the surveillance feeds. There are significant benefits in comparing the proposed design to conventional fire detection systems. First and foremost, it is economical since the cameras used are compact, affordable, and simple to install around the building without the need for elaborate wiring or infrastructure. Secondly, it is scalable, as the cloud provides the necessary computing resources and storage capacity to handle large amounts of data, making it possible to monitor large structures with many cameras.

Abstract In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm’s operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis.

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable. [ABSTRACT FROM AUTHOR]

Particle geometric is a key parameter that defines the eometric attributes of calcareous sand particles and is intricately related to their mechanical traits, such as compression and shear. The scanning electron microscopy and digital imaging were applied to capture the microscopic properties and geometric projections of calcareous sand. The qualitative analysis, conventional statistical methods and fractal theory were employed to describe the geometric morphology of sand particles. Additionally, we analyzed the structural and physical traits of calcareous sand based on its unique biological genesis. We developed a hypothetical structural-physical model for calcareous sand. Our findings revealed the interwoven reticulation on the surface of calcareous gravel particles, along with an uneven distribution of pores on the external surface. As the particle size increased, the global profile factor decreased and the angularity increased. The critical threshold for the variations in flatness, surface roughness, and circularity was observed at a particle size of 5 mm, with the particle size having a relatively minor effect on these characteristics for particles smaller than 5 mm. The shape of the calcareous sand particles exhibited fractal characteristics, with fractal dimension serving as a measure of surface smoothness, particle breakage, and strength. These experimental results could significantly enhance our understanding of the mechanical behavior of calcareous sand. [ABSTRACT FROM AUTHOR]

Pavement cracking is a primary cause of early damage in asphalt pavements, and fiber-reinforcement technology is an effective method for enhancing the anti-cracking performance of pavement mixtures. However, due to the multi-scale dispersed structure of pavement mixtures, it is challenging to address cracking and damage with a single fiber type or fibers of the same scale. To investigate the toughening mechanisms and damage behavior of hybrid-fiber-modified mixtures, we analyzed the fracture process and damage behavior of these mixtures using a combination of basalt fiber and calcium sulfate whisker hybrid fiber modification, along with semicircular bending tests. Additionally, digital imaging was employed to examine the fracture interface characteristics, revealing the toughening mechanisms at play. The results demonstrated that basalt fibers effectively broaden the toughness range of the modified mixture at the same temperature, reduce mixture stiffness, increase residual load at the same displacement, and improve crack resistance in the mixture matrix. While calcium sulfate whiskers enhanced the peak load of the mixture, their high stiffness modulus was found to be detrimental to the mixture's crack toughness. The fracture interface analysis indicated that the three-dimensionally distributed fibers form a spatial network within the mixture, restricting the relative movement of cement and aggregate, delaying crack propagation, and significantly improving the overall crack resistance of the mixture. [ABSTRACT FROM AUTHOR]

Accurate estimation of crop biomass is essential for assessing crop growth, yield potential, and optimizing agricultural management practices. Digital image analysis has emerged as a promising tool for non-destructive and efficient biomass prediction in crop production. In this study examine the predictive capabilities of digital image analysis for faba bean biomass estimation. Utilizing RGB (Red, Green, Blue) and vegetation indices image analysis techniques, the digital images was analyses of faba bean plant in fields to extract relevant biomass characteristics and quantify biomass. Through computational modelling and simulation, it assess the accuracy and reliability of these models across 100 days of growth and environmental conditions. The test analysis were conducted in the laboratory of the Agricultural Engineering Department. The results showed varying with the green biomass with the color indicators used, through which the green mass can be predicted. A linear equation appears relationship between normalized difference index and mass production during days of faba bean growth it was y = 6.0166x + 215.85 with R² = 0.9495. [ABSTRACT FROM AUTHOR]

BACKGROUND: Coffee roasting is one of the crucial steps in obtaining a high‐quality product as it forms the product's color and flavor characteristics. Roast control is made by visual inspection or traditional instruments such as the Agtron spectrophotometer, which can have high implementation costs. Therefore, the present study evaluated colorimetric approaches (a bench colorimeter, smartphone digital images, and a colorimetric sensor) to predict the Agtron roasting degrees of whole and ground coffee. Two calibration approaches were assessed, that is, multiple linear regression and least‐squares support vector machine. For that, 70 samples of whole and ground roasted coffees comprising the Agtron roasting range were prepared. RESULTS: The results showed that all three colorimetric acquisition types were efficient for the model building, but the bench colorimeter and the smartphone digital images generally performed with good determination coefficients and low errors as measured by external validation. For the whole bean coffee, the best model presented a determination coefficient (R2) of 0.99 and a root‐mean‐squared error (RMSE) of 1.91%, while R2 of 0.99 and RMSE of 0.87% was obtained for ground coffee, both using the colorimeter. CONCLUSION: The obtained models presented good prediction capability, as assessed by external validation and randomization tests. The obtained findings point to an alternative for coffee roasting monitoring that can lead to higher digitalization and local control of the process, even for smaller producers, due to its lower costs. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A digital image colorimetry method utilizing a smartphone as the detection tool was developed for the determination of acetylcysteine and cysteine in effervescent tablets. The method is based on a simple nitroprusside test specific for free −SH groups in aminothiols, which results in orange-red color sample solutions for colorimetric detection. The analytical response of the colorimetric detection decreased as the amino acid concentration increased. The data of the digital images taken were processed to RGB (red–green–blue) color scales using the Color Detector application. Today's technology allows faster and easier analysis with the use of easily accessible and portable smartphones in colorimetric detection systems. The developed method has good linearity in a linear working range of 5.0–25 mg/L (41.27–206.34 µM) and 4.94–47.30 mg/kg (30.27–289.85 µM) for cysteine and acetylcysteine, respectively. The limit of detection and quantification values for cysteine and acetylcysteine were calculated as 1.0 and 3.4 mg/L (8.25–28.06 µM) and 1.8 and 6.1 mg/kg (11.03–37.38 µM), respectively. The applicability and accuracy of the developed method were tested on drug samples. According to the results, this method has the potential to be used in the routine analysis and quality control of pharmaceutical formulations. [ABSTRACT FROM AUTHOR]

Copyright of Anuario Electrónico de Estudios en Comunicación Social 'Disertaciones' is the property of Colegio Mayor de Nuestra Senora del Rosario and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper continues a series in which we study deficiencies in previously published works concerning fixed point assertions for digital images. [ABSTRACT FROM AUTHOR]

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

With the increasing use of digital images, it is essential to protect confidential image data from unauthorized access. In view of the security problems of digital image in the fields of communication, storage and transmission, this study proposes a digital envelope technology encryption scheme with high security and high speed based on the advantages of symmetric algorithm model and asymmetric algorithm model. This method is based on AES(Advanced Encryption Standard) and ECC(Elliptic Curve Cryptography), and optimized ECC hardware architecture is used for symmetric key sharing to enhance the security of the key. The traditional AES is optimized by adding pseudo- random numbers, using column shift instead of column obfuscation, and three dimensional Sbox box to maintain the Shannon diffusion and obfuscation principle while reducing the time complexity. This study the digital image encryption simulation and performance test of AES algorithm are carried out on FPGA (Field Programmable Gate Array). The test results show that the proposed encryption scheme has the advantages of rapidity, high security and effectiveness, and can better achieve image encryption. [ABSTRACT FROM AUTHOR]

Purpose: To compare the applicability of modified US Public Health Service (USPHS) and FDI criteria for evaluating glass ionomer cement (GIC) restorations in primary posterior teeth through digital image analysis. Methods: This comparative analytic study was conducted at the Children's Dental Clinic RSKGM FKG UI, involving 40 GIC restorations on lower first primary molars in children aged 4–9 years. After cleaning, the restorations were assessed clinically using modified USPHS and FDI criteria before taking digital images, then the collected images were re-evaluated using both sets of criteria, and the clinical assessment results were compared to the digital image assessment results. Results: Statistical analysis revealed significant differences between the clinical evaluation of GIC restorations in primary teeth and their corresponding digital photographs when using the modified USPHS criteria, and although the use of FDI criteria yielded different results, these differences were not statistically significant. Conclusion: The assessment of GIC restorations through digital images aligns more closely with clinical assessments using the FDI criteria compared to the modified USPHS criteria. [ABSTRACT FROM AUTHOR]

The paper elaborates on methods of digital image compression. The focus is on the compression method that represents a raster image as a set of multiply thinned sub-images. Sub-images are processed consecutively to generate special reference images. The difference between the synthesized reference image and original sub-image forms a divergence array. The algorithm introduces a discrete error into the divergence array to provide the actual bit-depth reduction. However, the introduction of the error inevitably impairs the quality of the decompressed image. The aim is to make sure that the parametric smoothing of divergence arrays can lessen this quality impairment without changing the bit depth reduction originally provided by the method. Numerical experiments on real digital images are carried out to prove that the use of parametric smoothing improves noticeably the efficiency of the image compression method under discussion. [ABSTRACT FROM AUTHOR]