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ABSTRACTAbove-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially limited improvements in crop productivity. Recently, a deep learning technique called convolutional neural network (CNN) has been applied to estimate crop AGB due to its high capacity for digital image recognition. However, the versatility of the CNN-based AGB estimation for diverse cultivars is still unclear. We established and evaluated a CNN-based estimation method for rice AGB using digital images with 59 diverse cultivars which were mostly in World Rice Core Collection. Across two years at two locations, we took 12,183 images of 59 cultivars with commercial digital cameras and manually obtained their corresponding AGB. The CNN model was established by using 28 cultivars and showed high accuracy (R2 = 0.95) to the test dataset. We further evaluated the performance of the CNN model by using 31 cultivars, which were not in the model establishment. The CNN model successfully estimated AGB when the observed AGB was lesser than 924 g m−2 (R2 = 0.87), whereas it underestimated AGB when the observed AGB was greater than 924 g m−2 (R2 = 0.02). This underestimation might be improved by adding training data with a greater AGB in further study. The present study indicates that this CNN-based estimation method is highly versatile and could be a practical tool for monitoring crop AGB in diverse cultivars.

The paper presents studies of the color separation system based on the developed color triangle for conducting scientific research in microscopy which will allow identifying genetic or chemical deviations of the samples under study by an accurate change in color. The color triangle covers the entire visible range and is focused on the physiological RGB and XYZ colorimetric systems. Based on the method of converting color spaces, the addition curves of the developed systems were found. Based on the curves, sets of color separating light filters were selected to fit the shapes of these curves based on the selected monochrome camera. Three sets are presented. An analytical study of these sets was carried out and one optimal set was selected. An analytical study of this system is presented in the form of mathematical modeling with 14 control colors from the Munsell atlas. The selected set of the system was experimentally studied on the developed optoelectronic setup placed in a black box to exclude light and color flare. One important part of the setup is the reflective screen: the location follows the lighting/observation recommendations of the International Commission on Illumination for colorimetric measurement of samples. For an objective analysis of measurements, reference test objects were selected — standardized colored optical glasses. The study was based on the evaluation of glass groups: yellow, yellow-green, green, blue-green, since the work has expanded the color space in the direction of the selected colors to obtain color accuracy. Previously, the author, in an analytical study of modern color separation systems, obtained results where the best value was found with a wide color triangle of 0.009, the worst 0.04 — with a small one. Thus, it has been proven: the larger the coverage of the color triangle, the smaller the change in color. The obtained values of the developed KZS system are better than modern ones –0.0088 on average. During the mathematical modeling of the experiment, the change in color was obtained 0.016 on average, the practical result — 0.027 on average. The obtained parameters and characteristics will be taken into account when introducing the developed color separation system into a monochrome digital microscope to improve color rendering in microscopy.

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 Introduction: Artificial Intelligence (AI) is a tool that is already part of our reality, and this is an opportunity to understand how it can be useful in interacting with patients and providing valuable information about orthodontics. Objective: This study evaluated the accuracy of ChatGPT in providing accurate and quality information to answer questions on Clear aligners, Temporary anchorage devices and Digital imaging in orthodontics. Methods: forty-five questions and answers were generated by the ChatGPT 4.0, and analyzed separately by five orthodontists. The evaluators independently rated the quality of information provided on a Likert scale, in which higher scores indicated greater quality of information (1 = very poor; 2 = poor; 3 = acceptable; 4 = good; 5 = very good). The Kruskal-Wallis H test (p< 0.05) and post-hoc pairwise comparisons with the Bonferroni correction were performed. Results: From the 225 evaluations of the five different evaluators, 11 (4.9%) were considered as very poor, 4 (1.8%) as poor, and 15 (6.7%) as acceptable. The majority were considered as good [34 (15,1%)] and very good [161 (71.6%)]. Regarding evaluators’ scores, a slight agreement was perceived, with Fleiss’s Kappa equal to 0.004. Conclusions: ChatGPT has proven effective in providing quality answers related to clear aligners, temporary anchorage devices, and digital imaging within the context of interest of orthodontics.

Background. Since the beginning of the 21st century, the world system of financial, economic and economic relations has been objectively evolving towards the formation of a post-industrial integral cyber-formation production method. This serves as a basis for talking about the onset of the era of the eponymous Integral Cyber Formation Society. The complexity of the tasks to be solved by the state and society in the course of the upcoming changes requires new approaches to organizing industry, including the use of cyber-social industrial ecosystems and mechanisms for sustainable ESGC development. The purpose of the study is to develop in the system paradigm the conceptual foundations of the mechanism of sustainable ESGC-development of the cluster-type cybersocial industrial ecosystem, as well as the justification of the need for multidimensional system modeling of this mechanism for the design of its relevant digital image. Materials and methods. The study was conducted in a system paradigm based on system theory and a system approach. To study the causes of historical evolutionary changes in economic and economic mechanisms, general scientific methods of analysis and synthesis, and system-organizational analysis tools were used. During the analysis of the actual material and to present the results, the method of structural and logical modeling and visualization of the conceptual foundations of economic and economic systems was used. Results. The main results are: a model of the organizational mechanism of the cybersocial industrial cluster, which determines the conceptual essence of the multidimensional system modeling of the mechanism of sustainable ESGC-development of the cybersocial industrial ecosystem of the cluster type; the concept of a new, engineering approach to the design and modeling of cluster-type cybersocial industrial ecosystems based on the theoretical and methodological basis of the system paradigm. Conclusions. The proposed conceptual foundations of multidimensional system modeling of the mechanism of sustainable ESGC-development of a cluster-type cybersocial industrial ecosystem will contribute to the development of the digital industry economy and increase its efficiency.

The development of an economical and simple colorimetric system based on a smartphone camera and image processing was included in this study. This method was applied to determine three azo dyes namely: Methyl Blue (M.B), Methyl Red (M.R) and Methyl Orange (M.O) using the smartphone's camera as a detector. The results of the radar diagrams were giving a good agreement with the results of the calibration curves which were built using data of RGB for each dye. For establishing the accuracy and precision of this method, a classical method (spectrophotometric) was used for validation. This advancement in smartphone-based detection and identification systems will revolutionize environmental monitoring, ensuring rapid and effective diagnosis of contaminants for individuals and communities. Streamlining the Digital image colorimetry DIC process on smartphones is essential to public health and safety while promoting more conscious and sustainable practices worldwide.

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering 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 application of unmanned aerial vehicle (UAV) remote sensing technology for high-throughput acquisition of phenotypic values of field plant height is of great significance in plant height monitoring of wheat varieties (lines). Based on the UAV, mounted with high-resolution digital cameras, a low-altitude remote sensing platform was built to obtain images of the Berkut/Worrakatta recombinant inbred lines (RIL) wheat population (297 lines). The optimal digital surface model (DSM) for plant height extraction was constructed by combining the actual wheat plant heights measured in the field. Using a wheat 50K SNP chip, quantitative trait locus (QTL) analysis was performed for field plant height of the RIL population using UAV plant height data to detect the locus related to plant height. We verified the effectiveness of QTL analysis using the UAV plant height data. The results of the DSM plant height extraction method showed that the plant height extraction models during the entire growth period had a highly significant correlation (p < 0.001); the measured plant height correlated with the predicted plant height, with prediction model accuracy R2 = 0.7527 and verification model accuracy R2 = 0.8214. QTL analysis was conducted on the manually and UAV-measured plant height traits. For the manually measured plant height phenotypes, one locus related to plant height was detected on wheat chromosome 6A and explained 13.12% of phenotypic variation. For the UAV-measured phenotypes, one locus related to plant height was also detected on wheat chromosome 6A and explained 9.62% of phenotypic variation. The QPH.xjau-6A locus on chromosome 6A, which featured in the results of the two measurement methods, is a stable locus, indicating that the mapping results obtained using the actual plant height values were in good agreement with those obtained using the UAV extracted values. Three candidate genes related to plant height were screened: they encode protein kinase, NAC domain protein, and cytochrome P450, respectively. Therefore, this study provided reference information for monitoring plant phenotype and growth of wheat and also for the extraction of plant height for wheat breeding in the future. [ABSTRACT FROM AUTHOR]

The digital immaturity of the technological machines service support sector is established, which is explained by an information failure in the formation of a databank of production and structural, organizational, and technological processes. It is established that direct application of the developed Industry 4.0 technologies to the service support sector of transport and technological machines is almost impossible. An analysis of the significance of repair and recovery activities for transport and technological machines in terms of maintaining their performance and resource optimization is presented. A term is introduced, and the description of the concept "digital image of the repair and recovery technology" (DIRRT) is given in the context of a single service center/enterprise. The DIRRT is characterized by stepwise construction of an element-by-element predictive–probabilistic model of failure of individual parts with allowance for changing load characteristics during operation and creating a grid of the predictive maintenance requirement throughout the service life of the technological machine as a whole. An algorithm is developed for involving the digital module into the organizational production and technical infrastructure of the service center when optimizing repair and recovery of transport and technological machines. [ABSTRACT FROM AUTHOR]

Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation. [ABSTRACT FROM AUTHOR]

Freezing sets and cold sets have been introduced as part of the theory of fixed points in digital topology. In this paper, we introduce a generalization of these notions, the limiting set, and examine properties of limiting sets. [ABSTRACT FROM AUTHOR]

Today, the issue of protecting information from unauthorized access is one of the key issues in the field of information technology. As digital technologies continue to spread into all areas of human activity and methods of protecting information from unwanted access are improving, the development of methods and technologies for obtaining unauthorized access to confidential information for malicious purposes does not stop either. Therefore, it is important to continue to improve information security systems, thereby hindering intruders. An integral part of any modern information security system is a steganographic system that provides a hidden data transmission channel that allows you to transmit information without revealing the fact of its existence to an attacker. The task of choosing a steganographic container allows you to solve some of the requirements for a steganographic system when building it. One of the most important requirements is to ensure the steganographic system's resistance to attacks against the embedded message, since such attacks do not require extensive knowledge of steganography and steganalysis from the attacker and do not require special technical means, which makes this type of attack simple and widespread. The aim of the work is to improve the efficiency of the steganosystem by modifying the method of selecting a container from the given set, developed by the author earlier, which will ensure the maximum possible, or at least close to the maximum, resistance of the steganomessage to attack against the embedded message for the analyzed images. This goal was achieved by modifying the quantitative assessment of the amount of protected information, namely, using the relative value of the amount of protected information to the amount of all embedded information. The result of the work is the development of a modification of the method for selecting a steganographic container, which is ready for practical application. The effectiveness of the proposed method is higher than the prototype method, and remains high regardless of which steganomethod or perturbation was used. The significance of the result is to increase the overall resistance of the steganosystem to attacks against the embedded message by using this method to select the steganocontainer. [ABSTRACT FROM AUTHOR]

In environmental supervision, it is necessary to measure waste piles volume to determine whether the activities of the waste manager comply with the established requirements. The aim of this research is to determine whether the model, formed from images collected with low-priced unmanned aerial vehicle (UAV) - not with Real Time Kinematic Global Navigation Satellite System (RTK GNSS) capability - is sufficiently accurate to carry out waste-related surveying. Data collection took place in spring 2021 at the Aardlapalu transhipment station in Tartu County. The objects of the research were an unscreened composting pile and a covered composting pile. In the fieldwork, terrestrial laser scanning and photogrammetric flight were carried out. The reference value was the volume of the model formed from the data of laser scan. The volumes of all models formed by the photogrammetric method were within the permissible difference of 10% provided by law. The most accurate results were obtained from the covered composting pile with an overlap of 70% × 70% and 21 ground control points (GCPs). Using these parameters, the absolute error of the model was 1.48 m? and the relative error was 0.65%. The most inaccurate results were obtained from the unscreened composting pile with an overlap of 80% × 80% and 21 GCP-s. The research confirmed the hypothesis that sufficient accuracy to calculate waste piles volumes can also be achieved by using a cheaper UAV and camera and with software not specially designed for photogrammetry, design, and drawing. [ABSTRACT FROM AUTHOR]

Stenographic methods are closely related to the security and confidentiality of communications, which have always been essential domains of human life. Steganography itself is a science dedicated to the process of hiding information in public communication channels. Its main idea is to use digital files or even communication protocols as a medium inside which data are hidden. The present research aims to investigate the applicability of ant algorithms in steganography and evaluate the effectiveness of this approach. Ant systems could be employed both in spatial and frequency-based image steganography. The combination of frequency domain and optimization method to increase robustness is used, and an integer wavelet transform is performed on the host image. ACO optimization is used to find the optimal coefficients describing where to hide the data. The other method utilizes ACO to determine the optimal pixel locations for embedding secret data in the cover image. ACO is also used to detect complex regions of the cover image. Afterward, the least-significant-bits (LSB) substitution is used to hide secret information in the detected complex regions' pixels. Our study focuses on optimizing two mutually exclusive features of steganograms—high capacity and low distortion. An attempt was made to use ant systems to select areas of digital images that allow the greatest amount of information to be hidden with the least loss of image quality. The effect of variants of the ant system and its parameters on the quality of the results obtained was also investigated, and the final effectiveness of the proposed method was evaluated. The results of the experiments were compared with those published in related articles. The proposed procedures proved to be effective and allowed the embedding of large amounts of data with relatively little impact on image quality. [ABSTRACT FROM AUTHOR]

Nowadays, advanced medical imaging is a widely accepted scientific discipline in the healthcare industry due to technological advances and software breakthroughs. Traditionally using digital images of X-Ray for diagnosing diseases in healthcare is very common. Many researchers have proposed different methods like X-Ray, CT-scan, and MRI for bone implantation. But the problem is while implantation it does not get the actual size as original, so it causes a bad impact on the health and the person suffers from pain. Therefore, to solve this problem, the proposed system uses a canny edge detection that can sketch the edges of knee bone present in an x-ray image with the size of the bone for implantation by using a virtual reality technique. The system will convert the 2D image to 3D and then the 3D model will be shown in a VR headset. So, we can get a clear visualization of the image. This model provides accurate measurements and detailed visualization of bones in virtual reality with 95.58% accuracy. Virtual reality-based visualization using X-ray images is more effective than CT-Scan in improving radiologists' accuracy and efficiency in diagnosing certain medical conditions, leading to a preference for X-ray images in certain diagnostic scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Petrochemical Universities / Shiyou Huagong Gaodeng Xuexiao Xuebao is the property of Journal Editorial Department Of Liaoning Shihua 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.)

A feasible analytical method aimed at the quality control of red alcohol vinegar is introduced, with no need for dilutions, filtration, or expensive instrumentation to perform the analysis. The remarkable benefit is no need to add chemicals. Thirty sample solutions were prepared to aim to produce vinegar with good quality following a 22 central composite design (CCD), with a central point (625:3) and axial points (- 2 ; 2) . The poor taste and quality parameters were also tested with sample contents far from the central point (1.5-fold). Data from square images converted into color histograms of red (R), green (G), and blue (B), hue (H), saturation (S), and value or intensity (V) or (I), relative colors of RGB denoted as r, b, and g, and luminosity (L) was used for the calculations of multivariate classification models, such as k-nearest neighbors (kNN), soft independent modeling of class analogy (SIMCA), and partial least squares–discriminant analysis (PLS-DA), and the figures of merit showed that the reliability of the multivariate classification models was higher than 0.9 for accuracy, sensitivity, and specificity. Afterward, a low-cost and easy-to-use analytical method can aid in identifying operational errors and end-product out-of-specifications. [ABSTRACT FROM AUTHOR]

The need for image/video storage and transmission is growing dramatically in the current era of multimedia for purposes such as imaging, online meetings, and video streaming, etc. As a result of technological advancements, there has been a steady increase in communication channel network capacity. As a result of new advanced technology and increased capacity of existing ones, bandwidth requirement is increasing exponentially.Many of the current initiatives in the field of data compression are described by it. The objective of these endeavors is to propose new methods for encoding information sources like audio, images, and video in a manner that reduces the number of bits needed to represent the source content without noticeably compromising the quality. There is a necessity of the new methods that works by reducing the source data without significantly limiting the quality of the source. This is the main intension of these works. In the recent, there has been a significant increase in image compression research, which corresponds with a noteworthy rise in the generation of digital data in the form of images. The objective is to preserve the vital information contained in an image while representing it in the fewest possible bits. The three major types of information that images provide are irrelevant, redundant, and useful. Within the field of image information, there is a kind of data called deterministic redundant information that can be recovered with no loss by utilizing other data that is contained in the image. On the other hand, data that is excessively detailed and goes beyond the limits of perceptual significance is categorized as irrelevant data. Conversely, the part of the material that is neither redundant nor superfluous is the valuable information. The human visual system's limitations and capabilities provide the basis for how the ordinary person interprets decompressed images. This survey provides a comprehensive overview of current lossless and lossy image coding techniques. The methods are divided into many groups, and a taxonomy of techniques is shown. The different compression techniques are compared in this study. The compression algorithm performance evaluation parameters are presented. The main issues that were discovered during the current research are then reviewed, and potential directions for the future are presented. [ABSTRACT FROM AUTHOR]

Copyright of Al-Mustansiriyah Journal of Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is O (l o g (M × N)) , M and N being the height and width of the image. Efficient implementations of this parallel algorithm would allow images to be processed in real time, as well as permit us to uncover fast algorithms for contour detection and segmentation, opening new perspectives within the image processing field. [ABSTRACT FROM AUTHOR]

Art, always permeable to advances and social changes, quickly found in computer technology and the Internet a field of experimentation. Since the sudden development of social networks at the beginning of this century, we are witnessing the foray of the artistic image into a new scenario of an essentially interactive nature. In this research, we intend to delve into how the artistic image flows and is transformed in the space of social networks, paying attention to two issues that we understand to be closely associated with its contemporary condition: posttruth and the problem of the saturated self. Our aim is to highlight the way in which the latest art adapts to and converges with the digital medium through the networks, not only taking advantage of its resources, but also assimilating its modes, its language, and its aesthetics. Being a significant topical subject, we believe that it is entirely relevant to investigate this territory from the perspective of artistic thought, determined by the continuous development of new mobile applications and the constant reinvention of the networks. To this end, we will analyze the artistic proposals of a selection of contemporary creators whose work is located in social networks, emphasizing the creative possibilities offered by the screen, especially those related to sharing, interaction, and exchange. The final conclusion will show how art can interfere with the development social networks by behaving as a disruptive agent, enhancing the critical gaze and introducing alternative ideas. [ABSTRACT FROM AUTHOR]

The objective of this study was to develop a low-cost method for rice growth information obtained quickly using digital images taken with smartphone. A new canopy parameter, namely, the canopy volume parameter (CVP), was proposed and developed for rice using the leaf area index (LAI) and plant height (PH). Among these parameters, the CVP was selected as an optimal parameter to characterize rice yields during the growth period. Rice canopy images were acquired with a smartphone. Image feature parameters were extracted, including the canopy cover (CC) and numerous vegetation indices (VIs), before and after image segmentation. A rice CVP prediction model in which the CC and VIs served as independent variables was established using a random forest (RF) regression algorithm. The results revealed the following. The CVP was better than the LAI and PH for predicting the final yield. And a CVP prediction model constructed according to a local modelling method for distinguishing different types of rice varieties was the most accurate (coefficient of determination (R2) = 0.92; root mean square error (RMSE) = 0.44). These findings indicate that digital images can be used to track the growth of crops over time and provide technical support for estimating rice yields.

In this study, the Digital Image-Based Colorimetric Detection Method developed by Bakırdere et al. was used to find the TAC (Total Antioxidant Capacity) value of tea samples from different brands. To determine the total amount of antioxidants in tea samples, the CUPRAC (cupric ion reducing antioxidant capacity) method, which is widely used in antioxidant determination, was combined with a digital image-based colorimetric detection system. To use in our study, a box with opaque wood material measuring 24 cm x 19 cm x 17 cm (width/length/depth) was designed and manufactured. In the analysis, the oxidation reaction between the chromogenic copper(II)-neocuproine (Cu(II)-Nc) reagent and antioxidants was utilized. The color change that occurs as a result of the oxidation was calculated using an application on smartphones. In our study, analyzes were performed on 4 different brand tea extract samples (tea A, tea B, tea C, tea D) to determine the total antioxidant capacity of ascorbic acid equivalent. The TAC values for ascorbic acid equivalent in tea extract samples were found as 380 ± 8 mg/L (tea A), 402 ± 4 mg/L (tea B), 213 ± 3 mg/L (tea C), 232 ± 4 mg/L (tea D) using the digital image-based colorimetric detection systems.

Virus infection in chili plants may cause various symptoms. The complexity of the symptoms and human vision ability often become limiting factors during disease investigations. Digital image analysis is expected to become a method to assist in comprehensively describing the symptoms of plant viruses. A disease survey was conducted on cayenne pepper fields in Southeast Sulawesi Province to observe symptoms of virus infection virtually and to record the symptomatic plant using an RGB camera. The split-channel method is used to process images, followed by multidimensional scaling statistical analysis. Later on, viruses associated with plants were detected serologically. Single or mixed infection of Tobacco mosaic virus, Cucumber mosaic virus, Chili veinal mottle virus, and Pepper mottle virus was confirmed by plant leaves showing yellow-mosaic and mottle symptoms. The digital image analysis method could show variations in the characteristics of symptoms based on digital numbers in that cannot be recognized based on the observation of visual symptoms. A new approach to study the interactions between plant infecting viruses and their effects based on image analysis has also been developed during this research. This method needs to be further validated through testing under controlled conditions, such as inoculating plants with a predetermined type of virus.

The article deals with a new phenomenon for the legal science – the institute of legal regulation of the genesis of digital identity.The subject of the research is the relations that emerge in the process of such regulation. The aim of the research is to analyze the legal regulation of the institute of digital identity at the international, regional and national levels, including in the Russian Federation.The methodological basis of the research is the logical and systematic methods of scientific knowledge, as well as methods of analysis and synthesis, formal-dogmatic and comparative-legal methods.International, foreign and domestic experience in the genesis of digital identity is studied. The features of legal regulation of each of the elements of digital identity – digital profile, digital image, digital footprint – are analyzed. At the same time, the legal regulation of the relevant relations is considered in their dynamics. As a result of the study, conclusions were made about the peculiarities of international legal regulation of relations related to the genesis of a legal identity, the legal regulation of these relations in the European Union and China. It is noted that international legal regulation of relations related to the genesis of legal personality is carried out, as a rule, only in connection with the need to protect human rights and freedoms in the digital age, including in virtual space; in the European Union the legal regulation is mainly subject to the digital profile of the person, based on his personal data; at the level of individual states the most holistic and systemic regulation of relations related to digital identity is carried out in the People's Republic of China on the Special attention is given to the problems of legal regulation of the genesis of digital identity in the Russian Federation. It is noted that the problems of legal regulation of the genesis of digital identity are becoming particularly relevant at the present time. Its theoretical comprehension is a prerequisite for improving current legislation and law enforcement practice in the Russian Federation.It is pointed out that there are significant gaps in the legal regulation of relations related to the digital footprint in Russia, the most regulated relations in the area of digital profile and digital image; further lack of legal regulation of the collection and processing of information that constitutes the content of the digital identity footprint in Russia will lead to a massive violation of individual rights and freedoms.The study also found that the international legal regulation of relations related to the genesis of the legal personality is usually carried out only in connection with the need to protect human rights and freedoms in the digital age, including in virtual space, and in the European Union the legal regulation is mainly subject to the digital profile of the person, which is based on his personal data.

The use of nondestructive techniques that can guarantee in situ monitoring of the quality of food products during drying processes can promote improvements in industrial processes. In this context, digital imaging and near infrared spectroscopy can play an important role. Red beet moisture content, betalain content, shrinkage, and color were measured during drying. The digital images showed changes in color, which intensified with increasing temperature and correlated with betalain content through multivariate linear regression (R2 of 0.90). In addition, sample shrinkage was identified by digital images. The volumetric shrinkage increased proportionally to the drying air temperature, with a shrinkage of 85.4 ± 0.3% at 70 °C. Spectral data were obtained by a low-cost portable near infrared spectrometer. Partial least square regression was used to predict the product moisture content ( R P 2 = 0.89 ; RMSEP = 0.79 %) and total betalain content ( R P 2 = 0.79 ; RMSEP = 0.30 m g g d. b.) during drying. These tools can be used for the in situ control of red beet drying. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially limited improvements in crop productivity. Recently, a deep learning technique called convolutional neural network (CNN) has been applied to estimate crop AGB due to its high capacity for digital image recognition. However, the versatility of the CNN-based AGB estimation for diverse cultivars is still unclear. We established and evaluated a CNN-based estimation method for rice AGB using digital images with 59 diverse cultivars which were mostly in World Rice Core Collection. Across two years at two locations, we took 12,183 images of 59 cultivars with commercial digital cameras and manually obtained their corresponding AGB. The CNN model was established by using 28 cultivars and showed high accuracy (R2 = 0.95) to the test dataset. We further evaluated the performance of the CNN model by using 31 cultivars, which were not in the model establishment. The CNN model successfully estimated AGB when the observed AGB was lesser than 924 g m−2 (R2 = 0.87), whereas it underestimated AGB when the observed AGB was greater than 924 g m−2 (R2 = 0.02). This underestimation might be improved by adding training data with a greater AGB in further study. The present study indicates that this CNN-based estimation method is highly versatile and could be a practical tool for monitoring crop AGB in diverse cultivars. [ABSTRACT FROM AUTHOR]

The advancement of smartphones that produce high-resolution photos has made their use even more developed. One application that is currently developing is a digital image produced as colorimetry. Digital image-based colorimetry is an easy and simple determination method and can be an alternative for analyzing dyes quantitatively. In this method, the color response of the digital image is analyzed, namely red-green-blue (RGB) with values from 0 to 255. This data is extracted from digital images using ImageJ 1.52a software for quantitative determination. In this study, 2 standard methods were used, namely external standards and standard addition to evaluating the most suitable method to obtain accurate measurements. In addition, 2 methods were used to determine the relationship of response to methylene blue levels. The 1st method used simple linear regression of each color red, green, or blue (univariate). The 2nd method is used in multivariate linear regression of the 3 colors red, green, and blue. The accuracy of the method was validated using UV-Vis spectrophotometry as the reference method. The results showed that the multivariate models produced very good accuracy, namely precision of up to 99 % compared to the simple regression method with the univariate. [ABSTRACT FROM AUTHOR]

Objectives: Representative of a panel, an average face image could be used to analyse/display skin changes while alleviating image rights constraints. Therefore, we used landmark‐based deformation (warping) of individual skin images onto their panel's average face, evaluating this approach's relevance and possible limits. Methods: An average front face image was constructed from images of 71 Japanese women (50–60 years old). After warping individual skin images onto this average face, the resulting skin‐warped average faces were presented to three experts who graded: forehead wrinkles, nasolabial fold, wrinkle of the corner of the lips, pore visibility and skin pigmentation homogeneity. Two experts estimated subjects' age. Results were compared to gradings performed on original images. Results: Inter‐expert grading shows excellent to good correlation whatever image type: from 0.918 (forehead wrinkles) to 0.693 (visibility of pores). Correlations between scoring of both image types are almost always higher than inter‐expert correlations (maximum: 0.939 for forehead wrinkles—minimum: 0.677 for pore visibility). Frequencies of grades/ages are similar when scoring original and skin‐warped average face images. Experts scores are similar in 90.6%–99.3% of the cases. Average deviations upon scoring both image types are smaller than average inter‐expert deviations on original images. Conclusions: Scoring facial characteristics in original images and skin‐warped average face images show an excellent agreement, even for perceived age, a complex feature. This opens the possibility of using this approach to grade facial skin features, monitor changes over time, and to valorise results on a face deprived of image rights. [ABSTRACT FROM AUTHOR]

This interview with media anthropologist, Rebecca L. Stein, conducted by Noa Levin and Andrew Fisher in Spring 2023, takes her recent book Screenshots: State Violence on Camera in Israel and Palestine (2021) as its starting point in order to explore issues of state violence and the militarization of social media in Israel/Palestine. This book marks the culmination of a decade-long research project into the camera dreams introduced by digital imaging technologies and the fraught histories of their disillusionment. Stein discusses the way her research has critically conceptualized the recent history of hopes invested in the digital image in this geopolitical context, by the occupier as much as the occupied, and charts the failures and mistakes, obstructions and appropriations that characterize the conflicted visual cultures of Israel/Palestine. [ABSTRACT FROM AUTHOR]

In the recent trends and technology we found that the digital image plays an important role. The digital images are widely used in various fields. The validation is now becomes a challenging task because of advancement in the tools and technology. The various tools are used to modify the digital image to create fake digital images. The several research has been carried out in the field of image forgery detection. In this paper attempted to review the existing technique to identify the forgery in an image. Accordingly related papers are reviewed and analyzed. The active and passive are the two approaches in the image forgery detection technique. The digital signature and digital watermarking are the two methods in active approach, and in passive approach various techniques are there such as copy-move, splicing, resampling etc., In this paper we describe the comparative analysis of different types of image forgery detection technique along with future work to be carried out. [ABSTRACT FROM AUTHOR]

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Sardinella lemuru, one of the important pelagic fish in Bali Strait waters, has been reported to be in the overfishing condition. Various studies have been conducted to understand its population condition. Length-weight data has been also widely used to understand the population dynamic of S. lemuru, although data collection generally using manual direct measurement methods, which has the disadvantages if used on the enormous number of samples. Therefore, this research aims to understand the significance of S. lemuru length measurement using digital image analysis (ImageJ) compared with the manual measurement. Moreover, this research also aims to understand the population condition of S. lemuru landed in PPI Kedonganan analyzed using its length-weight data. The result indicated no significance difference between length measurement using manual methods and digital image methods, thus conclude that digital image data can be used to measure the fish length and represent the accurate measurement. Population condition analysis showed that S. lemuru population is in isometric condition (b=3); with indication of declining in the length growth pattern compared with previous research (L8= 20.75 cm with K= 1.1); the mortality estimation showed that fishing mortality (F=1.27) was lower than natural mortality (M=2.22), with the survival rate higher on smaller size fish. This research result can be used as an addition to the S. lemuru fisheries data and can also be used as reference methods to help conduct sampling and data collection more efficiently. [ABSTRACT FROM AUTHOR]

Measurement of ammonia in exhaled breath is of interest in medical and health sciences. The fluorometric determination of NH3 in exhaled breath condensate (EBC) was developed based on a digital image of a windowless falling drop (WFD) cell at the end of tube via a sensitive and selective reaction of NH3-OPA-sulfite in flow injection analysis system (FIA). A low-cost USB microscope as a facile instrumentation without expensive optical parts, an amplifier circuit, and a data acquisition unit, was used as a fluorescence light detector for the WFD cell via instant reporting temporal profile of digital value. A linear calibration graph of NH4+ in the range of 0.5–10 µmol L−1 can be obtained with a limit of detection of 0.5 µmol L−1 NH4+, and repeatability of 5.5% RSD (n = 11). The fluorescence (FL) signal obtained from the low-cost USB microscope was not different from that using the photodiode detector via a cross-shape flow cell. The proposed device was successfully applied for NH4+ determination in EBC and air absorbing solution samples with the recovery of 98–110%. The approach for instant reporting of a temporal profile of digital image value from a camera shows a potential to be applied for rapid chemical analysis in other microfluidics systems.

Abstract Cytopathological examination plays a crucial role in cancer diagnosis as it reflects the cellular pathology of cancer. However, this process traditionally relies on the visual examination by cytopathologists. Recent advancements in computer and digital imaging technologies have enabled the application of artificial intelligence (AI)‐based models to identify tumor cells in images, thereby assisting cytopathologists in achieving enhanced performance. AI‐based models can improve the accuracy and reproducibility of image evaluation and streamline clinical workflows. Moreover, AI‐based models can analyze a diverse range of sample types, including peripheral blood, urine, ascites, and bone marrow. AI‐based cytopathological recognition can help clinicians screen and diagnose cancer, predict prognosis and recurrence of cancers, such as leukemia, cervical cancer, urothelial carcinoma, and gastric cancer. Additionally, AI‐based models can predict the types of mutations in leukemia. A growing number of studies emphasize the potential of computational image analysis and deep learning‐based AI to build novel diagnostic tools that are conducive to the biomedical field. This review describes the recent developments in AI‐based cytopathological recognition and offers a perspective on how AI tools of cytopathology can help improve cancer diagnosis and prognosis prediction. Future developments in AI model applications can further contribute to the improvement of human health.

Underwater images suffer from absorption and scattering of light, so underwater images are blurry, while contrast, clarity, and lighting are low. To improve the quality of underwater images, a method based on the new metaheuristic algorithm, the CHIO algorithm, was proposed. In our work, we first read the images and convert the color system from RGB to HSV. Subsequently, apply the CHIO algorithm to the image, and finally convert the color system from HSV to RGB. Experiments on the standard benchmark dataset for underwater image optimization proved the effectiveness of the method, while the performance of our algorithm is better than that of the standard optimization algorithms.

The complex mechanical characteristics of the Xiyu conglomerate significantly influence the resistance and deformation features of its caverns’ surrounding rock, thereby constraining the construction of related water diversion tunnels. This paper introduces an improved SegFormer framework developed for the detection of mesoscale geomaterial structures. Computerized tomography (CT) scan images of the Xiyu conglomerate were employed to establish a high-precision numerical model. From the results of segmentation, the proposed algorithm outperformed UNet, HRNet, and the original SegFormer neural network. The segmentation results were used to calculate the porosity, and biaxial compression numerical simulation experiments based on the real structure were carried out using the particle flow code (PFC). We observed the failure process of the model and obtained the shear strength of the Xiyu conglomerate. We explored the causes and influencing factors of the anisotropy of the Xiyu conglomerate from the microstructure perspective and provide a micro-observation basis for establishing an anisotropic mechanical model.