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The rapid development of information and communication technology provides an opportunity for scientists to develop a quantitative analytical method that is built on a mobile gadget as a detector. In this paper, we report a low-cost, simple and portable analytical method for mercury ion quantification based on digital image colorimetry coupled with a smartphone application. A small amount of silver nanoparticles (AgNPs) was used as a colorimetric agent that is selective only to mercury ions. The yellowish brown color of AgNPs instantly changed to colorless after the addition of mercury ions due to a redox reaction. To increase the portability, we attached the AgNPs onto a medium to create a paper-based analytical device. The final data processing of the colorimetric analysis was conducted using an android application available on the Google Play Store, called “Mercury Detector”. The proposed method has good sensitivity, with a detection limit of 0.86 ppb, which is comparable to those of bulk and more expensive instruments. This allows for the detection of mercury ions as low as 2 ppb (10 nM), which is also the maximum contaminant level permitted in drinking water by the US Environmental Protection Agency. The proposed method was applied to real samples that provide satisfactory results on accuracy (2.4%) and precision (2.5%).

Abstract Cronobacter spp. are recognized as a world-wide emerging opportunistic food-borne pathogens that can persist in various food and processing environment for a long time. Therefore, the prevention and detection of them are particularly important. In this study, a micro-spot paper-based analytical device (μPADs) was created by combining PVC pad with filter paper. Detection is achieved by measuring the color change (from colorless to indigo) when a species-specific enzyme associated with the Cronobacter spp. of interest reacts with a chromogenic substrate. When combined with the optimization of specific enrichment process, the method allows for a testing time of 10 h or less and is capable of detecting live bacteria on the inoculated surface of samples in concentrations as low as 101 CFU cm−2. We are surprised to discover that C. dublinensis species and their subspecies had the highest ability to produce α-glucosidase in all genus of Cronobacter spp.. This work demonstrated that the manufacturing method is novel, simple, well reproducible (RSD less than 5%) and low-cost (less than $ 0.15 per micro-spot). Graphical abstract Image 1 Highlights • The fabrication of μPADs using PVC pad is described for the first time, which is simple and low-cost (less than $ 0.15). • The colorimetric detection of Cronobacter spp. using μPADs for the first time. • We found that C. dublinensis and its subspecies had the highest ability to produce α-glucosidase. • The LOD and RSD (n = 6) are 101 CFU cm−2 and less than 5%. In addition, the detection time is about 10 h. [ABSTRACT FROM AUTHOR]

This paper describes the development and application of a paper-based analytical device (μPAD) for the determination of hydrogen peroxide, an important adulterant in milk. The method employs the reaction between hydrogen peroxide and guaiacol, catalyzed by peroxidase, producing a red product, which is then quantified by digital imaging. Experimental design methodology was used to optimize the experimental conditions. The linear concentration range was from 12.5 × 10−4 to 150 × 10−4 mol L−1, resulting in the regression equation A B = 0.02466 (±0.00192) + 17.053 (±0.750) C, with an excellent correlation coefficient (r = 0.986). The relative standard deviations obtained were 1.1 and 1.3% (intra-day), and 4.8 and 2.9% (inter-day), for 25.0 × 10−4 and 100 × 10−4 mol L−1 of hydrogen peroxide, respectively. The limits of detection and quantification were 3.54 × 10−4 and 11.8 × 10−4 mol L−1, respectively, with standard deviation of the blank of 0.002012. The proposed method was successfully applied for the determination of peroxide in milk samples, with recoveries between 92.2 and 109%. The proposed device constitutes a valuable analytical tool for the identification of hydrogen peroxide adulteration and offers advantages including low cost, simplicity, portability, and no (or minimal) requirement for sample pretreatment. Unlabelled Image • Bioactive paper platform as solid support for H 2 O 2 analysis using digital image • Determination of hydrogen peroxide in adulterated milk • The proposed method represents an advantageous alternative to most reported methods. • It was developed of a simple, low-cost, portable, and eco-friendly method. [ABSTRACT FROM AUTHOR]

[Display omitted] • For the first time a test strip is developed to determine Cu2+ in cachaças. • Precise, simple and fast qualitative and quantitative methods. • The methods enable producers to make an adequate control of Cu2+ levels in beverages. • The proposal is cheap and can be easily disseminated in the productive environment. In this work we propose for the first time, a paper-based test strip to analyse Cu2+ content in sugar cane spirits, which, due to its simplicity, high portability and fast analytical response (3 min), can be easily applied to in situ analyses by producers. The test strip was developed aiming: i) identify qualitatively the Cu2+ content in sugar cane spirits, and, ii) determine quantitatively the Cu2+ content using a digital image method employing a smartphone. The paper-based test strip was functionalized with cuprizone and optimized through a Box-Behnken, an experimental design for obtaining the best reaction conditions. Based on qualitative method with naked eyes approach performed by six volunteers analyst untrained, the method present a percentage of accuracy of 93%. For the quantitative analysis, it was determined the metal content at a level of statistical agreement with the reference method, as well as it was obtained the dynamic linear range from 2 to 13 mg L−1 with limits of detection and quantification of 0.034 and 0.103 mg L−1, respectively. Furthermore, the quantitative method showed a reliable precision with an RSD of 4.3% (n = 10) and the recovery of Cu2+ ranged from 80 to 103.8%. [ABSTRACT FROM AUTHOR]

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

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

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

The most extensive data storage size is significantly increasing, and storing such data, including images to make them obtainable over the network, has become a significant problem. Thus, efficient compression techniques are necessary because the original images need much disk space. Generally, the primary purpose of image compression is to decrease the amount of data required for representing digital images. Image compression techniques can be categorized into lossless and lossy. Rounding Intensity followed by the Dividing Technique (FIRD) is a known lossy technique utilized in the proposed method that aims to reduce the range of the intensities and increase redundancy, achieving better compression performance. The main idea of this paper is to separate the (8*8 block of mage) into two parts: the matrix of hundreds and the single matrix. The elements in the hundreds matrix will be divided into 10, and the resulted matrix will be compressed using the Huffman technique. The elements in the single matrix will be rounded as follow: 0,1,2,3, and 4 to 2, and the elements 5, 6, 7, 8, and 9 are rounded to 7. Then, the Huffman technique will be applied to the resulting matrix. The previously generated values are combined, and the initial results indicate a reduction in distortion with better compression performance. This paper deals with whether the proposed technique enhancing the quality of Compressed Images using Rounding Intensity Followed by the Dividing Technique (E-RFID) can be used as a distinctive option in the distortion reduction process image a high compression ratio. In addition, image compression technology and quality enhancement with high compression explain each step's work, which will be presented in detail. The research factors have been positively impacted by improving the results and comparing them through MSE, PSNR, and MAE. The E-RIFD technique experiments showed an improved rate in MSE 66.82% and PSNR 18.31%, while the MAE improvement rate was 21.09% for grayscale images. However, when testing color images, the results show that the rate of improvement in MSE was 66.59% and PSNR 15.28, while the improvement rate in the MAE was 18.64%. [ABSTRACT FROM AUTHOR]

The paper firstly establishes the so-called n-times iterated connected sum of a simple closed k-surface in Z 3 , denoted by C k n , k ∈ { 6 , 18 , 26 } . Secondly, for a simple closed 18-surface M S S 18 , we prove that there are only two types of connected sums of it up to 18-isomorphism. Besides, given a simple closed 6-surface M S S 6 , we prove that only one type of M S S 6 ♯ M S S 6 exists up to 6-isomorphism, where ♯ means the digital connected sum operator. Thirdly, we prove the digital k-contractibility of C k n : = M S S k ♯ ⋯ ♯ M S S k ⏞ n − times , k ∈ { 18 , 26 } , which leads to the simply k-connectedness of C k n , k ∈ { 18 , 26 } , n ∈ N . Fourthly, we prove that C 6 2 and C k n do not have the almost fixed point property (AFPP, for short), k ∈ { 18 , 26 } . Finally, assume a closed k-surface S k (⊂ Z 3) which is (k , k ¯) -isomorphic to (X , k) in the picture (Z 3 , k , k ¯ , X) and the set X is symmetric according to each of x y -, y z -, and x z -planes of R 3 . Then we prove that S k does not have the AFPP. In this paper given a digital image (X , k) is assumed to be k-connected and its cardinality | X | ≥ 2 . [ABSTRACT FROM AUTHOR]

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

The rapid development of information and communication technology provides an opportunity for scientists to develop a quantitative analytical method that is built on a mobile gadget as a detector. In this paper, we report a low-cost, simple and portable analytical method for mercury ion quantification based on digital image colorimetry coupled with a smartphone application. A small amount of silver nanoparticles (AgNPs) was used as a colorimetric agent that is selective only to mercury ions. The yellowish brown color of AgNPs instantly changed to colorless after the addition of mercury ions due to a redox reaction. To increase the portability, we attached the AgNPs onto a medium to create a paper-based analytical device. The final data processing of the colorimetric analysis was conducted using an android application available on the Google Play Store, called "Mercury Detector". The proposed method has good sensitivity, with a detection limit of 0.86 ppb, which is comparable to those of bulk and more expensive instruments. This allows for the detection of mercury ions as low as 2 ppb (10 nM), which is also the maximum contaminant level permitted in drinking water by the US Environmental Protection Agency. The proposed method was applied to real samples that provide satisfactory results on accuracy (2.4%) and precision (2.5%). [ABSTRACT FROM AUTHOR]

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

The present paper investigates digital topological properties of an alignment of fixed point sets which can play an important role in fixed point theory from the viewpoints of computational or digital topology. In digital topology-based fixed point theory, for a digital image (X , k) , let F (X) be the set of cardinalities of the fixed point sets of all k-continuous self-maps of (X , k) (see Definition 4). In this paper we call it an alignment of fixed point sets of (X , k) . Then we have the following unsolved problem. How many components are there in F (X) up to 2-connectedness? In particular, let C k n , l be a simple closed k-curve with l elements in Z n and X : = C k n , l 1 ∨ C k n , l 2 be a digital wedge of C k n , l 1 and C k n , l 2 in Z n . Then we need to explore both the number of components of F (X) up to digital 2-connectivity (see Definition 4) and perfectness of F (X) (see Definition 5). The present paper addresses these issues and, furthermore, solves several problems related to the main issues. Indeed, it turns out that the three models C 2 n n , 4 , C 3 n − 1 n , 4 , and C k n , 6 play important roles in studying these topics because the digital fundamental groups of them have strong relationships with alignments of fixed point sets of them. Moreover, we correct some errors stated by Boxer et al. in their recent work and improve them (see Remark 3). This approach can facilitate the studies of pure and applied topologies, digital geometry, mathematical morphology, and image processing and image classification in computer science. The present paper only deals with k-connected spaces in DTC. Moreover, we will mainly deal with a set X such that X ♯ ≥ 2 . [ABSTRACT FROM AUTHOR]

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

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

This paper discusses a feasible software solution that enables automatic marking and grading of scripts. Technology keeps expanding, and more advanced innovations are being implemented with time. The marking and allocation of grades for examination scripts through human efforts are gradually becoming a thing of the past. Hence, machines and software applications are introduced to make the entire marking and grading of examination scripts more efficient, fast, and less tedious. Computer vision is an artificial intelligence (AI) knowledge domain that ensures devices obtain useful information from digital images, videos, and other visual inputs. Image processing and recognition, a unique part of computer vision alongside the python programming language and the OpenCV library was employed for this project. These are the most used in developing most recent applications that utilize, to some extent, artificial intelligence to attain specific desired results. The result of the project seeks to develop a maintainable android software application that uses image processing technology to scan patterns or images and grades results of multiple-choice question scripts based on a set marking scheme. This ensures that desired results are obtained while increasing efficiency and productivity. [ABSTRACT FROM AUTHOR]

In research, there is a growing interest in using artificial intelligence to find solutions to difficult scientific problems. In this paper, a deep learning algorithm has been applied using images of samples of materials used for road surfaces. The photographs showed cross-sections of random samples taken with a CT scanner. Historical samples were used for the analysis, located in a database collecting information over many years. The deep learning analysis was performed using some elements of the VGG16 network architecture and implemented using the R language. The learning and training data were augmented and cross-validated. This resulted in the high level of 96.4% quality identification of the sample type and its selected structural features. The photographs in the identification set were correctly identified in terms of structure, mix type and grain size. The trained model identified samples in the domain of the dataset used for training in a very good way. As a result, in the future such a methodology may facilitate the identification of the type of mixture, its basic properties and defects. [ABSTRACT FROM AUTHOR]

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

In order to more accurately realize the automatic identification and quantitative characterization of coral reef multi-scale pore structure, this paper has carried out such research. On the basis of using multiple information acquisition technology, it can effectively collect field measured data and indoor observation data to form a high-precision digital image of the development characteristics of multi-scale coral reef pore structure. By constructing the multi-scale structure information correlation and fusion function of coral reef pore structure, it can formulate the positioning and search strategy for the key areas of coral reef pore structure development characteristics. In this paper, a fine identification and quantitative characterization method suitable for the pore structure of "millimeter–micron–nanometer" is formed. At the same time, combined with the actual project requirements, the multi-scale pore structure feature area location method of coral reef is constructed, which combines the distribution characteristics, variation characteristics, fractal characteristics and shape characteristics in a certain range, and realizes the search of pore key areas between different scales and different regions of the same scale through the similarity matching algorithm, which can effectively realize the automatic search of key areas in the "millimeter–micron–nanometer"-scale image of coral reefs. Finally, the correctness and feasibility of this method are verified by multi-angle example data comparison. It shows that this method can effectively solve the productivity prediction and evaluation problem of coral reef oil and gas reservoirs. [ABSTRACT FROM AUTHOR]

Digital image security and rectitude are the top major issues in today's informative world. Water marking is a specific method that is used for copyright protection and firm verified. Watermark should be strong and gradual. Digital image watermarking is an embedding data such as an image converting into digital signal using a strong media QR code. It is very difficult to analyse the digital image watermarking has applied to data. This is a competent way to prevent illegal copyright from the modern multimedia network with modern technology in printing and scanning, creating imitate documents which has become a difficult task that makes it very complicated to understand between actual and artificial. So, it is a very big challenge for serving security or digital data. This paper put forward an idea or proclamation to provide and innovative method to verify a digital document. The advanced method of QR code is brought here which permits us to embed a digital data such as an image in QR code. In this paper we bring up and innovative method where the image is encoded in QR that is code in encoded method. If a hacker tried to modify or meddle with the data then he/she will fail to do that so. This is caused because of the encryption key to which the hacker is not aware of it. It is unfeasible to scan or access to the encrypted QR code as the codes are robust. [ABSTRACT FROM AUTHOR]

Copyright of Iraqi 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.)