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A turn-on fluorescent aptasensor based on a paper-based microfluidic chip was developed to detect arsenite via aptamer competition strategy and smartphone imaging. The chip was prepared by wax-printing hydrophilic channels on filter paper. It is portable, low-cost, and environmentally friendly. Double-stranded DNA consisting of aptamer and fluorescence-labeled complementary strands was immobilized on the reaction zone of the paper chip. Due to the specific strong binding between aptamer and arsenite, the fluorescent complementary strand was squeezed out and driven by capillary force to the detection area of the paper chip, so that the fluorescent signal arose in the detection area under the excitation wavelength of 488 nm. Arsenite can be quantified by using smartphone imaging and RGB image analysis. Under the optimal conditions, the paper-based microfluidic aptasensor exhibited excellent linear response over a wide range of 1 to 1000 nM, with a detection limit as low as 0.96 nM (3σ). [ABSTRACT FROM AUTHOR]

A user-friendly, cost-effectively, portable, and environmentally friendly colorimetric sensor for the quantitative determination of formaldehyde was developed based on the combining of microfluidic paper-based analytical device (μPAD), headspace microextraction (HSME), and digital image colorimetry. Coupling HSME and μPAD led to enhancements in selectivity and sensitivity of the sensor through sample cleanup and analyte enrichment. To construct the μPAD-HSME device, two pieces of paper as the sample and detection zone were placed facing each other so that a small common and sealed space was created between them. The color change occurred when the analyte in the gaseous form crossed this gap and reached the detection zone. Colorimetric sensing in the detection zone was performed based on the Hantzsch reaction. The color change in the detection zone was recorded by a smartphone and digital images were processed using image analysis software based on the RGB model. The influence of some key variables on the sensitivity of the method including derivatization reagent composition, sample volume, extraction temperature, and extraction time was studied and optimized. The linear dynamic range of the method was obtained in two ranges of 0.10–0.75 and 0.75–5.0 mg L−1 with a limit of detection of 0.03 mg L−1. The recoveries were in the range 80–126% for the quantification of formaldehyde in textile, milk, and wastewater samples. [ABSTRACT FROM AUTHOR]

Medieval paper, a handmade product, is made with a mould which leaves an indelible imprint on the sheet of paper. This imprint includes chain lines, laid lines and watermarks which are often visible on the sheet. Extracting these features allows the identification of the paper stock and gives information about the chronology, localisation and movement of manuscripts and people. Most computational work for feature extraction of paper analysis has so far focused on radiography or transmitted light images. While these imaging methods provide clear visualisation of the features of interest, they are expensive and time consuming in their acquisition and not feasible for smaller institutions. However, reflected light images of medieval paper manuscripts are abundant and possibly cheaper in their acquisition. In this paper, we propose algorithms to detect and extract the laid and chain lines from reflected light images. We tackle the main drawback of reflected light images, that is, the low contrast attenuation of chain and laid lines and intensity jumps due to noise and degradation, by employing the spectral total variation decomposition and develop methods for subsequent chain and laid line extraction. Our results clearly demonstrate the feasibility of using reflected light images in paper analysis. This work enables feature extraction for paper manuscripts that have otherwise not been analysed due to a lack of appropriate images. We also open the door for paper stock identification at scale. [ABSTRACT FROM AUTHOR]

Computer vision is one of the artificial intelligence's most challenging fields, enabling computers to interpret, analyse and derive meaningful information from the visual world. There are various utilizations of computer vision algorithms, and most of them, from simpler to more complicated, have an object and shape recognition in common. Traditional pen and paper tests are designed in a pre-established format and consist of numerous basic shapes, which designate the important parts of the test itself. With that in mind, many computer vision applications regarding pen and paper tests arise as an opportunity. Massive courses and large schooling organizations mostly conduct their exams in paper format and assess them manually, which imposes a significant burden on the teaching staff. Any kind of automatization that will facilitate the grading process is highly desirable. Hence, an automated answer recognition system in assessment was developed to mitigate the problems above. The system uses images of scanned test pages obtained from the test scanning process and performs the necessary image manipulation steps to increase target recognition accuracy. Further, it manages to identify regions of interest containing multiple-choice questions and contours. Finally, the system verifies obtained results using the knowledge of the whereabouts of the test template regions of interest. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate a simple and low-cost colorimetric method based on Fe(III) chloride and potassium hexacyanoferrate(III) for determination of acetaminophen, ascorbic acid and phenylephrine, which can be used in small laboratories using restricted materials and resources. A paper based sensor using the reagent was introduced. Significance of different variables in determination of drugs were studied by Placket-Burman design and optimal values for concentration of hydrochloric acid, Fe(III) chloride and potassium hexacyano ferrate(III), 0.01 M, 0.004 M and 0.004 M, respectively were obtained. Without using any sophisticated instrument and only by image analysis the concentration of acetaminophen, ascorbic acid and phenylephrine was determined in pharmaceutical samples. In this way we were able to determine by analyzing the final color of the reactions. The linear range in ppm for all the analytes was obtained as 0.00–50.00 and limit of detection of 2.52, 2.48 and 2.48 were obtained for acetaminophen, ascorbic acid and phenylephrine, respectively. Percent relative standard deviation (RSD %) and percent relative error (RE %) of the proposed method for analysis of real samples of acetaminophen tablet were 4.77 and 7.64, ascorbic acid tablet, 4.73 and 1.41, and phenylephrine syrup, 2.44 and − 1.61, respectively. [ABSTRACT FROM AUTHOR]

Development of paper-based microfluidic devices that perform colorimetric measurements requires quantitative image analysis. Because the design geometries of paper-based microfluidic devices are not standardized, conventional methods for performing batch measurements of regularly spaced areas of signal intensity, such as those for well plates, cannot be used to quantify signal from most of these devices. To streamline the device development process, we have developed an open-source program called ColorScan that can automatically recognize and measure signal-containing zones from images of devices, regardless of output zone geometry or spatial arrangement. This program, which measures color intensity with the same accuracy as standard manual approaches, can rapidly process scanned device images, simultaneously measure identified output zones, and effectively manage measurement results to eliminate requirements for time-consuming and user-dependent image processing procedures. [ABSTRACT FROM AUTHOR]

Tissue furnish optimization plays a key role in enhancing tissue properties, making the process cost-effective. Typically, this furnish is composed of a mixture of hardwood eucalyptus fibers (HW) and softwood (SW) fibers, which ensure strength and tissue machine runnability. However, the tissue paper production with the maximization of eucalyptus fibers achieves softer papers at less cost, since SW fibers are often more expensive than HW fibers. From this perspective, this study aims to investigate the effect of micro/nano-fibrillated cellulose (MFC/NFC) as an additive, on structural, softness, strength, and water absorption properties of tissue papers, promoting partial or total removal of SW fibers to produce 100% eucalyptus materials. MFC/NFC was characterized in terms of morphological, chemical, and water interaction properties. The results showed that MFC/NFC presents a high bonding surface area, high carboxyl group content and, when incorporated into tissue furnishes, it promotes strong inter-fiber bonds. This evidence was also supported by SEM image analysis methods and FTIR. Additionally, laboratory tissue handsheets with low basis weight were produced and used in the characterization assays. Overall, the results indicated that MFC/NFC improved strength, at the expense of bulk, porosity, softness, and absorption properties. Compared to typical industrial furnish mixtures (75%HW + 25%SW), MFC/NFC enhanced the production of bulkier, porous, and softer structures, but with reduced strength and absorption. It was possible to optimize the furnish composition by using fiber modeling to obtain 3D structure computation simulations with predictive capability. The MFC/NFC proved to be a high-quality additive to improve softness and strength properties. [ABSTRACT FROM AUTHOR]

A novel method combining headspace single-drop microextraction with a paper-based colorimetric assay was developed. Headspace single-drop microextraction using a microdrop containing unmodified gold nanoparticles (AuNPs) as both the extractant and the colorimetric probe was used for the sensitive and selective determination of Se(IV). The method relies on the color change of the microdrop solution caused by the adsorption of in situ-generated hydrogen selenide on the surface of AuNPs. Following extraction, the microdrop was spotted onto cellulose paper, and scanometric-assisted digital image analysis was used for selenium quantification. The analytical variables affecting the method sensitivity, including the drop volume, the concentrations of KBH4, HCl, and AuNP solutions, and the extraction time, were studied. Under the optimal conditions, a linear correlation between the colorimetric signal and Se(IV) concentration in the range from 15–100 μg L−1 with a limit of quantification of 12 μg L−1 was achieved. The repeatability of the method was studied by the calculation of intraday and interday precision for the standard solutions at concentrations of 20 and 70 μg L-1. The batch-to-batch reproducibility of the AuNPs synthesized under the same conditions was also assessed. The relative standard deviations were less than 7%. The method provided satisfactory results for the determination of selenium in real samples. [ABSTRACT FROM AUTHOR]

This paper aims to provide a review of the basis for application of AI in radiology, to discuss the immediate ethical and professional impact in radiology, and to consider possible future evolution. Even if AI does add significant value to image interpretation, there are implications outside the traditional radiology activities of lesion detection and characterisation. In radiomics, AI can foster the analysis of the features and help in the correlation with other omics data. Imaging biobanks would become a necessary infrastructure to organise and share the image data from which AI models can be trained. AI can be used as an optimising tool to assist the technologist and radiologist in choosing a personalised patient's protocol, tracking the patient's dose parameters, providing an estimate of the radiation risks. AI can also aid the reporting workflow and help the linking between words, images, and quantitative data. Finally, AI coupled with CDS can improve the decision process and thereby optimise clinical and radiological workflow. [ABSTRACT FROM AUTHOR]

The variability and interdependence of local porosity and local mean geodesic tortuosity, which is a measure for the sinuosity of shortest transportation paths, is investigated at the example of the microstructure in sack paper. By means of statistical image analysis, these two morphological characteristics are computed for several cutouts of 3D image data obtained by X-ray microcomputed tomography. Considering cutouts of different sizes allows us to study the influence of the sample size on the local variability of the considered characteristics. Moreover, the interdependence between local porosity and local mean geodesic tortuosity is quantified by modeling their joint distribution parametrically using Archimedean copulas. It turns out that the family of Gumbel copulas is an appropriate model type, which is formally validated by a goodness of fit test. Besides mean geodesic tortuosity, we consider further related morphological characteristics, describing the sinuosity of those shortest transportation paths, whose minimum diameter exceeds a predefined threshold. Moreover, we show that the copula approach investigated in this paper can also be used to quantify the negative correlation between local porosity and these modified versions of local mean geodesic tortuosity. Our results elucidate the impact of local porosity on various kinds of morphological characteristics, which are not experimentally accessible and which are important for local air permeance – a key property of sack paper. [ABSTRACT FROM AUTHOR]

Because the human eye cannot visually detect the results of direct bilirubin test papers accurately and quantitatively, this study proposes four different highly collimated mini light-emitting diodes (HC mini-LEDs) as light sources for detection. First, different concentrations of bilirubin were oxidized to biliverdin by FeCl3 on the test paper, and pictures were obtained with a smartphone. Next, the red, green, and blue (RGB) channels of the pictures were separated to average grayscale values, and their linear relationship with the direct bilirubin concentration was analyzed to detect bilirubin on the test paper noninvasively and quantitatively. The experimental results showed that when green HC mini-LEDs were used as the light sources and image analysis was performed using the G channel, for a direct bilirubin concentration range of 0.1–2 mg/dL, the G channel determination coefficient (R2) reached 0.9523 and limit of detection was 0.459 mg/dL. The detection method proposed herein has advantages such as rapid analysis, noninvasive detection, and digitization according to RGB grayscale changes in the images of the detection test paper. [ABSTRACT FROM AUTHOR]

In this study, uncoated paper was characterized. Three-dimensional structure of the layer was reconstructed using imaging results of micro-CT scanning with a relatively high resolution $$(0.9~\upmu \hbox {m})$$ . Image analysis provided the pore space of the layer, which was used to determine its porosity and pore size distribution. Representative elementary volume (REV) size was determined by calculating values of porosity and permeability values for varying domain sizes. We found that those values remained unchanged for domain sizes of $$400\times 400\times 150\,\upmu \hbox {m}^{3}$$ and larger; this was chosen as the REV size. The determined REV size was verified by determining capillary pressure-saturation imbibition curves for various domain sizes. We studied the directional dependence of curves by simulating water penetration into the layer from various directions. We did not find any significant difference between curves in different directions. We studied the effect of compression of paper on curves. We found that up to 30% compression of the paper layer had very small effect on the curve. Relative permeability as a function of saturation was also calculated. Water penetration into paper was visualized using confocal laser scanning microscopy. Dynamic visualization of water flow in the paper showed that water moves along the fibers first and then fills the pores between them. [ABSTRACT FROM AUTHOR]

The authors describe a nitrocellulose membrane based paper matrix array for detection of mercury(II) with high throughput and repeatability. A thymine-rich signal reporting ssDNA probe labeled with gold nanoparticles (AuNPs) was designed, and detection is based on the strong T-Hg-T interaction which leads to a significant color change. In addition, catalytic signal amplification is accomplished by silver staining in order to enhance coloration. The method has a detection limit as low 0.5 ppt. This is at least 500-fold better than that of existing methods. By using an array, multiple sets of duplicate detections can be carried out simultaneously and in parallel. This improves the efficiency and accuracy of the assay. The method shows remarkable specificity since the signal of other competing metal ions is negligible compared to that of mercury(II) even at 100-fold higher concentration. The protocol was successfully applied to the determination of Hg(II) in spiked tap water and lake water samples. In our perception, this assay is a most promising tool for optical determination f ultra-low levels of Hg(II). [ABSTRACT FROM AUTHOR]

This paper introduces a diagnostic model that effectively diagnoses in fourteen different stages, by fusing functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) information. Georgios Theodoridis and Athanasios Tsadiras from the Aristotle University of Thessaloniki, Greece, have authored the seventh paper which is entitled "Applying machine learning techniques to predict and explain subscriber churn of an online drug information platform." Machine learning (ML) and more specifically deep learning (DL) algorithms are considered among the most paramount technologies of both artificial intelligence (AI) and 4 SP th sp industrial revolution. This paper provides an in-depth comparison of various machine learning (ML) techniques and advanced preprocessing methods, in an effort to successfully perform online subscriber churn prediction. [Extracted from the article]

With the continuous progress of science and technology, image processing techniques have been used increasingly in recent years. Image processing plays an indispensable role in the fields of computer vision, artificial intelligence, pattern recognition, and related fields. Improvements in basic algorithms and the development of new algorithms have resulted in considerable innovation and progress. This paper is devoted to finding new game applications in a branch of image processing. It introduces an analysis model proposed by the author and discusses the relationship between roughness in the frequency domain and visual image interpretation. By using the concept of roughness, we separated the image features into meaningful information and residual information and analysed the image in the frequency domain. The results were compared with those of traditional image processing methods. The starting point is the visual identification of a feature based on human interpretation. The image information was separated into meaningful features and the residual component to reduce the redundancy of the model. This allowed for a sparse representation of the feature information in the image. By analysing the meaningful features and residual components of an image separately, we established a relationship between the results and the original images. Parameters such as texture, morphology, and the degree of blurring were considered and we developed a parameter called "frequency roughness". The algorithm incorporates the concepts of frequency and roughness and the roughness is determined in the frequency domain. The frequency roughness algorithm successfully separated the rough features in the frequency domain and calculated the residual value in an image. This model provided more accurate image processing results than comparable methods. This paper includes an analysis and game applications of the proposed model for de-blurring, image enhancement, recognition, and other image processing tasks. Some game applications were successful, whereas others require further investigation. [ABSTRACT FROM AUTHOR]

Urine test paper is a standard, noninvasive detection method for direct bilirubin, but this method can only achieve qualitative analysis and cannot achieve quantitative analysis. This study used Mini-LEDs as the light source, and direct bilirubin was oxidized to biliverdin by an enzymatic method with ferric chloride (FeCl3) for labeling. Images were captured with a smartphone and evaluated for red (R), green (G), and blue (B) colors to analyze the linear relationship between the spectral change of the test paper image and the direct bilirubin concentration. This method achieved noninvasive detection of bilirubin. The experimental results demonstrated that Mini-LEDs can be used as the light source to analyze the grayscale value of the image RGB. For the direct bilirubin concentration range of 0.1–2 mg/dL, the green channel had the highest coefficient of determination coefficient (R2) of 0.9313 and a limit of detection of 0.56 mg/dL. With this method, direct bilirubin concentrations higher than 1.86 mg/dL can be quantitatively analyzed with the advantage of rapid and noninvasive detection. [ABSTRACT FROM AUTHOR]

Modern μ -CT scans offer non-destructive three-dimensional microstructure analysis of various materials. Despite small density contrasts, this technology also accurately captures the complex network structure of paper and paperboard. It provides detailed insight into the fiber orientation distribution in paper, which was previously unattainable with existing measurement methods. The image analysis results are applicable for numerical fiber network models, and simulations based on these models can significantly improve our understanding of the microstructural influence on macrostructural paper properties, such as strength, stiffness, and curl tendency. This work presents a new method for investigating the microstructural fiber orientation in paperboard. Orientation tensors obtained from μ -CT scan image processing are extracted and evaluated. Based on the direction of the orientation tensor's first eigenvector, the fiber orientation distribution is determined and then approximated by periodic and non-periodic probability density functions (PDFs). In this study, 40 commercial paperboard samples, each consisting of three plies, were analyzed. From a given set of commonly used PDFs, the best fitting ones have been identified based on their original location in the paper roll and on their ply. It was found that the von Mises PDF provided the most accurate representation of fiber orientation distribution in the middle ply, while the Elliptical PDF was the most suitable for the outer plies. Moreover, a more pronounced fiber orientation anisotropy was observed at the edges of the paper roll than in its center. The best PDFs and their function parameters are provided to allow for direct usage in numerical microstructure models of paperboard, thus enhancing their representativeness. [ABSTRACT FROM AUTHOR]

Pore structure characterisation of paper, using automated image analysis methods, has previously been performed in two-dimensional images. Three dimensional (3D) images have become available and thereby new representations and corresponding measurements are needed for 3D pore structure characterisation. In this article, we present a new pore structure representation, the individual pore-based skeleton, and new quantitative measurements for individual pores in 3D, such as surface area, orientation, anisotropy, and size distributions. We also present measurements for network relations, like tortuosity and connectivity. The data used to illustrate the pore structure representations and corresponding measurements are high resolution X-ray microtomography volume images of a layered duplex board imaged at the European Synchrotron Radiation Facility (ESRF). Quantification of the pore structure is exemplified and the results show that differences in pore structure between the layers in the cardboard can be characterised using the presented methods. [ABSTRACT FROM AUTHOR]

The causes of foxing, a rust-red spotting of engravings, books and archive documents, are not yet completely understood, but they are usually ascribed to mould growth and/or heavy-metal-induced degradation of cellulose and sizing materials. In the present work we report the use of attenuated total reflection Fourier transform infrared spectrometry, image analysis and atomic force microscopy as non-destructive tools for the surface analysis of foxing stains in respect of their chemical and physical characteristics. [ABSTRACT FROM AUTHOR]

The deep sea has abundant resources and is regarded as the "sixth continent" that humans can use. Although research on the deep sea has made some progress, its exploration is not deep enough. The acquisition and analysis of deep-sea images can provide important technical means for deep-sea exploration and the acquisition of information. However, there is currently no complete underwater image dataset. For this reason, this paper is the first deep-sea visual dataset of the South China Sea, contains over 100 TB videos and 1 million high quality images. This paper also applies the dataset to underwater image enhancement and image superresolution, and the experiment proves the effectiveness of the dataset. [ABSTRACT FROM AUTHOR]

The main scientific results of the 16th International Conference on Pattern Recognition and Information Processing (PRIP-2023), Minsk, Republic of Belarus, October 2023, are reviewed and analyzed. The history of this series of conferences is outlined, and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue is provided, prepared from reports selected by the PRIP-2023 Program Committee. [ABSTRACT FROM AUTHOR]

The article presents abstracts on medical topics including the effect of increasing posterior tibial slope on anterior cruciate ligament strain, the influence of pre-operative laxity on anterior cruciate ligament reconstruction outcomes, and the quantitative detection of the pivot shift phenomenon by simple image analysis.

An edge-cloud computing collaborative dust concentration detection architecture is proposed for real-time operation of intelligent algorithms to reduce the warning delay. And, an end-to-end three-channel convolutional neural network (E2E-SCNN) method is proposed in the paper to facilitate intelligent monitoring and management of dust concentration in tobacco production workshops. This model, which includes three sub-networks-a local feature branch, a global feature branch, and a spatial feature branch, learns the detail texture, overall layout, and spatial distribution information of the input image respectively. A fusion of the three complementary features is performed at the end of the network for the final dust concentration regression prediction. The design, when compared with the single network structure that directly regresses the entire image, is shown to more fully represent the overall information of the image and enhance monitoring performance. A richly annotated image dataset of tobacco production workshops is constructed to verify the effectiveness of the proposed method. The prediction error of E2E-SCNN is compared with existing image estimation algorithms, dual-channel networks, and other methods on this dataset using indicators such as Mean Absolute Error (MAE) and R 2 . It is shown by the results that excellent performance is achieved by the E2E-SCNN algorithm, significantly surpassing other comparison methods. The paper demonstrates that the accuracy and robustness of dust concentration prediction can be greatly improved by using a three-channel convolutional neural network spatial information monitoring framework. This achievement provides an effective means for dust supervision and governance during the tobacco production process and offers a technical route that can be referred to for image analysis tasks in other similar fields. [ABSTRACT FROM AUTHOR]

A disposable paper-based bipolar electrochemical biosensor is reported for determination of glucose. The closed bipolar electrochemical cell is fabricated on a small part of paper using a laser printing-based process for paper hydrophobization. The bipolar and driving electrodes are provided by pressing the writing pencil HB on the paper. The mechanism of sensing of glucose is oxidation of the analyte in the sensing cell using glucose oxidase followed by reduction of the produced H2O2 by application of an external potential (10.0 V). This causes the oxidation of K4Fe(CN)6 in the presence of Fe(II) ions and subsequent formation of Prussian Blue (PB) particles in the reporting cell. The intensity of the blue color in the reporting cell is used as a visual and colorimetric signal that can be digitally read using a scanner of digital camera. The parameters affecting the performance of the device were optimized using experimental design and chemometrics modeling. The P-BPE represents a very wide response range that extends from 0.1 mmol.L−1 to 4.0 mol.L−1 in the case of hydrogen peroxide, and from 0.1 to 50 mmol.L−1 in the case of glucose. The limit of detections for hydrogen peroxide and glucose are 4.9 μmol.L−1 and 70 μmol.L−1 respectively. [ABSTRACT FROM AUTHOR]

A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a paper. However, some edits are instances of scientific misconduct and undermine the integrity of the presented research. Determining the legitimacy of edits made to scientific images is an open problem that no current technology can perform satisfactorily in a fully automated fashion. It thus remains up to human experts to inspect images as part of the peer-review process. Nonetheless, image analysis technologies promise to become helpful to experts to perform such an essential yet arduous task. Therefore, we introduce SILA, a system that makes image analysis tools available to reviewers and editors in a principled way. Further, SILA is the first human-in-the-loop end-to-end system that starts by processing article PDF files, performs image manipulation detection on the automatically extracted figures, and ends with image provenance graphs expressing the relationships between the images in question, to explain potential problems. To assess its efficacy, we introduce a dataset of scientific papers from around the globe containing annotated image manipulations and inadvertent reuse, which can serve as a benchmark for the problem at hand. Qualitative and quantitative results of the system are described using this dataset. [ABSTRACT FROM AUTHOR]

This paper mainly updates the image segmentation and multi-target recognition algorithm through the support vector machine algorithm in ML, and verifies the reliability of the new proposed algorithm through simulation experiments, and determines that the ML-based image segmentation and multi-target recognition algorithm can have stronger processing capability in image recognition and processing. Finally, the performance of traditional image segmentation and multi class object recognition algorithms and the image segmentation and multi class object recognition algorithms proposed in this paper were compared through experiments. The results showed that the performance of the ML based image segmentation and multi class object recognition algorithms proposed in this paper has increased by 29.1% on average in all aspects. Article Highlight: This paper studies the different modes of machine learning algorithm models, and combines machine learning algorithm models with image segmentation and multi-target recognition modes, so that image segmentation and multi-target recognition algorithms can be used in image data analysis, data processing, and intelligent processing. Three aspects have better performance. In image data analysis and data processing, the advantages of machine learning models for data processing are mainly used. Through training on large amounts of data, a dedicated machine learning model for image data analysis and processing is established. The intelligent processing is reflected in the continuous improvement of this model and the further improvement of the degree of automation. [ABSTRACT FROM AUTHOR]

Background: The increasing number of physicians that are trained in point-of-care ultrasound (POCUS) warrants critical evaluation and improvement of current training methods. Performing POCUS is a complex task and it is unknown which (neuro)cognitive mechanisms are most important in competence development of this skill. This systematic review was conducted to identify determinants of POCUS competence development that can be used to optimize POCUS training. Methods: PubMed, Web of Science, Cochrane Library, Emcare, PsycINFO and ERIC databases were searched for studies measuring ultrasound (US) skills and aptitude. The papers were divided into three categories: "Relevant knowledge", "Psychomotor ability" and 'Visuospatial ability'. The 'Relevant knowledge' category was further subdivided in 'image interpretation', 'technical aspects' and 'general cognitive abilities'. Visuospatial ability was subdivided in visuospatial subcategories based on the Cattell-Horn-Carroll (CHC) Model of Intelligence v2.2, which includes visuospatial manipulation and visuospatial perception. Post-hoc, a meta-analysis was performed to calculate pooled correlations. Results: 26 papers were selected for inclusion in the review. 15 reported on relevant knowledge with a pooled coefficient of determination of 0.26. Four papers reported on psychomotor abilities, one reported a significant relationship with POCUS competence. 13 papers reported on visuospatial abilities, the pooled coefficient of determination was 0.16. Conclusion: There was a lot of heterogeneity in methods to assess possible determinants of POCUS competence and POCUS competence acquisition. This makes it difficult to draw strong conclusions on which determinants should be part of a framework to improve POCUS education. However, we identified two determinants of POCUS competence development: relevant knowledge and visuospatial ability. The content of relevant knowledge could not be retrieved in more depth. For visuospatial ability we used the CHC model as theoretical framework to analyze this skill. We could not point out psychomotor ability as a determinant of POCUS competence. [ABSTRACT FROM AUTHOR]

This paper accounts for the diagnostic campaign aimed at understanding the phenomenon of black stains appeared on the passepartout close to the margins of Folio 843 of Leonardo da Vinci's Codex Atlanticus. Previous studies excluded microbiological deterioration processes. The study is based on a multi-analytical approach, including non-invasive imaging measurements of the folio, micro-imaging and synchrotron spectroscopy investigations of passepartout fragments at different magnifications and spectral ranges. Photoluminescence hyperspectral and lifetime imaging highlighted that black stains are not composed of fluorescent materials. μATR-FTIR imaging of fragments from the passepartout revealed the presence of a mixture of starch and PVAc glues localized only in the stained areas close to the margin of the folio. FE-SEM observations showed that the dark stains are localized inside cavities formed among cellulose fibers, where an accumulation of inorganic roundish particles (∅100–200 nm in diameter size), composed of Hg and S, was detected. Finally, by employing synchrotron μXRF, μXANES and HR-XRD analyses it was possible to identify these particles as metacinnabar (β-HgS). Further research is needed to assess the chemical process leading to the metacinnabar formation in the controlled conservation condition of Leonardo's Codex. [ABSTRACT FROM AUTHOR]

Engaging students and the public in understanding UV radiation and its effects is achievable using the real time experiment that incorporates blueprint paper, an “educational toy” that is a safe and easy demonstration of the cyanotype chemical process. The cyanotype process works through the presence of UV radiation. The blueprint paper was investigated to be used as not only engagement in discussion for public outreach about UV radiation, but also as a practical way to introduce the exploration of measurement of UV radiation exposure and as a consequence, digital image analysis. Tests of print methods and experiments, dose response, spectral response and dark response were investigated. Two methods of image analysis for dose response calculation are provided using easy to access software and two methods of pixel count analysis were used to determine spectral response characteristics. Variation in manufacture of the blueprint paper product indicates some variance between measurements. Most importantly, as a result of this investigation, a preliminary spectral response range for the radiation required to produce the cyanotype reaction is presented here, which has until now been unknown. [ABSTRACT FROM AUTHOR]

A colorimetric test is described for the rapid detection of Staphylococcus aureus (SA). Gold nanorods (AuNRs) are first labeled with urease and yolk immunoglobulin (IgY). This probe can specifically bind SA. In the next step, nonspecific magnetic beads and sample are added. This leads to the formation of the AuNR-IgY-SA-nMB immunocomplex which is then magnetically separated. Finally, a solution of urea is added to the supernatant. Ureases catalyzes the decomposition of urea which results in an increase in the pH value. The increase in the pH value is detected by using a phenolphthalein test paper which undergoes a color change from white to pink. The analytical process can be completed within 20 min. The method is highly specific and can detect as little as 476 cfu·mL−1. It was verified by analyzing contaminated Chinese cabbage and beef samples, and 1000 cfu·mL−1 of SA were accurately detected. [ABSTRACT FROM AUTHOR]

This paper is devoted to a review of the achievements of the Moscow scientific school of image recognition, formed under the leadership of Professor Vladimir L'vovich Arlazarov, in the field of development and application of the Viola–Jones method. One of the main areas of research at the school is the development of computationally efficient recognition algorithms, which requires a deep understanding of the problem and a wide expertise in the field of existing classical algorithms. Such classic method as the Viola—Jones method became an essential tool to solve a wide range of image recognition problems. This paper provides an overview of the modifications of the original method developed by the scientific school and describes in detail the experience of solving many different practical problems that arise in the development of modern energy-efficient image recognition systems. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. [ABSTRACT FROM AUTHOR]

The publication presents an introductory paper to the Special issue of the international journal Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications of the Russian Academy of Sciences. The main scientific results of the 8th International Workshop "Image Mining: Theory and Applications," held on August 21, 2022, Montreal, Canada, are presented. Historical information is given on this series of international workshops, and their significant role in the development of the theory and practice of automation of image analysis, pattern recognition, and artificial intelligence is emphasized. The list of papers of the Special issue of PRIA, prepared based on the invited and regular papers selected and recommended for publication by the Program Committee of the IMTA-VIII-2022, is presented. [ABSTRACT FROM AUTHOR]

Films made of nanofibrillated cellulose (NFC) are most interesting for use in packaging applications. However, in order to understand the film-forming capabilities of NFC and their properties, new advanced methods for characterizing the different scales of the structures are necessary. In this study, we perform a comprehensive characterisation of NFC-films, based on desktop scanner analysis, scanning electron microscopy in backscatter electron imaging mode (SEM-BEI), laser profilometry (LP) and field-emission scanning electron microscopy in secondary electron imaging mode (FE-SEM-SEI). Objective quantification is performed for assessing the (i) film thicknesses, (ii) fibril diameters and (iii) fibril orientations, based on computer-assisted electron microscopy. The most frequent fibril diameter is 20–30 nm in diameter. A method for acquiring FE-SEM images of NFC surfaces without a conductive metallic layer is introduced. Having appropriate characterisation tools, the structural and mechanical properties of the films upon moisture were quantified. [ABSTRACT FROM AUTHOR]

Historical texts incorporate important characteristics that need to be assessed including genre, text structure and content. Often overlooked are characteristics of handwritten manuscripts commonly divided into legibility, readability and aesthetics. To determine the scientific feasibility of classification of handwritten texts an objective approach is developed to describe twenty handwritten pages of an 1819 Greek manuscript, that refers to the initiation to the Greek secret "friendly society" (Philike Hetaereia) organization, established as part of the Greek independence against the Ottoman Turks. It is investigated through a fractal and RGB image analysis approach. Fractal Minkowski Dimension was applied on the handwritten text and the RGB color analysis on the ink and paper and both were used as a non-invasive manner and revealed interesting results. The novel RGB image analysis and the fractal analysis of the manuscript identified respectively, five iron gall inks and four scribes from the ink content and handwritten styles, of the compact five lines text and whole text pages. The novel approach was verified with another old manuscript of known ink pigments, as well as with thirteen known handwritten texts of that period and four prints representing modern and similar period texts substantiating the findings of the novel methods. [ABSTRACT FROM AUTHOR]

Existing image analysis algorithms cannot achieve consistency with human visual classification results when classifying particles based on angular levels. To address this issue, this paper proposes an image analysis method based on triangle side ratio to quantify particle angularity, referred to as a TSR method. The proposed method utilizes a primary parameter, Mean Angularity, to assess the mean angularity level, and employs three auxiliary parameters to offer insights into the Sharpest Angularity, the Flat Proportion, and the Number of Angularity. When quantifying the angularity, the method further provides the count of convex angles. Each parameter can reflect different characteristic information of the angularity. When using the mean angularity level to order particles, the TSR method achieves the same results as visual classification, and furthermore introduces a range of values for the main parameter corresponding to the different angularity levels. The TSR method is simpler and more stable, since the particle parameters can be calculated directly without contour smoothing, and consistent results are achieved for different shapes with the same degree of angular sharpness. The results of the study on lunar soil, volcanic rock, mechanism stone, and stream stone, show that the TSR method can objectively and comprehensively analyze and quantify the particle angularity. [ABSTRACT FROM AUTHOR]

Breast cancer is the second most prevalent cause of cancer death and the most common malignancy among women, posing a life-threatening risk. Treatment for breast cancer can be highly effective, with a survival chance of 90% or higher, especially when the disease is detected early. This paper introduces a groundbreaking deep U-Net framework for mammography breast cancer images to perform automatic detection of abnormalities. The objective is to provide segmented images that show areas of tumors more accurately than other deep learning techniques. The proposed framework consists of three steps. The first step is image preprocessing using the Li algorithm to minimize the cross-entropy between the foreground and the background, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), normalization, and median filtering. The second step involves data augmentation to mitigate overfitting and underfitting, and the final step is implementing a convolutional encoder-decoder network-based U-Net architecture, characterized by high precision in medical image analysis. The framework has been tested on two comprehensive public datasets, namely INbreast and CBIS-DDSM. Several metrics have been adopted for quantitative performance assessment, including the Dice score, sensitivity, Hausdorff distance, Jaccard coefficient, precision, and F1 score. Quantitative results on the INbreast dataset show an average Dice score of 85.61% and a sensitivity of 81.26%. On the CBIS-DDSM dataset, the average Dice score is 87.98%, and the sensitivity reaches 90.58%. The experimental results ensure earlier and more accurate abnormality detection. Furthermore, the success of the proposed deep learning framework in mammography shows promise for broader applications in medical imaging, potentially revolutionizing various radiological practices. [ABSTRACT FROM AUTHOR]

The surveillance and management of cargo fleets is a crucial objective of intelligent transportation systems. Load, especially overload, has a destructive effect on roads and bridges, and monitoring it can increase the life of road surface and its structure. For low-end hardware with lack of CPU power and no GPU support, this paper presents a rapid method to detect whether heavy vehicles have loads or not; then it proposes a fast method for classifying load types to distinguish soil and construction waste from other miscellaneous loads for heavy weight vehicles. This paper applies a method for classifying cargo types using image processing and texture image classification. This method extracts features for statistical analysis of texture images based on gray-level co-occurrence matrices and local binary patterns. The classification is carried out by support vector machine, k-nearest neighborhood, K-mean, artificial neural networks and random forest classifiers. A large number of positive and negative patterns have been used to train these classifiers. We compare the performance of proposed extracted features and classifiers. The simulation results demonstrate that soil and construction waste can be identified from other miscellaneous loads effective in real-time implementation. [ABSTRACT FROM AUTHOR]

The Cauvery Basin is one of the prolific hydrocarbon-producing basins of southern India. The pericratonic basin has five sub-basins separated by five basement and structural highs formed by granitic and gneissic rocks of the Archaean Southern Granulite Terrane. The sub-basins have excellent Cretaceous source rocks and hydrocarbon-bearing reservoirs of different geological ages. Commercial hydrocarbons are produced from the Archean basement highs, specifically from the Kumbakonam–Madanam palaeo-highs of the Cauvery basin. The paper addresses basement characterisation using conventional as well as advanced well logs for accurate characterisation of the basement reservoirs. Basement reservoirs are challenging in terms of sporadic porosity and permeability distribution. Most of the porosities and permeability are attributed to secondary generation by fracturing and weathering. The present work aims to understand and compare the petrophysical attributes of two kinds of basement reservoirs (fractured basement and weathered basement) on the Kumbakonam–Madanam High. Two wells from each type lying on either side of the Madanam High were selected. Well log data, including gamma-ray, resistivity, porosity (neutron and bulk density), acoustic logs and advanced logs, such as resistivity images and dipole acoustic and elemental capture spectroscopy logs, from these four wells were analysed for their petrophysical understanding. Furthermore, image and acoustic log data were integrated to characterise the fracture geometry and fracture permeability of the basement reservoirs. Conventional log suites showing low GR, low resistivity, high density, and high neutron porosity are indicative of intricate lithologies, possibly mafic rocks. A notable negative crossover in density and neutron log along with excess Si concentration indicates weathering. A higher fracture density with a crisscross fracture/mesh fracture pattern is indicated by the analysis of image logs, shear wave anisotropy, and Stoneley fractures. Thus, a comparison of the petrophysical attributes of both fields is attempted to understand the fractured and weathered basement reservoirs and their geological characteristics. In conclusion, the basement reservoirs of the Madanam High Field are of interest because of their hydrocarbon-producing ability, and proper synthesis of petrophysical attributes will help develop activities in the basement reservoirs. [ABSTRACT FROM AUTHOR]

We discuss a roughness problems in this paper. Roughness measurements play a fundamental role in terrestrial planet research. The main question discussed in this paper is how to build a more efficient roughness model on the basis of images. The model needs to beat a series of traditional roughness problems on the image. Lunar reconnaissance orbiter camera (LROc) and charged coupled device (CCD) images are our primary data target. Roughness is divided into trend surface roughness and topography roughness with analysis and inversion. The two parts are combined and analyzed. The analysis is based on two structural features: trend reflection and detail details surface. Our team use inversion to remap the surface roughness by CCD image, and then classify the roughness into the trend influence class and the detail influence class. We use frequency analysis tools are to find the discontinuous energy in the frequency domain. Finally, these roughness information are remapped into a visual image results. Our model provide a more accurate method on terrestrial planetary surface roughness problem. We can analyze the difference between the trend surface and the detail roughness by single image. We find that, lunar surface roughness presents more detail with the help of the frequency analysis. Highlands and the mare shows very different roughness decomposition. Our analysis shows better results in the areas in which details surface is not associated with roughness. It is hoped that this study will provide new insights in the surface roughness of terrestrial planets. Especially for CCD images (LROc) rather than details surface data (DEM). In the future, we will focus on calculations and analysis for Mars based on the mode in this paper. For the roughness data, we will start depth learning, classification, pattern recognition and other research in future work. [ABSTRACT FROM AUTHOR]

With the development of society and the improvement of people's living standards, ecotourism based on forest ecological environment has become an urgent need of urban and rural people. Forest leisure tourism is different from traditional sightseeing tourism. The differences between them are not only reflected in consumption purpose, consumption behavior, consumption grade and consumption form, but also reflected in the differences between tourism products, service system and service quality demand. Therefore, in order to develop forest leisure tourism, we must first fully understand the objective status quo of the leisure tourism market and the real needs of the market, and correctly judge the basic characteristics of the current forest leisure tourism market. At present, the competition of tourism market is upgraded from the competition of tourism price and tourism product to the competition of tourism brand. However, the resource utilization efficiency of forest park tourism is insufficient, and the internal management and external marketing are difficult to adapt to the changes of the market, thus the image and attraction of forest park tourism need to be improved urgently. Therefore, based on remote sensing image analysis and neural computing model, this paper constructs a forest ecotourism evaluation scheme. We designed the novel cloud-based mobile edge computing model to construct the efficient scenario for the prediction. The experimental results show that the model proposed in this paper can effectively evaluate the development plan of forest eco-tourism. [ABSTRACT FROM AUTHOR]

The paper considers and analyzes the main scientific results of the 15th International Conference on Pattern Recognition and Information Processing (PRIP-2021), Minsk, Republic of Belarus, September 2021. The history of this series of conferences is outlined and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue prepared on the basis of reports selected by the PRIP-2021 program committee is provided. [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]

In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist's reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs. [ABSTRACT FROM AUTHOR]

Contour detection is the basic content of image processing and plays an important role in image analysis and target recognition. This paper proposed a contour perception model that simulates the complex connection pattern of the visual cortex. The connection included the feedforward input from the lateral geniculate body (LGN), the horizontal input from the neurons in the same layer, and the feedback input from the advanced visual cortex. Using the sparse coding characteristics of the LGN, the windmill-like structure receptive field of the primary visual cortex, and the hue perception characteristics of the advanced visual cortex to improve the accuracy of the contour extracted by the proposed model. Choosing the BSDS500 natural scene dataset as the experimental object, the F-score is selected as the evaluation index. The average optimal F-score of the proposed method is 0.72, which is better than other mainstream biological vision-based methods. Concurrently, the NYUD dataset is used for further verification. To comprehensively verify the effectiveness of the model proposed in this paper, Performance-value rather than F-score is selected as the evaluation index. The average optimal Performance-value of the proposed method is 0.42, which shows better results, too. The complex connection pattern allows neural encoding and decoding to make full use of the characteristics of information exchange between the visual cortexes, which is more in line with the biological vision system. [ABSTRACT FROM AUTHOR]

This paper studied the multilevel threshold image segmentation-based metaheuristics optimization methods and their applications. Image segmentation is a common problem in the image processing domain, and it is an essential process in image analysis, directly impacting image analysis results. Thresholding is one of the most manageable and extensively utilized methods for handling image segmentation problems. In this paper, four main parts are given; (1) We present the main procedures and definitions of the multilevel threshold image segmentation problem. The standard fitness function and the evaluation criteria are also given to facilitate the problem representation for the new researchers in this domain. (2) All the related works that have used optimization methods in solving the multilevel threshold image segmentation problems are presented in more detail, focusing on the image segmentation problem and its solutions. The given related works are outlined according to the used algorithms. (3) Comprehensive results and analysis of several well-known optimization algorithms are conducted to solve the multilevel threshold image segmentation problems. These comparative methods include Aquila Optimizer (AO), Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), Arithmetic Optimization Algorithm (AOA), Particle Swarm Optimizer (PSO), Marine Predators Algorithm (MPA), Krill Herd Algorithm (KHA), Multi-verse Optimizer (MVO), and Gray Wolf Optimizer (GWO). Eight standard benchmark images are used to test the comparative methods. The results are evaluated using three standard measures: fitness function, PeakSignal-to-Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). (4) Discussion, open challenging, and new trends are given to help the scholars in future research get near the common problems and defect in that domain. The collected data in this review has been taken from google scholar using the stander search method. The main keywords that have been used in the search are multilevel, threshold, image, segmentation, optimization, and algorithm. We covered all the published papers in detail according to the given information, focusing on finding the common problems that still need further investigation. Furthermore, future research directions based on recently evolving designs are outlined, which should undoubtedly aid current researchers and practitioners and pave the way for new researchers interested in multilevel threshold image segmentation to seek their research in the field. [ABSTRACT FROM AUTHOR]

Abstract—This paper offers a general approach to the modelling of the wellbore stress state in the Bazhenov formation. In modelling, strata are presented as transversely isotropic objects with a set direction of the anisotropy axis that does not coincide with the vertical line. The paper also presents results of the laboratory Bazhenov formation core analysis. The analysis of core materials yielded correlation dependences for reconstructing stress-strain parameters of a transversely isotropic medium in selected wells. Parameters of the rock anisotropy were analyzed on the basis of standard and non-standard laboratory tests. The solution of the stress state problem in a deformation bounded medium was used to suggest an algorithm of calculating the components of the regional stress state along the well path. RN-SIGMA software tools were used to construct borehole stability models for wells penetrating anisotropic rocks. The effect of the anisotropy of elasticity on a safe drilling mud density window is demonstrated. The results of the calculations show that the anisotropy effects are crucial for calculating a safe drilling window. Examples are given to demonstrate potential effects of the anisotropy in image interpretation as well as in selecting horizontal wellbore drilling directions. [ABSTRACT FROM AUTHOR]

With the advent of several tools and web engines trained for finding journal articles out of billions of research papers on millions of topics in different databases with a high degree of generalizability, it often leads to a loss of specificity. Scientific pursuits need a tool to extract data from selected resources for performing domain-specific tasks. Current algorithms and generalized tools lack specificity and are challenged by errors in analysing data from a bundle of specific documents selected eclectically. Current work addresses the need for such a tool, which focuses on specificity based on users' input keywords and phrases to find relevant information from bundles of articles from the web. Reseractor is based on a customized algorithm, Whitespace, in synergy with output from open-access tools for document image analysis and focused domain data extraction using NLP. The current tool is designed for the material science domain with the features of adopting various generalized and scientific corpora as layers. It is tested on two sets of different bundles of papers and gives an accuracy of 81.12% along with a recall of 78.38% and a precision of 84.06%. Owing to the simple and direct applicability of algorithms, users from other domains can directly use their corpora in algorithms and remodel the tool for their purpose. Current work fulfills the need for domain-specific experimental data extraction stored in organized and structured databases for upcoming computational researchers. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to detect solar panel defects early, leveraging distinct datasets comprising aerial and electroluminescence (EL) images. The decision to employ separate datasets with different models signifies a strategic choice to harness the unique strengths of each imaging modality. Aerial images provide comprehensive surface-level insights, while electroluminescence images offer valuable information on internal defects. By using these datasets with specialized models, the study aims to improve defect detection accuracy and reliability. The research explores the effectiveness of modified deep learning models, including DenseNet121 and MobileNetV3, for analyzing aerial images, and introduces a customized architecture and EfficientNetV2B2 models for electroluminescence image analysis. Results indicate promising accuracies for DenseNet121 (93.75%), MobileNetV3 (93.26%), ELFaultNet (customized architecture) (91.62%), and EfficientNetV2B2 (81.36%). This study's significance lies in its potential to transform solar panel maintenance practices, enabling early defect identification and subsequent optimization of energy production. [ABSTRACT FROM AUTHOR]

Since deep learning is introduced to medical image segmentation, UNet and its variants have been extensively applied in medical image analysis. This paper proposes a multi-scale channel attention UNet (MSCA-UNet) to raise the accuracy of the segmentation in medical ultrasound images. Specifically, a multi-scale module is constructed to connect and to enhance the feature maps with different scales extracted by convolution. Subsequently, A channel attention mechanism is designed to compress feature maps through learnable depth separable convolutions. Eventually, we have explored the global feature module to establish the dependency between multi-level features. The proposed method is thoroughly evaluated and compared with the existing methods on four medical ultrasound image datasets. The experiments indicate that our method outperforms the SOTA method in accuracy on four medical ultrasound image datasets. Compared with UNet network, the parameters of our model have decreased 29.82%. In addition, visual comparisons further demonstrate the proposed method. [ABSTRACT FROM AUTHOR]