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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]

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]

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 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.

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]

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]

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

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]

The problem of calculating a quantitative quality assessment of digital images of metal object fractures recorded by a camera or a digital microscope is considered. Quality assessment is performed when the reference images are absent. The paper presents an approach based on calculation of local estimates followed by analysis of their distribution. Several variants for calculation of local estimates have been studied. Those whose distribution is unimodal were selected. It is shown that the average of local estimates is an acceptable general characteristic of image quality if they have a normal (Gaussian) distribution. In this case, the average is one of its parameters. Otherwise, the parameters of the Weibull distribution can serve as more accurate quantitative characteristics of image quality in general. The proposed approach divides more objectively the set of the analyzed images into two groups those with satisfactory or unsatisfactory quality for performing expert studies using images. Examples of the quality assessment of different object images recorded at different resolutions are presented.

Purpose: is to propose a model for managing a digital brand image brand based on consecutive feedback loops from the image targetaudience. Methods: collection, processing, and analysis of secondary data were carried out on the basis of desk research, synthesis methods, contentanalysis, and systemic, structural and functional data evaluation using an interdisciplinary approach. Results: systemized methodological approaches in Russian and foreign practice by the authors served as the basis for development of a digital brand image management model based on consecutive feedback loops from the image target audience. Within the framework of the proposed model, it is supposed to create systems in a digital environment for providing feedback to the image target audience and monitoring of formed relationships with consumers. The authors' methodological recommendations for implementation of the proposed model are manifested in detailed approaches to organizing the relationship of the image object with the image target audience through different types of feedback loops, methods of monitoring responses and reactions of target consumers, methods of assessing image in a brand's digital environment to close feedback loops. Conclusions and Relevance: loops of feedback from the brand’s image target audience are a causative mechanism of dynamic nature in which the output data of subjective and objective consumer assessment of brand reputation is used as input data for the management and enhancement of the digital image. Setting up a system of feedback from consumers through digital channels on the basis of consecutive loops allows you to correlate planned indicators of the brand image with the actual indicators, as well as identifi cation of the existing problems and introducing timely adjustments to decisions on managing a brand’s digital image.

This paper continues a series discussing flaws in published assertions concerning fixed points in digital images.

The Internet is an important part of modern life; however, most virtual processes are beyond the scope of legal control. The state as a legislator regulates relations that originate in the real world. As a result, the law often fails to take into account the specifics of virtual environment. The poor legal regulation comes from the fact that the legislator cannot determine whether digital rights and freedoms are new categories or a continuation of the existing legal provisions. In this article, the author attempts to identify the correct approach by answering the obvious question: Is there a digital life and a separate digital personality, or is the digital space a continuation of the constitutional right to life exercised by a real-life legal subject? The research relied on standard methods of cognition and specific scientific methods, e.g., comparative and formal logical analyses. While analyzing the concept of the constitutional right to life, the author defines the categories of digital image and digital personality in order to develop a variant of legal regulation of digital rights and freedoms. The validity of digital personality depends on the approach the legislator chooses to regulate digital rights and freedoms. If the legislator sees digital rights and freedoms as a continuation of the rights and freedoms that exist in reality, a digital personality is impossible: it is a digital image of a real person in virtual space. If the legislator evaluates rights and freedoms as a new category, a digital personality is a valid concept, but only in the context of artificial intelligence.

Objective: This study aimed to develop a method for predicting the body weight of beef cattle using meta-analysis based on digital image processing. Materials and Methods: The meta-analysis process commenced by collecting studies with the keywords "beef cattle," "correlation," "digital image," and "body weight" from Google Scholar and Science Direct. The obtained studies were reviewed papers based on their titles, abstracts, and content, and then categorized by authors, year, country, sample size, and correlation coefficient. A digital image of body measurements used included wither and hip height, chest depth, heart girth, body length, and top view. The statistical analysis was conducted by calculating effect sizes using the correlation coefficient and sample sizes. Results: The results of the meta-analysis, based on 3,017 cattle from 13 selected studies, showed the highest and lowest correlation coefficients for the top view variable and hip height. Based on cattle breed, significant differences (p < 0.05) were observed in the wither height variable with correlation coefficients of 0.94, 0.79, and 0.66 for Hanwoo, Holstein, and Simmental, respectively. Based on sex, significant differences (p < 0.05) were seen in the wither height variable, with correlation coefficients of 0.73 for males and 0.90 for females, while for hip height, the values were 0.70 and 0.87, respectively. Conclusion: In conclusion, to achieve the best accuracy in predicting the body weight of beef cattle based on a digital image, the top view variable can be used. However, for ease of field experimentation, body length or chest depth can also be used while taking breed and sex categories into the model. [J Adv Vet Anim Res 2024; 11(1.000): 153-160]

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.

This paper proposes a novel deep learning model incorporating attention mechanisms for the analysis of slope stochastic fields. Initially, a deep learning model is designed to digitally image the stochastic field features of soil strength variability. This is achieved by discretizing the slope soil stochastic field using the Karhunen-Loeve expansion method and transforming the discrete results into digital images. These images are then used to establish a Convolutional Neural Network (CNN) surrogate model that maps the implicit relationship between stochastic field images and slope functional function values, thus calculating the probability of slope failure. The precision of the CNN surrogate model is enhanced through Bayesian optimization and five-fold cross-validation. Moreover, to overcome the limitations of existing data-driven landslide stability prediction models, this study also introduces a Spatial-Temporal Attention (STA) mechanism. By combining the CNN with Long Short-Term Memory (LSTM) networks, the model can accurately approximate the actual results of slope stability calculations in scenarios of high-dimensional representation imaging of stochastic fields and low-probability slope instability. Consequently, this significantly improves the computational efficiency of slope reliability analysis considering stochastic field simulations. [ABSTRACT FROM AUTHOR]

Introduction: The consumption of alcohol-based hand sanitizers has increased significantly after the pandemic caused by SARS-CoV-2. Despite the conclusion of the health emergency declared by the WHO in 2023, the habit of sanitizing hands with ethanol-based gel sanitizers has been globally adopted by the population. Since general methods described in official compendia for ethanol determination such as gas chromatography or distillation are laborious and not-specific to carbomer-containing gels, this work proposes an alternative method based on digital image colorimetry. Method: The digital image (provided by ethanol-phenolphthalein reaction) was captured and transformed into an analytical signal based on the Red-Green-Blue system. The image acquisitions were performed using a Samsung Galaxy J6 smartphone, and the signals were generated using the Photometrix Pro® free program. The method was validated in accordance with ICH and applied in commercial samples. Additionally, the proposed method was evaluated for its environmental impact using the Green Analytical Process Index (GAPI) tool. Pictograms were generated using the ComplexGAPI® free program. Results: Green channel exhibited a linear response in the calibration curves for ethanol concentrations ranging from 5 to 40 % (w/w) in acidic medium. The method showed linearity, precision, accuracy, and robustness. Conclusions: The proposed method presented as main advantages the use of low-cost and easy-to-handle devices and reduced reagent consumption, in accordance with green analytical chemistry principles. [ABSTRACT FROM AUTHOR]

Image processing software is designed to manipulate the digital images by capturing and converting them into digital form and to accomplish manipulations on traffic data. In this paper the methodology for detecting vehicles on highways is presented. Due to urbanization all over the world, the growth of traffic is shattered since few years and we entered in to the era of big data for transportation. The present study involves the understanding of fundamental elements of traffic, the problems in collecting the data, and their suitable corrections (speed, lateral and longitudinal) to be applied, extraction of error free macroscopic data, modelling and analysis of the macroscopic data. TRAZER is used for trajectory data extraction and the macroscopic relationships investigated by the trajectory data. Problems with TRAZER raw data extraction and corrections to it are discussed. The curves illustrate the basics traffic flow relationships between the three parameters such as density, flow and speed and from the analysis it is observed that the data is fitting Greenshields model. [ABSTRACT FROM AUTHOR]

In this work, the notion of digital fiber homotopy is defined and its properties are given. We present some new results on digital fibrations. Moreover, we introduce digital h-fibrations. We prove some of the properties of these digital h-fibrations. We show that a digital fibration and a digital map p are fiber homotopic equivalent if and only if p is a digital h-fibration. Finally, we explore a relation between digital fibrations and digital h-fibrations. [ABSTRACT FROM AUTHOR]

The current research aims to evaluate the positive and negative effects of electronic word of mouth communication on the digital image of travel agencies. In this context, research data were obtained from the five most-commented travel agencies in Nevşehir registered in the TripAdvisor system. In this study, which adopted the qualitative method, the research design was a case study and phenomenology. This research employing the document review technique and criterion sampling method, identified 7.259 online comments. This study subjected 1.724 online comments from 2022 to content analysis and descriptive analysis and obtained five main themes, twenty one positive sub-themes, eight negative sub-themes, and 4.534 codes. The main themes are tour, tourist guide, transport, eating and drinking, and accommodation. The tour main theme contains the sub-themes of types of tours, places included in the tour, all-inclusive package tours, price alternatives, organization and timing, transfer services, shopping opportunities, repreference and recommendations. The tourist guide main theme covers the sub-themes of accumulation of knowledge and detailed expression, language skill, personality traits, interest and relevance to tourists. The transport main theme includes the sub-themes of vehicle comfort and cleanliness, captain and safe driving, and in-vehicle catering. The eating and drinking main theme covers the sub-themes of restaurant location, food variety, and food taste. The accommodation main theme contains the sub-themes of referral to a travel agency, accommodation services, and food and beverage services. In the main themes, the most frequently mentioned topics were the tour and tourist guide who represent the travel agency. On the other hand, the most focused themes by the tourists were the tourist guide's knowledge and detailed explanation and the re-preference and recommendations. Overall, it has been concluded that travel agencies have a positive image, and the determined themes affect digital image formations. [ABSTRACT FROM AUTHOR]

The article is devoted to the study of the concept of the Metaverse as a future research space for jurisprudence. It has been established that the Metaverse is known as a special virtual space created by computer technologies, where users can interact with each other, between various digital objects and the digital environment thanks to the created avatars – virtual digital images. It has been determined that the Metaverse can be called a copy of the real world, but in a virtual space that combines modern technologies, virtual and augmented reality, three-dimensional graphics, artificial intelligence, social networks, as well as other advanced innovative technologies. The focus is on the fact that the hypothetical transfer of real life to the virtual world raises a large number of legal issues and creates a huge field for scientific research, in particular, in jurisprudence. It was found that legal problems that may arise in the future in the Metaverse should be solved by lawyers together with philosophers, technologists, experts, and scientists from other fields of science based on developed uniform standards and norms. The opinion of scientists regarding the creation of a single international legal framework for the regulation of the Metaverse due to the absence of borders in the Metaverse and non-belonging to a specific state is supported. It is stated that the process of developing a unified international legal framework for the regulation of the Metaverse is a very large, complex, and long-lasting work that requires taking into account the cultural, national, legal, and moral differences of different countries. Such a difficult task requires the approval and consent of states, international organizations, and societies. However, given the potential for active development of the Metaverse and its impact on society, these efforts can contribute to its effective and consistent regulation. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to develop a method for predicting the body weight of beef cattle using meta-analysis based on digital image processing. Materials and Methods: The meta-analysis process commenced by collecting studies with the keywords "beef cattle," "correlation," "digital image," and "body weight" from Google Scholar and Science Direct. The obtained studies were reviewed papers based on their titles, abstracts, and content, and then categorized by authors, year, country, sample size, and correlation coefficient. A digital image of body measurements used included wither and hip height, chest depth, heart girth, body length, and top view. The statistical analysis was conducted by calculating effect sizes using the correlation coefficient and sample sizes. Results: The results of the meta-analysis, based on 3,017 cattle from 13 selected studies, showed the highest and lowest correlation coefficients for the top view variable and hip height. Based on cattle breed, significant differences (p < 0.05) were observed in the wither height variable with correlation coefficients of 0.94, 0.79, and 0.66 for Hanwoo, Holstein, and Simmental, respectively. Based on sex, significant differences (p < 0.05) were seen in the wither height variable, with correlation coefficients of 0.73 for males and 0.90 for females, while for hip height, the values were 0.70 and 0.87, respectively. Conclusion: In conclusion, to achieve the best accuracy in predicting the body weight of beef cattle based on a digital image, the top view variable can be used. However, for ease of field experimentation, body length or chest depth can also be used while taking breed and sex categories into the model. [ABSTRACT FROM AUTHOR]

BackgroundFemale genital schistosomiasis (FGS) is characterised by cervico-vaginal lesions such as grainy sandy patches, homogeneous yellow sandy patches, and abnormal blood vessels. Diagnosis, treatment, and surveillance of FGS presents challenges due to the absence of diagnostic tools in endemic areas. Likewise, a tool for quantification of the lesions is missing. This study aimed to validate the digital gridded imaging technique (DGIT) for quantification of three specific cervical lesion types associated with FGS.MethodsUsing the QubiFier software program, 70 cervical photographic images obtained from women diagnosed with FGS and treated with Praziquantel (PZQ) were randomly sampled for a validation study. The women lived in a S. haematobium-endemic area of Madagascar. For each image, a semi-transparent grid was applied, composed of 424 equally sized squares positioned to cover the cervix. Squares exhibiting any of the specific lesions were marked by three observers to determine the grainy sandy patches proportion (GSP), homogeneous yellow sandy patches proportion (HSP), and abnormal blood vessel proportion (BVP). To train the observers, a set of ten images was utilized, leaving 60 images for the validation. To test inter-rater reliability, the 60 cervical images were scored independently by three observers. To test intra-rater reliability, ten images were scored twice with a two-week interval. Intraclass correlation coefficient (ICC) was used as the main statistical method to assess the reliability between observers.ResultsThe median age of the included women was 26.5 (IQR 20.8-33.0) years, and 74% of them had detectable S. haematobium eggs in their urine. The three proportions were found to be consistent and reliable across the observers, as well as the rescoring on Days 0 and 14. Inter-rater reliability was good for all three cervical lesion types (ICC 0.768-0.890). Intra-rater reliability was good for GSP (ICC 0.832) and excellent for HSP and BVP (ICC 0.932 and 0.982, respectively).ConclusionIn this study DGIT was validated as a potential morbidity detection method for quantification of the three lesion types associated with FGS.

Protecting ownership of digital content is challenging in today’s online world, especially when sharing content through social networks and instant messengers. One possible solution is the use of watermarking; however, if the watermarking method is not robust enough, the watermark can get damaged or erased during transmission. This study introduces a template-based watermarking method with neural network-based extraction, strengthened by error-correcting polar codes, that is designed to work well on major social networks like Facebook, Facebook Messenger, VK, Telegram, Snapchat, Pinterest, and WhatsApp. The method can embed imperceptible (PSNR=39.66) watermarks ranging from 650 to 1600 bits for high-definition images (1920-by-1080 pixels) with small probability (0.01) of erroneous extraction after being transferred through social networks. The key feature of the method is its ability to work with real communication channels, as shown during its testing.

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The rapid advancement of science and technology has led to the potential for easy manipulation of multimedia content through the use of diverse editing tools. This poses a significant threat to the credibility and integrity of multimedia information. Consequently, substantiating digital images is becoming gradually crucial as digital images hold vital information and are used as essential pieces of evidence in various sectors. The necessity and relevance of digital image forensics have drawn several academics to develop various detection procedures in image forensics. Passive image forgery detection is the foundation of image forensics. Some common passive forgeries that influence the image's authenticity are image splicing, copy-move, and retouching. In recent times, substantial research effort has been devoted to developing novel approaches for detecting several image forgeries. This study provides an overview of similar research efforts that have been carried out utilizing a well-defined methodology. Our goal is to create an efficient way for image forensics researchers to discover new features of forgeries. This study presents a brief introduction to image forensics, including a historical perspective, taxonomy, and framework of image forgery detection approaches. Various resources useful to academic researchers, such as journals, datasets, websites, and performance parameters are explored and presented. This paper will provide a comprehensive review that will aid researchers in overcoming the numerous challenges experienced in earlier studies. Also, future directions are provided to help scholars in this domain. The purpose of this research is to evaluate passive image forgery detection approaches, therefore benefiting new researchers. [ABSTRACT FROM AUTHOR]

The integrity of the information content, in particular the digital image considered in the work, is one of the criteria for its security. Image splicing is one of the most common methods of forgery today. This way of breaking the integrity of the image is easily implemented with the help of modern graphic editors (PhotoShop, Gimp, etc.). During image splicing, one image is created from several. The task of detecting image splicing is the subject of research by scientists in the field of information security around the world, but it remains relevant today: there are no universal expert methods that can even detect the presence of a "foreign" part in any case of image splicing; the effectiveness of existing methods needs to be increased even in limited conditions of their application. The paper presents a new polynomial degree 2 expert method, which makes it possible to separate the original image from one whose integrity is violated. The method is based on the analysis of the properties of the blocks smallest singular values matrix, which corresponds to the matrix of the digital image. The method localizes the area containing the part of another image. The developed method is based on established differences in the properties of the blocks smallest singular values matrix for digital images in different formats - lossy and lossless. These differences are reflected in the additional method of separating images in different formats proposed in the work. This method can be used both alone and as a component of the developed image splicing detection method in the case where the falsified image consists of parts of images stored with loss and without loss. It was established that the proposed expert method provides an increase in the efficiency of image splicing detection in comparison with the modern analogue method. [ABSTRACT FROM AUTHOR]

This paper continues a series discussing flaws in published assertions concerning fixed points in digital images. [ABSTRACT FROM AUTHOR]