Search

Purpose: To examine if there are differences in smile esthetic evaluation scores by orthodontists and prosthodontists and if there are changes in those scores when two evaluations are performed, when the time of observation is increased, and when the lips are present or absent from the images. Materials and Methods: In total, 12 individuals participated in this pilot study. Two digital images were taken from each individual. The first digital image was a smile view, and the second image was an intraoral view. Two presentation files were prepared, with two images for each individual. The smile and dental attractiveness ratings were obtained from 10 specialists. Results: Repeated measures ANOVA was applied including all four within-subject factors, the evaluator (E), the repetition (R), the time of observation (T), and the presence or not of lips (L). Factors E, T, and L each had a statistically significant main effect. E and R had a statistically significant combined effect. In particular, the esthetic score for the view with smile was overall higher than for the intraoral view. The same results were obtained when the analysis was repeated with the 10 evaluators grouped to prosthodontists and orthodontists, and the prosthodontists tended to score higher than the orthodontists. Conclusions: The evaluator and the presence of lips have a statistically significant effect. The present pilot study has found that three (E, L, R) out of four factors (T) are important for the evaluation of dental esthetics. [ABSTRACT FROM AUTHOR]

This study investigates bilayers of 0.94(Na1/2Bi1/2)TiO3–0.06BaTiO3 (NBT–6BT) and 0.90(Na1/2Bi1/2)TiO3–0.06BaTiO3–0.04(K0.5Na0.5)NbO3 (NBT–6BT–4KNN) using digital image correlation, enabling the separate analysis of strain response in each layer. The bilayers were electrically connected without mechanical connection (polarization coupled) as well as mechanically and electrically connected (polarization and strain coupled) to determine the role of interlayer mechanical interactions. The large signal longitudinal and transverse piezoelectric coefficients, d 33 ∗ and d 31 ∗ , were characterized for both cases. In the polarization coupled composite, d 33 ∗ decreased linearly from 410 to 260 pm/V with increasing vol. % NBT–6BT. In contrast, in the polarization and strain coupled case, d 33 ∗ and d 31 ∗ were maximum at 50 vol. % NBT–6BT with values of 440 and −130 pm/V, respectively, highlighting the critical role of strain interactions in ceramic–ceramic composites. The stress-induced phase transformation through strain coupling significantly impacted the electromechanical response, with improved strain values observed in the NBT–6BT–4KNN layer. Furthermore, this study highlights the variability in the significance of strain coupling within bilayer systems as a function of the altering volume fraction of their constituent components. [ABSTRACT FROM AUTHOR]

Cloud Computing (CC) is a fast emerging field that enables consumers to access network resources on-demand. However, ensuring a high level of security in CC environments remains a significant challenge. Traditional encryption algorithms are often inadequate in protecting confidential data, especially digital images, from complex cyberattacks. The increasing reliance on cloud storage and transmission of digital images has made it essential to develop strong security measures to stop unauthorized access and guarantee the integrity of sensitive information. This paper presents a novel Crayfish Optimization based Pixel Selection using Block Scrambling Based Encryption Approach (CFOPS-BSBEA) technique that offers a unique solution to improve security in cloud environments. By integrating steganography and encryption, the CFOPS-BSBEA technique provides a robust approach to secure digital images. Our key contribution lies in the development of a three-stage process that optimally selects pixels for steganography, encodes secret images using Block Scrambling Based Encryption, and embeds them in cover images. The CFOPS-BSBEA technique leverages the strengths of both steganography and encryption to provide a secure and effective approach to digital image protection. The Crayfish Optimization algorithm is used to select the most suitable pixels for steganography, ensuring that the secret image is embedded in a way that minimizes detection. The Block Scrambling Based Encryption algorithm is then used to encode the secret image, providing an additional layer of security. Experimental results show that the CFOPS-BSBEA technique outperforms existing models in terms of security performance. The proposed approach has significant implications for the secure storage and transmission of digital images in cloud environments, and its originality and novelty make it an attractive contribution to the field. Furthermore, the CFOPS-BSBEA technique has the potential to inspire further research in secure cloud computing environments, making the way for the development of more robust and efficient security measures. [ABSTRACT FROM AUTHOR]

Background/Objectives: The damage assessment of dental instruments, such as endodontic files, is crucial to ensure patient safety and treatment quality. Conventional scanning electron microscopy (SEM) has been the gold standard for this purpose; however, its limited accessibility and complex sample preparation protocols hinder its routine use in clinical settings. This study proposes a novel system that leverages digital photography and advanced image processing techniques as a viable alternative to SEM. Methods: Our system accurately detects early instrument damage by capitalizing on the high resolution of digital images. Its exceptionally user-friendly interface, portability, and key features make it highly suitable for daily clinical practice. Results: Our findings suggest that the proposed system provides image quality comparable to SEM. Conclusions: Image stacking provides a practical, efficient, and objective method for assessing endodontic instruments' morphology. By detecting early damage, this system significantly improves the safety and quality of endodontic procedures, especially for reusable NiTi files, instilling confidence and security in its use. It offers a cost-effective and user-friendly alternative to traditional methods such as visual inspection and SEM, making it a comfortable and confident choice for both research and clinical settings. [ABSTRACT FROM AUTHOR]

Understanding the long-term behavior of brittle rocks requires fundamental consideration of time-dependent strain evolution and brittle creep processes. Previous studies have evaluated sub-critical crack growth during time-dependent deformation and damage evolution in brittle rocks; however, there is an incomplete knowledge of how damage evolves spatially and temporally within the body of intact rocks, where distributed regions of damage interact and coalesce during creep. This paper presents laboratory research focusing on evaluating brittle creep damage processes in Stanstead granite (SG) using 2-dimensional digital image correlation (2D-DIC). In the laboratory, the prismatic SG specimens were loaded beyond an estimated Crack Damage stress threshold (CD) level and then maintained a constant stress to initiate the creep process. DIC was used to characterize full-field spatiotemporal strain evolution, which was then interpreted in the context of local regions of "damage", determined according to a strain-based criterion. A method was proposed for identifying "existing" and "new" damage regions over specified intervals during the test, followed by spatial clustering of these regions to assess their spatiotemporal evolution. The clustering analysis results demonstrated the extension of existing damage regions was the main damage process during brittle creep, which is consistent with existing models of sub-critical crack growth. In addition, temporal analysis of tensile and shear strains on a point-by-point basis revealed both new damage formation and the strain concentration within existing damaged regions significantly contribute to overall specimen strain during primary creep. In contrast, during secondary creep, increases in specimen deformation are influenced by the accumulation of strains within already damaged regions. Highlights: Strain-based evolution of damage is characterized during brittle creep in intact rocks. The temporal evolution of tensile and shear strains during primary and secondary creep is quantified. Relative influences of newly damaged regions are investigated in evaluating the damage process during creep. [ABSTRACT FROM AUTHOR]

With the tremendous technological progress in many fields, especially in communications, and to protect the information transmitted through communication channels, especially digital images, researchers in this field try to find new methods that provide a high level of security. Combining chaotic maps and DNA encryption provides a high level of security because of their high randomness, complexity, and sensitivity to initial conditions. This study presents a novel technique for protecting images by encrypting their data at three levels. The first level involves redistributing the image points. In contrast, the second level combines a 1D chaotic map (PWLCM) and DNA sequences. In the third level, three keys are produced using three 1D chaotic maps (Logistic maps, Tent maps, and Sine maps). Each key encrypts data of a specific color. The results of the statistical tests showed that the suggested technique provides a good security level compared to the methods, achieving an average Number of Pixels Change Rate (NPCR) of 99.62, a Unified Average Changing Intensity (UACI) of 32.83, and an entropy of 7.9972. [ABSTRACT FROM AUTHOR]

Digital Image Forgery (DIF) detection involves identifying instances where a portion of image is copied and placed in different areas within same image to create a seemingly authentic but altered version. However, the detection of small duplicated regions is challenging, especially when noise is present in the image. This issue becomes more significant when the model is trained on noisy data as it negatively affects its accuracy. This research proposes an Elite Opposition-based Learning with Black Widow Spider Optimization (EBWSO) for feature selection and Cyclic Symmetry Convolutional Neural Network (CSCNN) for detection to enhance accuracy in image forgery detection. Pre-processing techniques such as Single Image Super-Resolution (SISR) and Histogram Equalization (HE) are used for image enhancement. The VGG16 and ResNet50 are used for feature extraction in digital images, done through identifying the key features such as edges and shapes. The EBWSO technique is utilized for feature selection by updating the relevant features and balancing exploration and exploitation. Detection is carried out using the CSCNN technique, which is designed to be rotation-invariant and aims to enhance detection accuracy. The EBWSO-CSCNN model accurately classifies image forgery when compared to the existing techniques such as Stacked Sparse Denoising Autoencoder (SSDAE) and Simple Linear Iterative Cluster (SLIC) algorithm. The proposed method achieves a better accuracy of 99.15% on MICC-F220, 98.10% on MICC-F600, 99.25% on MICC-F2000, and 98.95% on the CASIA 2.0 dataset. [ABSTRACT FROM AUTHOR]

The document discusses a legal case involving copyright infringement, performers' rights infringement, and unfair competition between two companies, Sihai Company and Mofa Company, regarding virtual digital human content. The court ruled that virtual digital humans are not entitled to copyright or neighbouring rights, but content related to them may be protected. Sihai Company's unauthorized use of Mofa Company's virtual digital human content was deemed infringing, constituting false advertising and unfair competition. The court upheld the original judgment, awarding Mofa Company compensation for economic losses and reasonable expenses for rights protection. [Extracted from the article]

ABSTRACT Color palette is a critical component of art, design, and lots of applications, providing the basis for organizing and utilizing colors to achieve specific objectives. However, generating color palettes from digital images presents unique challenges due to the complexity of colors in images. This review comprehensively investigates various techniques for generating color palettes from digital images and provides a thorough classification and discussion of these techniques from multiple perspectives. A color space must be selected to generate a color palette, and a generation method must be employed. This paper offers a concise overview of color spaces, an introduction to current palette generation methods, and an analysis of the metrics used to evaluate color differences between palettes. The review encompasses traditional manual methods and computer‐aided automation methods, further categorized as histogram‐based, clustering‐based, and neural network‐based methods. Discussion on the strengths, weaknesses, and applicability of existing methods are presented, and also opportunities for future research to enhance color palette generation from digital images are identified. [ABSTRACT FROM AUTHOR]

Image based screening is now routinely available for early detection of cancer and other diseases. Quantitative analysis for effects of risk factors on digital images is important to extract biological insights for modifiable factors in prevention studies and understand pathways for targets in preventive drugs. However, current approaches are restricted to summary measures within the image with the assumption that all relevant features needed to characterize an image can be identified and appropriately quantified. Motivated by data challenges in breast cancer, we propose a nonparametric statistical framework for risk factor screening that uses the whole mammogram image as outcome. The proposed permutation test allows assessment of whether a set of scalar risk factors is associated with the whole image in the presence of correlated residuals across the spatial domain. We provide extensive simulation studies and illustrate an application to the Joanne Knight Breast Health Cohort using the mammogram imaging data. [ABSTRACT FROM AUTHOR]

Digital pathology using whole-slide imaging (WSI) scanners aids pathologists challenged by diagnostic volume and novel diagnostic methods. Colour reproducibility of WSI scanners is crucial for accurate digital diagnoses. We propose an objective and quantitative method for evaluating colour reproduction in digital images of pathological specimens from two perspectives: true colour value reproduction and colour discrimination. We define 57 critical features for evaluation, extract tissue structures, and establish diagnostic criteria. Our method is unique and useful for clinical use. We tested a prototype scanner, and it satisfied all criteria for true colour value reproduction (all the colour samples corresponding to the selected tissue evaluation points were experimentally within the acceptable range) and colour discrimination (the colour difference ΔE2000 calculated from the corresponding colour sample combinations exceeded the lower permissible limit for the selected discrimination points). This evaluation method and WSI scanner can contribute to accurate colour reproduction in digital pathology. [ABSTRACT FROM AUTHOR]

Background and Objectives: The present study aims to assess and compare the accuracy of post-space impressions captured by three different intraoral scanners (IOS) using various canal diameters. Methods: Three extracted natural maxillary central incisors were selected and prepared for a 1 mm wide margin and a 3 mm ferrule. All steps required for the endodontic procedure were performed, and the post space was prepared using post drills. The post length was kept constant at 12 mm, whereas the width was varied (Group 1: 1.4 mm, Group 2: 1.6 mm, and Group 3: 1.8 mm). Three IOSs (Trios3, iTero2, and Medit i700) were used to acquire a digital impression of the prepared post space. Each tooth was scanned 10 times by each scanner. So, in the end, 90 digital images were recorded, and the STL files were stored. GC Pattern resin was used to fabricate resin post and core patterns, which were scanned using an extraoral scanner (EOS). The STL file obtained was used as the reference file. To evaluate the trueness of the tested IOSs, each three-dimensional scan from an IOS was superimposed on the reference scan with the help of the Medit Design software 2.1.4. The software generates color plots and gives numerical values as deviations in the Root mean square (RMS) for the variance between the two superimposed scans. The data collected was tabulated for statistical analysis. One Way ANOVA was used to test the significance difference between three different IOSs, followed by Bonferroni Post-hoc test pairwise test to identify the differences between every two different IOS. Statistical significance was set at p < 0.05. Results: The mean deviation for trueness in post space impression values recorded by the Medit i700 was highest among groups 1, 2, and 3 [0.825 (±0.071), 0.673 (±0.042) and 0.516 (±0.039), respectively], followed by iTero2 [0.738 (±0.081), 0.569 (±0.043) and 0.470 (±0.037), respectively] and Trios3 [0.714 (±0.062), 0.530 (±0.040) and 0.418 (±0.024), respectively]. Significant differences were found between the groups for all three IOSs (Trios3: p-value < 0.0001; iTero2: p-value < 0.0001; Medit i700: p-value < 0.0001). Conclusions: Within the limitations of this study, it can be concluded that Trios3 IOS has higher accuracy (as it exhibited minimal deviation for trueness) in recording post space, followed by iTero2 and Mediti700 IOS. As the diameter of the post space is increased, the accuracy of recording by IOS increases. For all the tested IOSs (except for Trios3 and iTero2, when used to record post space with 1.8 mm canal diameter), the deviations in trueness were higher than the clinically acceptable limits. Thus, IOSs should be used cautiously when recording impressions of post spaces. [ABSTRACT FROM AUTHOR]

Chaos-based encryption is promising for safeguarding digital images. Nonetheless, existing chaos-based encryption algorithms still exhibit certain shortcomings. Given this, we propose a novel multi-channel image encryption algorithm that leverages pixel reorganization and hyperchaotic maps (MIEA-PRHM). Our MIEA-PRHM algorithm employs two hyperchaotic maps to jointly generate chaotic sequences, ensuring a larger key space and better randomness. During the encryption process, we first convert input images into two fused matrices through pixel reorganization. Then, we apply two rounds of scrambling and diffusion operations, coupled with one round of substitution operations, to the high 4-bit matrix. For the low 4-bit matrix, we conduct one round of substitution and diffusion operations. Extensive experiments and comparisons demonstrate that MIEA-PRHM outperforms many recent encryption algorithms in various aspects, especially in encryption efficiency. [ABSTRACT FROM AUTHOR]

Image segmentation is the process of splitting a digital image into distinct segments or categories based on shared characteristics like texture, color, and intensity. Its primary aim is to simplify the image for easier analysis while preserving its important features. Each pixel in the image is assigned a label, grouped together by pixels with similar traits together. Segmentation helps to delineate boundaries and identify objects such as curves or lines within the image. The process generates a series of segmented images that cover the entire original image. This article reviews emerging applications of image segmentation in medical diagnostics, specifically employing nature-inspired optimization algorithms (NIOAs). It begins by outlining different segmentation methods and NIOAs types, then by examining relevant databases and medical imaging technologies. The study draws on a diverse range of research sources. Finally, this paper briefly discusses the challenges and future trends of medical image segmentation using NIOAs to detect different diseases. [ABSTRACT FROM AUTHOR]

Physical unclonable function labels have emerged as a promising candidate for achieving unbreakable anticounterfeiting. Despite their significant progress, two challenges for developing practical physical unclonable function systems remain, namely 1) fairly few high-dimensional encoded labels with excellent material properties, and 2) existing authentication methods with poor noise tolerance or inapplicability to unseen labels. Herein, we employ the linear polarization modulation of randomly distributed fluorescent nanodiamonds to demonstrate, for the first time, three-dimensional encoding for diamond-based labels. Briefly, our three-dimensional encoding scheme provides digitized images with an encoding capacity of 109771 and high distinguishability under a short readout time of 7.5 s. The high photostability and inertness of fluorescent nanodiamonds endow our labels with high reproducibility and long-term stability. To address the second challenge, we employ a deep metric learning algorithm to develop an authentication methodology that computes the similarity of deep features of digitized images, exhibiting a better noise tolerance than the classical point-by-point comparison method. Meanwhile, it overcomes the key limitation of existing artificial intelligence-driven classification-based methods, i.e., inapplicability to unseen labels. Considering the high performance of both fluorescent nanodiamonds labels and deep metric learning authentication, our work provides the basis for developing practical physical unclonable function anticounterfeiting systems. The authors use polarized fluorescent nanodiamonds to achieve high-dimensional encoding for physical unclonable functions, and propose a deep metric learning based approach for robust authentication of unseen labels. [ABSTRACT FROM AUTHOR]

OBJECTIVE To review data on the advances in equlne ophthalmic imaging that have been made during the past 5 years and highlight advantages of using multiple imaging modalities to improve clinical observational skills and improve diagnostic accuracy. METHODS A literature review from 2019 through 2024 of equine ophthalmic digital photography, fundus photography, ocular and orbital ultrasonography (US), ultrasound biomicroscopy (UBM), confocal microscopy (CM), spectral domain optical coherence tomography (SD-OCT), radiography, CT, and MRI. RESULTS Digital photography remains the cornerstone of equirle ophthalmic imaging for documenting examination findings, sharing information with colleagues, and consulting with specialists. Digital images also allow for in-depth postexamination review and evaluation, often revealing subtleties that may have otherwise gone undetected during the ophthalmic examination. Advanced imaging modalities are being used more frequently in equine ophthalmology, especially those that can be used with the horses standing under sedation, including US, UBM, SD-OCT, CM, and CT. DISCUSSION Advances in equine ophthalmic imaging have led to many new clinical discoveries and to an increase in our knowledge of ocular anatomy and diseases in the horse. Many of these advanced diagnostic imaging modalities, such as MRI, CT, SD-OCT, and CM, are cost prohibitive and require substantial operator training to ensure proficiency. However, their availability in tertiary referral centers, such as veterinary teaching hospitals and large equine clinics/practices, is becoming more widespread. CONCLUSIONS Advanced equine ophthalmic imaging data contributes substantially to our general understanding of clinical and applied anatomy and improves our understanding of the underlying pathogenesis associated with specific diseases. [ABSTRACT FROM AUTHOR]

Ca ii K observations of the Sun have a great potential for probing the Sun's magnetism and activity, as well as for reconstructing solar irradiance. The Kodaikanal Solar Observatory (KoSO) in India, houses one of the most prominent Ca ii K archives, spanning from 1904 to 2007, obtained under the same experimental conditions over a century, a feat very few other sites have achieved. However, the KoSO Ca ii K archive suffers from several inconsistencies (e.g., missing/incorrect timestamps of observations and orientation of some images) which have limited the use of the archive. This study is a step towards bringing the KoSO archive to its full potential. We did this by developing an automatic method to orient the images more accurately than in previous studies. Furthermore, we included more data than in earlier studies (considering images that could not previously be analyzed by other techniques, as well as 2845 newly digitized images), while also accounting for mistakes in the observational date/time. These images were accurately processed to identify plage regions along with their locations, enabling us to construct the butterfly diagram of plage areas from the entire KoSO Ca ii K archive covering 1904 – 2007. Our butterfly diagram shows significantly fewer data gaps compared to earlier versions due to the larger set of data used in this study. Moreover, our butterfly diagram is consistent with Spörer's law for sunspots, validating our automatic image orientation method. Additionally, we found that the mean latitude of plage areas calculated over the entire period is 20.5 % ± 2.0 higher than that of sunspots, irrespective of the phase or the strength of the solar cycle. We also studied the north–south asymmetry showing that the northern hemisphere dominated plage areas during solar cycles 19 and 20, while the southern hemisphere dominated during Solar Cycles 21 – 23. [ABSTRACT FROM AUTHOR]

Image encryption is a vital field ensuring the secure transmission of digital images. In this study, encryption is the core process, employing complex mathematical algorithms and cryptographic keys to transform the original image into a secure format, shielding visual data from unauthorized access during transmission. To enhance security, the research integrates Fibonacci and advanced encryption standard (AES)-least significant bit (LSB) methodologies for a complex key generation system. This mechanism introduces intricate transformations within the image data, creating patterns challenging for potential attackers to decipher. Evaluation of the algorithm's performance reveals efficiency in terms of mean squared error (MSE) and peak signal-to-noise ratio (PSNR). The RGB cover image achieves the lowest MSE of 0.0001 and the highest PSNR values ranging from 44.31 to 49.27. Integration of the Fibonacci sequence notably improves visual quality, enhancing both MSE and PSNR metrics. Unified average changing intensity (UACI) and normalized pixel change rate (NPCR) assessments consistently show the effectiveness of the algorithm, with the RGB cover image presenting the highest UACI and NPCR values. Future research directions involve exploring advanced encryption algorithms, optimizing techniques for highdimensional datasets, and addressing ethical implications in image encryption, contributing to the development of adaptable and secure solutions. [ABSTRACT FROM AUTHOR]

Lately, image encryption has stand out as a highly urgent demand to provide high security for digital images against use and unauthorized distribution. A lot of existing researches use chaotic systems, symmetric or asymmetric schemes for image encryption, but cryptosystem based on one encryption technique only, faces many challenges like weak security and low complexity. Therefore, incorporating two or more different ciphering methods yields a secure and efficient algorithm to protect image information. In this work, a new image cryptosystem is suggested by joining zigzag scan technique, RSA algorithm and chaotic systems. These three security factors introduce Triple Incorporated Ciphering stages system (TIC). Initially, the plaintext image is divided into 8 × 8 non-overlapping blocks, then the odd blocks are isolated from the even blocks. After that, a new modified zigzag scan in two different directions is adopted for shuffling pixels in the odd and even blocks. This operation effectively enhances the shuffling degree. Next, the RSA algorithm is utilized after combining the scrambled blocks in one matrix. Finally, chaotic systems are implemented on the resultant encrypted matrix to complete the ciphering process. The chaos is implemented in two steps; confusion and diffusion. Duffing map is exploited in the confusion stage, whereas Lu system is adopted on the shuffled matrix in the ¨ diffusion stage. The simulation results show the superiority of TIC in both security and attacks robustness compared to other cryptographic algorithms. Therefore, TIC can be exploited in real-time communication systems for secure image transmission. [ABSTRACT FROM AUTHOR]

Nam June Paik's artistic experiments, especially the distortion of the electronic image with magnets, from which he started, provide the author a defining example and reference point for his audiovisual work. The possible combinations of analog and digital techniques open up new doors for contemporary media artists. In this artistic statement the author describes some of his own experimental works of art in the hopes of stimulating a discourse on experimentation and innovation. [ABSTRACT FROM AUTHOR]

The analog technology of scan processing was developed and used during the 1970s as a method of electronic image animation. Because their real time modes of operation strongly resemble those of electronic musical instruments, scan processors can be considered instruments with which to compose and play moving images. Although cheaper digital tools replaced them in commercial studios during the 1980s, scan processing has a distinctive aesthetic that continues to inspire both visual artists and instrument designers. This article examines the way that scan processing instruments' affordances arise from specific combinations of the user, technology, and situation by surveying a number of technical and artistic use cases involving two different instruments: the VP-8 Image Analyzer and the Rutt/Etra Video Synthesizer. The article also demonstrates how reenacting an instrument works through its historical affordances using current technological means to address present-day contexts. [ABSTRACT FROM AUTHOR]

Due to their distinct textures, porous cellular materials have been of interest to many engineering applications. Energy conversion through such materials, especially at high temperatures, is governed by naturally coupled conduction-radiation physics. Therefore, a thorough understanding of the conduction-radiation behavior within these materials is important to properly design and optimize these materials. Several numerical methods developed over the last few decades allow to solve and study the influence of different textural parameters on coupled conductive-radiative heat transfers. These numerical methods require 3D digitized images of the sample of interest obtained either using 3D imagery technique or generated using numerical algorithms. To better represent the complex geometry, the 3D image of the sample requires high spatial resolution, and for a sample which is representative of involved physics might contain hundreds of millions of voxels which is complex to solve and computationally expensive. To cope with this issue, we developed a parallelized discrete scale numerical approach using cell centered Finite Volume Method (FVM) and deterministic ray tracing to solve coupled heat transfer within highly porous large scale complex cellular materials. At the heart of this method rests a decomposition approach based on modified zonal method which significantly reduces the coupling efforts, memory requirements, and computation time. The results of two test cases presented in this study are cross-verified with those presented in literature and computed using Star-CCM+ software. Finally, the method is applied to virtual fibrous sample. These results present the ability of the solver to consider different boundary conditions, at large temperature gradient across boundaries, and to deal with large samples consisting hundreds of million of voxels while providing accurate results. We also present and discuss the sensitivity of other associated parameters on the final results. [ABSTRACT FROM AUTHOR]

Time and temperature are the crucial factors affecting the quality of perishable products. The device capable of recording temperature fluctuation and maintaining time has huge practical potential. In this research, a novel thermal history indicator (THI) was proposed based on covalent organic frameworks (COFs) with the irreversible color change performance. The color change and absorbance of the THI were investigated over a broad monitoring temperature range (− 22 ~ 55 ℃) and were closely correlated with its thermal accumulation over time and temperature. The activation energy (Ea) of the THI was calculated to be 55.31 kJ mol−1, which can be used to monitor the perishable products with deterioration reaction Ea ranging from 30.31 to 80.31 kJ mol−1. In addition, the THI also demonstrated the significant response to the environmental temperature fluctuations. Furthermore, the potential applications were evaluated by recording the changes of the THI gel and the total viable count (TVC) of food under the same conditions. The results indicated that the digital images of the colorimetric THI gel were correlated with TVC of food at 4 ℃ and 25 ℃, respectively. These results support that the proposed THI is potential for real-time, accurate, non-destructive, visually, and quantitatively monitoring food freshness during food supply chain distribution. [ABSTRACT FROM AUTHOR]

During the last decades, cultural heritage collections have been digitized, for example, for the use of academic scholars. However, earlier studies have mainly focused on the use of textual materials. Thus, little is known about how digitized photographs are used and searched in digital humanities. The aim of this paper is to investigate the applied search tactics and perceived barriers when looking for historical photographs from a digital image archive for research and writing tasks. The case archive of this study contains approximately 160,000 historical wartime photographs that are openly available. The study is based on a qualitative interview and demonstration data of 15 expert users of the image collection searching photographs for research and writing tasks. Critical incident questions yielded a total of 37 detailed real-life search examples and 158 expressed barriers to searching. Results show that expert users apply and combine different tactics (keywords, filtering and browsing) for image searching, and rarely using one tactic only is enough. During searching users face various barriers, most of them focusing on keyword searching due to the shortcomings of image metadata. Barriers were mostly in the context of the collection and tools. Although scholars have benefited from the efforts put into digitizing cultural heritage collections, providing digitized content openly online is not enough if there are no sufficient means for accessing the content. Automatic annotation methods are one option for creating metadata to improve the findability of the images. However, a better understanding of human information interaction with image data is needed to better support digitalization in the humanities in this respect. [ABSTRACT FROM AUTHOR]

In today's digital landscape, digital images are widely used for communication across various platforms, but they are also vulnerable to tampering and deception. The proliferation of image editing tools and software has made it easier for individuals to manipulate images, but extensive tampering can compromise the accuracy of conveyed information. The human eye is difficult to distinguish between original and tampered images, leading to potential misinformation. Therefore, robust mechanisms using powerful algorithms are crucial to examining image authenticity and detecting forgery. This is especially important in fields like forensics, where evidence manipulation can have significant implications. This work conducts a survey providing a structured overview of research on image forgery techniques and detection approaches. The survey discusses various approaches, including source camera identification, type-dependent and type-independent methods, and recent deep-learning approaches. The survey also presents benchmark datasets for training and validation. The survey aims to improve understanding of research paths in this field and demonstrate how techniques initially developed for a domain can be applied to other areas. It aims to assist other researchers in enhancing their expertise in digital forensics and forgery detection techniques. [ABSTRACT FROM AUTHOR]

Ensuring data security in today's digital landscape is of paramount importance, driving the exploration of advanced techniques for safeguarding confidential information. This study introduces a robust approach that combines advanced encryption standard (AES) encryption with key expansion, genetic algorithms (GA), and least significant bit (LSB) embedding to achieve secure data concealment within digital images. Motivated by the pressing need for enhanced data protection, our work addresses the critical challenge of securing sensitive information from unauthorized access. Specifically, we present a systematic methodology that integrates AES encryption for robust data security, GA for optimization, and LSB embedding for subtle information concealment. Through comprehensive experimentation, involving images such as 'Lena.jpg,' 'Peppers.jpg,' and 'Baboon.jpg,' we demonstrate the efficacy of our approach. The imperceptible modification rates mean squared error (MSE) of 0.199, 0.101, and 0.105, coupled with high peak signal-to-noise ratios (PSNR) of 10.04 dB, 9.95 dB, and 9.79 dB respectively, underscore the fidelity and subtlety of the embedded information. This study contributes to the ongoing discourse on data security by offering a comprehensive and innovative approach that addresses the evolving challenges in safeguarding digital information. [ABSTRACT FROM AUTHOR]

Copyright of Anadolu University Journal of Art & Design / Sanat & Tasarım is the property of Anadolu 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 huge advance of digital communication and networks has led to enormous storage and transmission of information over public networks. Nevertheless, the assurance of information security remains incomplete across these unsecured networks. Currently, digital images are the primary mean for sharing information over open networks. Consequently, the confidentiality of digital images during storage and transmission has become a crucial concern, particularly when sharing sensitive information. Image encryption has emerged as a solution to this problem. This paper presents an image encryption technique based on multiple one-dimensional chaotic maps and DNA coding. The technique employs three one-dimensional chaotic maps, including the logistic map, tent map and piecewise map, multiple times to produce 18 random sequences with different initial values and parameters. SHA-512 hash function is used to indicate the initial values of chaotic maps. For encrypting images, the binary elements from various sequences of chaotic maps are amalgamating to alter the pixel intensities of the image in the diffusion process. Dynamic DNA coding is performed through random selection of DNA rules and operations (XOR, XNOR and Addition) to each pixel in the image. The technique is enforced using circular rotations which are applied randomly to each key. The proposed technique is evaluated using many standard images. Different performance metrics have been measured. The empirical findings illustrate the security and resilience of the suggested method and its ability to resist statistical and differential attacks. [ABSTRACT FROM AUTHOR]

The article presents the results of adapting the digital image correlation method for the possibility of diagnosing reinforced concrete structures. Reinforced concrete (RC) bending elements are the most widely used in construction practice, which determines the importance of reliable estimation of their stress-strain state. The purpose of this study includes reliable theoretical and experimental investigation of the strength and deformability parameters of the RC beam. The experimental study was conducted using digital image correlation and sub-micron contactless gauges. Experimental data was verified with the calculation of the stress-strain state of the RC beam according to DBN V.2.6-98:2009 and Eurocode 2 and the finite-element modelling (FEM). As a result, the values of deflections, concrete and rebar strains were obtained and presented as corresponding diagrams. The results of all the methods are within the same ranges. Also, the form and character of corresponding diagrams are very similar. The indicated deviations were within acceptable limits. It was noted that the theoretical calculation generally provides lower strain values, which is a satisfactory result, as it indicates the bearing capacity reserves provided by the current regulations. The propagation of cracks was monitored during the experiment and the measured cracks opening was compared with theoretical assumptions. Theoretical values are higher than experimental, which shows certain conservativity of valid normative regulations. The experimental and theoretical results were in good correspondence, which confirms their reliability. It was concluded, that the proposed in the study complex theoretic-experimental approach provides essential information about the strength and deformability of the structure. [ABSTRACT FROM AUTHOR]

In Oaxaca, Mexico, there are more than 30 species of the Agave genus, and its cultivation is of great economic and social importance. The incidence of pests, diseases, and environmental stress cause significant losses to the crop. The identification of damage through non-invasive tools based on visual information is important for reducing economic losses. The objective of this study was to evaluate and compare five deep learning models: YOLO versions 7, 7-tiny, and 8, and two from the Detectron2 library, Faster-RCNN and RetinaNet, for the detection and classification of Agave angustifolia plants in digital images. In the town of Santiago Matatlán, Oaxaca, 333 images were taken in an open-air plantation, and 1317 plants were labeled into five classes: sick, yellow, healthy, small, and spotted. Models were trained with a 70% random partition, validated with 10%, and tested with the remaining 20%. The results obtained from the models indicate that YOLOv7 is the best-performing model, in terms of the test set, with a mAP of 0.616, outperforming YOLOv7-tiny and YOLOv8, both with a mAP of 0.606 on the same set; demonstrating that artificial intelligence for the detection and classification of Agave angustifolia plants under planting conditions is feasible using digital images. [ABSTRACT FROM AUTHOR]

Introduction: Diagnosing oral cancer is crucial in healthcare, with technological advancements enhancing early detection and outcomes. This review examines the impact of handheld AI-based tools, focusing on Convolutional Neural Networks (CNNs) and their advanced architectures in oral cancer diagnosis. Methods: A comprehensive search across PubMed, Scopus, Google Scholar, and Web of Science identified papers on deep learning (DL) in oral cancer diagnosis using digital images. The review, registered with PROSPERO, employed PRISMA and QUADAS-2 for search and risk assessment, with data analyzed through bubble and bar charts. Results: Twenty-five papers were reviewed, highlighting classification, segmentation, and object detection as key areas. Despite challenges like limited annotated datasets and data imbalance, models such as DenseNet121, VGG19, and EfficientNet-B0 excelled in binary classification, while EfficientNet-B4, Inception-V4, and Faster R-CNN were effective for multiclass classification and object detection. Models achieved up to 100% precision, 99% specificity, and 97.5% accuracy, showcasing AI's potential to improve diagnostic accuracy. Combining datasets and leveraging transfer learning enhances detection, particularly in resource-limited settings. Conclusion: Handheld AI tools are transforming oral cancer diagnosis, with ethical considerations guiding their integration into healthcare systems. DL offers explainability, builds trust in AI-driven diagnoses, and facilitates telemedicine integration. [ABSTRACT FROM AUTHOR]

The computer network has fundamentally transformed modern interactions, enabling the effortless transmission of multimedia data. However, the openness of these networks necessitates heightened attention to the security and confidentiality of multimedia content. Digital images, being a crucial component of multimedia communications, require robust protection measures, as their security has become a global concern. Traditional color image encryption/decryption algorithms, such as DES, IDEA, and AES, are unsuitable for image encryption due to the diverse storage formats of images, highlighting the urgent need for innovative encryption techniques. Chaos-based cryptosystems have emerged as a prominent research focus due to their properties of randomness, high sensitivity to initial conditions, and unpredictability. These algorithms typically operate in two phases: shuffling and replacement. During the shuffling phase, the positions of the pixels are altered using chaotic sequences or matrix transformations, which are simple to implement and enhance encryption. However, since only the pixel positions are modified and not the pixel values, the encrypted image's histogram remains identical to the original, making it vulnerable to statistical attacks. In the replacement phase, chaotic sequences alter the pixel values. This research introduces a novel encryption technique for color images (RGB type) based on DNA subsequence operations to secure these images, which often contain critical information, from potential cyber-attacks. The suggested method includes two main components: a high-speed permutation process and adaptive diffusion. When implemented in the MATLAB software environment, the approach yielded promising results, such as NPCR values exceeding 98.9% and UACI values at around 32.9%, demonstrating its effectiveness in key cryptographic parameters. Security analyses, including histograms and Chi-square tests, were initially conducted, with passing Chi-square test outcomes for all channels; the correlation coefficient between adjacent pixels was also calculated. Additionally, entropy values were computed, achieving a minimum entropy of 7.0, indicating a high level of randomness. The method was tested on specific images, such as all-black and all-white images, and evaluated for resistance to noise and occlusion attacks. Finally, a comparison of the proposed algorithm's NPCR and UAC values with those of existing methods demonstrated its superior performance and suitability. [ABSTRACT FROM AUTHOR]

Soil roughness, defined as the irregularities of the soil surface, yields significant information about soil water storage, infiltration and overland flow and, thus, is a key factor in characterizing the quality of the terrain; it is often used as input in many synthetic general agricultural models and in particular in soil moisture estimation models. In this paper, we propose a framework that combines a specific setup for data acquisition with deep convolutional networks for actual estimation. The former relies on projecting a line red laser beam on the analysed soil surface followed by digital color image acquisition. The later, involves two convolutional models that are trained in a supervised manner to predict the soil roughness. The data set was produced in the laboratory both on synthetic and real soil samples. The labels used in the training process are the soil roughness values measured by using a pinboard. The detailed evaluation showed that the error of the automatic precision lies in the range of ground truth deviation, thus validating the proposed procedure. [ABSTRACT FROM AUTHOR]

The development of new technologies has made image watermarking crucial in the digital era to preserve and protect illegal distribution of images against unauthorized users. This paper presents a robust image watermarking technique that employs a set of embedding rules in the three-level of integer wavelet transform (IWT). The proposed method aims to achieve high robustness of image watermarking while maintaining the imperceptibility. The proposed scheme divides the red and green layers into non-overlapping 16×16 blocks. Three levels of IWT are applied to obtain 2×2 LL sub-band, four coefficients of IWT are then modified based on the proposed set of rules for embedding watermark. The experimental results demonstrate a comparison of the proposed embedding and the existing methods. The proposed scheme produced an average NC value of 0.965 against the median filter. The results also showed the imperceptibility of the the image with a PSNR of 45.1760 db and SSIM of 0.9995. [ABSTRACT FROM AUTHOR]

The proliferation of queer influencers is framed in various phenomena that connect celebrity culture, digital psychopolitics and historical LGBTIQ+ movements, resulting in what we can call a post-reality. This study analyzes the tensions of bodies/emotions that arise from the visibility of queer influencers in Mexico in terms of representation and subjectivation of the sex/gender system, comparing visual narratives and political positionings or the lack of these in a necropolitical context for the LGBTIQ+ community. A descriptive approach was carried out to have a overview of the diversity of profiles, and subsequently the mediabiography methodology was applied to a case study, with the selection of texts and digital images on an analytical model of three axes: corporality, emotionality and visuality. The presence of Mexican queer influencers reconfigures dominant gender narratives and opens possible scenarios to recognize non-hegemonic corporalities and corporal practices. The content they generate articulates counter-narratives, counter-visualities and counter-emotionalities, such as pride in the face of shame, and anger in the face of the neoliberal depoliticization of pride. Queer Studies guide the enactment of public policies that recognize the rights of sex/gender diversity. Mediabiography opens creative avenues for the study of contemporary visual narratives on social media. Counter-narratives, counter-visualities and counter-emotionalities legitimize the dignified existence of corporalities systematically and historically marginalized by the dominant gender model, and de-hierarchize life. This is the first study that uses mediabiography for the analysis of visual narratives on social media. It contributes to new discussions for Communication Studies, Queer Studies and the Sociology of Bodies and Emotions. [ABSTRACT FROM AUTHOR]

Due to the wide use of digital images in the virtual world and the growing attacks on them, passive image authentication schemes became an urgent and necessary need. The forgery attacks threaten the ownership of the digital image. Copy- move forgery detection is the main type of image forgery detection schemes. In this forgery, at least one region is copied and pasted at same image. In the proposed scheme, we proposed anew serial strategy through combining feature-based detector and block-based detector by using Region growing merging Segmentation method (RGMS).Multi scale Hessian-affine detector as primary detector, Polar cosine transform(PCT) as final detector and Simple linear iterative clustering segmentation(SLIC) with Factor Graph to construct RGMS. We designed serial random fitting model to include the true results and exclude the undesired results. Serial random supports single and multi-cloning regions. Our strategy provides facility to reveal and extract the iterative regions under different constraints. In addition, the proposed scheme is able to detect the slight and invisible cloning regions in the smooth digital image. This scheme is invariant to simple and complex attacks. Furthermore, it invariant to affine transformation likes skewness, distortion and perspective changes. Different datasets are used like GRIP, MICC-F600, MICC-F8.The experimental results confirmed that the proposed scheme has high percentage of True positive rates (99%), and low percentage of false positive rates (1.5%). The proposed scheme can be used as passive image forensic scheme in the cybercrimes and court rooms. [ABSTRACT FROM AUTHOR]

Digital image processing is manipulating and analyzing digital images using computer algorithms to enhance or extract information. Designing a median filter based on QCA offers potential advantages in terms of improved speed and energy efficiency. QCA technology utilizes the unique properties of quantum dots to perform computational tasks, including image processing. This study promotes the use of QCA technology since it is crucial to develop and implement the MF for DIP as it helps reduce noise. The suggested QCA-based MF has a single layer, a few cells, and exceptionally low latency. Comparing the suggested MF's cell consumption and occupied area to its best predecessor reveals 77.67% and 83.50% improvements, respectively. The MF is created using the comparison and selective module (CSM). Finally, each of the suggested architectures is designed and validated using the QCA Designer-E 2.2 simulation and a configuration tool for the Bistable approximation engine. [ABSTRACT FROM AUTHOR]

ABSTRACT The modelling of rock structure is of great significance in characterizing rock characteristics and studying the failure laws of rock samples. In order to construct a high‐fidelity model of the rock structure efficiently, this paper proposes an adaptive mesh dissection algorithm based on the Voronoi structure. Image processing techniques, including greyscale, threshold segmentation and edge detection, are applied to simplify the original rock image into a feature edge image. Then, a probability density diagram of the feature image is generated, which provides a probabilistic basis for the subsequent spreading of mesh seed points. Moreover, the concept of polygonal representation rate and the mesh quality evaluation system of four‐dimensional metrics are established to suggest values for the seed point parameters of the initial mesh. The initial mesh is continuously optimized and iterated by barycentric iteration and gradient descent optimization methods to form mesh structural models with high representational performance efficiently. The model tests on particle, fracture and multi‐phase rock images show that the optimized mesh model is highly similar to the original image in terms of similarity and edge fit, and the algorithm significantly reduces the short‐edge rate and improves the shape regularity of the mesh structure. Finally, numerical tests of uniaxial compression are carried out based on the optimized mesh model. The results show that the model has computational potential in numerical calculations. This method builds a procedural structure from digital images to numerical models, which can provide a reliable model basis for simulating the physico‐mechanical behaviour of heterogeneous rocks. [ABSTRACT FROM AUTHOR]

The majority of the KP-based copy-move forgery (CMF) detection techniques have a high computational cost due to their large feature descriptor sets and numerous KPs. Furthermore, the accuracy of the results may be impacted by the identified KPs not spreading over all regions of the image and the classical clustering approaches not efficiently classifying the feature space into cluster space. This article therefore attempts to employ both KAZE & fast hessian matrix (FHM) techniques for identifying KPs that spread over the entire image region, SURF for evaluating feature descriptors, Network based Dimensionality Reduction (NDR) for reducing the dimension of each feature descriptor and Self-Organizing Map (SOM) for clustering the feature vectors for avoiding sub-optimal clusters. It portrays the superior performances of the proposed forgery detection scheme on a standard image database “MICC-F220” and medical records like fundas images. [ABSTRACT FROM AUTHOR]

The proliferation of fake images generated by deepfake techniques has significantly threatened the trustworthiness of digital information, leading to a pressing need for face forgery detection. However, due to the similarity between human face images and the subtlety of artefact information, most deep face forgery detection methods face certain challenges, such as incomplete extraction of artefact information, limited performance in detecting low-quality forgeries, and insufficient generalization across different datasets. To address these issues, this paper proposes a novel noise-aware multi-scale deepfake detection model. Firstly, a progressive spatial attention module is introduced, which learns two types of spatial feature weights: boosting weight and suppression weight. The boosting weight highlights salient regions, while the suppression weight enables the model to capture more subtle artifact information. Through multiple boosting-suppression stages, the proposed model progressively focuses on different facial regions and extracts multi-scale RGB features. Additionally, a noise-aware two-stream network is introduced, which leverages frequency-domain features and fuses image noise with multi-scale RGB features. This integration enhances the model's ability to handle image post-processing. Furthermore, the model learns global features from multi-modal features through multiple convolutional layers, which are combined with local similarity features for deepfake detection, thereby improving the model's robustness. Experimental results on several benchmark databases demonstrate the superiority of our proposed method over state-of-the-art techniques. Our contributions lie in the progressive spatial attention module, which effectively addresses overfitting in CNNs, and the integration of noise-aware features and multi-scale RGB features. These innovations lead to enhanced accuracy and generalization performance in face forgery detection. [ABSTRACT FROM AUTHOR]

Non-fungible tokens (NFTs), which are immutable and transferable tokens on blockchain networks, have been used to certify the ownership of digital images often grouped in collections. Depending on individual interests, wallets explore and purchase NFTs in one or more image collections. Among many potential factors of shaping purchase trajectories, this paper specifically examines how visual similarities between collections affect wallets' explorations. Our model characterizes each wallet's explorations with a Lévy flight and shows that wallets tend to favor collections having similar visual features to their previous purchases while their behaviors vary widely. The model also predicts the extent to which the next collection is close to the most recent collection of purchases with respect to visual features. These results are expected to enhance and support recommendation systems for the NFT market. [ABSTRACT FROM AUTHOR]

This research work presents a novel secured spatial steganography algorithm consisting of three stages. In the first stage, a secret message is divided into three parts, each is encrypted using a tan logistic map encryption key with a unique seed value. In the second stage, the encrypted parts are transformed into quick response codes, serving as a layer of channel coding. Subsequently, the quick response codes are decoded back into bit‐streams. To enhance security, a uniquely‐seeded Mersenne Twister key is generated and employed to apply DNA coding onto each bit‐stream. The resulting bit‐streams are then embedded in the least significant bits of the RGB channels of a cover image. Finally, the RGB channels are merged to form a single stego image. A comprehensive set of experimental analyses is conducted to evaluate the performance of the proposed secure steganography algorithm. The experimental results demonstrate the algorithm's robustness against various attacks and its ability to achieve high embedding capacity while maintaining imperceptibility. The proposed algorithm offers a promising solution for secure information hiding in the spatial domain, with potential applications in areas such as data transmission, digital forensics, and covert communication. [ABSTRACT FROM AUTHOR]

Image hiding is a task that embeds secret images in digital images without being detected. The performance of image hiding has been greatly improved by using the invertible neural network. However, current image hiding methods are less robust in the face of Joint Photographic Experts Group (JPEG) compression. The secret image cannot be extracted from the stego image after JPEG compression of the stego image. Some methods show good robustness for some certain JPEG compression quality factors but poor robustness for other common JPEG compression quality factors. An image‐hiding network (RIHINNet) that is robust to all common JPEG compression quality factors is proposed. First of all, the loss function is redesigned; thus, the secret image is hidden as much as possible in the area that is less likely to be changed after JPEG compression. Second, the classifier is designed, which can help the model to select the extractor according to the range of JPEG compression degree. Finally, the interval robustness of the secret image extraction is improved through the design of a denoising module. Experimental results show that this RIHINNet outperforms other state‐of‐the‐art image‐hiding methods in the face of JPEG compressed noise with random compression quality factors, with more than 10 dB peak signal‐to‐noise ratio improvement in secret image recovery on ImageNet, COCO and DIV2K datasets. [ABSTRACT FROM AUTHOR]