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Forensic document examination has been considered one of the most important fields in the forensic science. For this, a quick, effective, and cheap techniques for evaluation of forensic evidence is highly required. This study explores the comparison of A4 document paper by chemical and physical analysis. 16 of white A4 paper from different brands were analyzed by FTIR for investigation of structural characteristics. Video Spectral Comparator 8000 has been used to compare the A4 papers under different light source. The inorganic filler composition in the paper is also explored by ICP‐OES. The calcium carbonate content in document papers play a discriminant role which found to be between 16.02 % and 35.54 %. Also, water contact angle has been utilized in order to examine the surface of paper which is generally related to the inorganic filler and other additives. The WCA results ranged between 83° and 111° that enable quick and easy way to compare A4 document papers. The experimental results have demonstrated that besides FTIR methods, other methods such as ICP‐OES for elemental composition, WCA for wetting test, VSC for visualization under different light of source could have been applied for comparison of document papers in case of criminal cases. [ABSTRACT FROM AUTHOR]

This study proposes a method to determine the two‐dimensional absorption capacity of paper towels. Individual plies were tested to minimize the effects of transverse wicking on wetting. The method involves arbitrary point source injection of liquid into the towel surface while recording microscopic images of the wetted areas. The samples were selected from the main manufacturing processes: conventional wet pressing and through‐air drying. Wetting graphs that illustrate the dynamic and static wetting behaviors were generated. The driving force was found to have a negligible effect on the maximum wetted area. The maximum wetted area and the paper grammage were used to determine the two‐dimensional absorption capacity. The results were validated by the basket‐immersion test method. [ABSTRACT FROM AUTHOR]

BACKGROUND: This work proposes the use of a simple, inexpensive method for the hydrothermal synthesis of fluorescent carbon dot nanoparticles (CDs) from rice starch aimed at the determination of Hg2+ in water. The proposed method involved using a paper‐based analytical device coupled to a 3D plate, with a UV‐LED chamber and a smartphone for the acquisition and analysis of the fluorescence digital images of the CDs. RESULTS: The size of the carbon dots ranged from 0.5 to 3 nm, with an average particle size of ~1 nm. The functionalization of carbon dots with methimazole allowed a high selective for Hg2+ determination. The results obtained showed a linear response R2 of 0.997 and Hg2+ concentration in the range of 0.5–45.0 μmol L−1 with a limit of detection and limit of quantitation of 0.23 and 0.62 μmol L−1, respectively. The results of the study show that there were no significant differences, at 95% confidence level, between the data obtained from the application of the proposed method and the reference method. CONCLUSION: The proposed method is in line with the principles of green chemistry, as it involves the use of renewable sources for starch extraction and a hydrothermal synthesis process that does not employ toxic reagents. In addition, the method employs only 15 μL reagent/sample. © 2024 Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

We propose a printed 384‐well microtiter paper‐based fluorescent chemosensor array device (384‐well microtiter PCAD) to simultaneously categorize and discriminate saccharides and sulfur‐containing amino acids for food analysis. The 384‐well microtiter PCAD requiring 1 μL/4 mm2 of each well can allow high‐throughput sensing. The device embedded with self‐assembled fluorescence chemosensors displayed a fingerprint‐like response pattern for targets, the image of which was rapidly captured by a portable digital camera. Indeed, the paper‐based chemosensor array system combined with imaging analysis and pattern recognition techniques not only successfully categorized saccharides and sulfur‐containing amino acids but also classified mono‐ and disaccharide groups. Furthermore, the quantitative detectability of the printed device was revealed by a spike and recovery test for fructose and glutathione in a diluted freshly made tomato juice. We believe that the 384‐well microtiter PCAD using the imaging analysis system will be a powerful sensor for multi‐analytes at several categorized groups in real samples. [ABSTRACT FROM AUTHOR]

The surface characteristics of a large set of commercial lightweight coated paper grades are explored. The quantification of the 3D structure is revealed by atomic force microscopy, laser profilometry and X-ray microtomography. This comprehensive study demonstrates the suitability of different and modern methods for assessing critical coating layer properties, thus identifying the right tools for specific structural analyses. Based on the assessment of the top and bottom surfaces of 25 commercial lightweight coated samples, three main conclusions can be drawn: (1) the facet orientation polar angle is a function of roughness, (2) skewness did not describe the surface details affecting the gloss of the commercial lightweight coated samples assessed in this study and (3) surface roughness at wavelengths below approximately 1.0 μm does not affect the paper gloss significantly. This is important knowledge for the understanding of lightweight coated paper surface structure and its properties. [ABSTRACT FROM AUTHOR]

Wearable eye‐tracking technology is an essential component in AR/VR devices. Conventional Pupil‐Corneal Reflection (PCR) eye‐tracking technology often presents challenges due to high costs and bulky designs, particularly for compact AR and smart glasses. To overcome these limitations, our next‐generation eyetracking technology relies on AI image analysis, significantly reducing the number of required IR LEDs and image sensors. This simplification enhances flexibility and lowers production costs while maintaining accuracy. Our breakthrough solution makes eye tracking more accessible for the masses in AR/VR devices in the new era of spatial computing and the metaverse. [ABSTRACT FROM AUTHOR]

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber‐security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

This study presents high‐resolution optical inspection methods to evaluate the assembly yield of densely‐aligned NR LED array for self‐emitting inorganic material‐based display. Fast line scanning images and their deep learning‐based image analysis enabled us to quantitively investigate the assembly yield of NR LEDs even up to 5300 pixel per inch in which, thereafter, automatic optical inspection (AOI) accuracy reaches up to 97 % after 8000 times iteration. Furthermore, confocal photoluminescence optical microscopic system with picoseconds time resolving capability investigated tens nanometer spatially‐resolved emission characteristics of NR LEDs while also showing the non‐radiative recombination from the damaged active layers. Therefore, we believe that this study contributes a significant step toward inspection process of micro LEDs and their manufacturing development. [ABSTRACT FROM AUTHOR]

Reviewing literatures for a certain research field is always important for academics. One could use Google-like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis. [ABSTRACT FROM AUTHOR]

The authors present a comprehensive overview of image processing and analysis work done to support research into the model flowering plant Arabidopsis thaliana. Beside the plant's importance in biological research, using image analysis to obtain experimental measurements of it is an interesting vision problem in its own right, involving the segmentation and analysis of sequences of images of objects whose shape varies between individual specimens and also changes over time. While useful measurements can be obtained by segmenting a whole plant from the background, they suggest that the increased range and precision of measurements made available by leaf-level segmentation makes this a problem well worth solving. A variety of approaches have been tried by biologists as well as computer vision researchers. This is an interdisciplinary area and the computer vision community has an important contribution to make. They suggest that there is a need for publicly available datasets with ground truth annotations to enable the evaluation of new approaches and to support the building of training data for modern data-driven computer vision approaches, which are those most likely to result in the kind of fully automated systems that will be of use to biologists. [ABSTRACT FROM AUTHOR]

This document is a guest editorial for a special issue of the CAAI Transactions on Intelligence Technology journal focused on intelligence technology for remote sensing image analysis. The editorial highlights various research papers that cover topics such as scene interpretation, target detection, hyperspectral classification, and multi-modal analysis. The papers discuss different methods and techniques to improve the accuracy and efficiency of remote sensing tasks, including privacy-preserving recognition, knowledge distillation, optimisation strategies, intelligent anchor learning, deep learning methods, and fusion approaches. The editorial acknowledges the authors and reviewers for their contributions and hopes that the special issue will inspire further research in remote sensing scene interpretation. [Extracted from the article]

The roles of physicists in medical imaging have expanded over the years, from the study of imaging systems (sources and detectors) and dose to the assessment of image quality and perception, the development of image processing techniques, and the development of image analysis methods to assist in detection and diagnosis. The latter is a natural extension of medical physicists’ goals in developing imaging techniques to help physicians acquire diagnostic information and improve clinical decisions. Studies indicate that radiologists do not detect all abnormalities on images that are visible on retrospective review, and they do not always correctly characterize abnormalities that are found. Since the 1950s, the potential use of computers had been considered for analysis of radiographic abnormalities. In the mid-1980s, however, medical physicists and radiologists began major research efforts for computer-aided detection or computer-aided diagnosis (CAD), that is, using the computer output as an aid to radiologists—as opposed to a completely automatic computer interpretation—focusing initially on methods for the detection of lesions on chest radiographs and mammograms. Since then, extensive investigations of computerized image analysis for detection or diagnosis of abnormalities in a variety of 2D and 3D medical images have been conducted. The growth of CAD over the past 20 years has been tremendous—from the early days of time-consuming film digitization and CPU-intensive computations on a limited number of cases to its current status in which developed CAD approaches are evaluated rigorously on large clinically relevant databases. CAD research by medical physicists includes many aspects—collecting relevant normal and pathological cases; developing computer algorithms appropriate for the medical interpretation task including those for segmentation, feature extraction, and classifier design; developing methodology for assessing CAD performance; validating the algorithms using appropriate cases to measure performance and robustness; conducting observer studies with which to evaluate radiologists in the diagnostic task without and with the use of the computer aid; and ultimately assessing performance with a clinical trial. Medical physicists also have an important role in quantitative imaging, by validating the quantitative integrity of scanners and developing imaging techniques, and image analysis tools that extract quantitative data in a more accurate and automated fashion. As imaging systems become more complex and the need for better quantitative information from images grows, the future includes the combined research efforts from physicists working in CAD with those working on quantitative imaging systems to readily yield information on morphology, function, molecular structure, and more—from animal imaging research to clinical patient care. A historical review of CAD and a discussion of challenges for the future are presented here, along with the extension to quantitative image analysis. [ABSTRACT FROM AUTHOR]

Neoangiogenesis is an integral component of bone marrow myeloproliferation in patients with myelofibrosis with myeloid metaplasia (MMM). As extramedullary haematopoiesis is a constitutive feature of MMM, we studied spleen neoangiogenesis by a computerized image analysis in MMM patients. Compared with five normal subjects, spleen CD34-staining capillary vascular density (CVD) was 2·1–3·03 times higher than the upper range of normal in six of the 15 (40%) MMM patients. CD8-staining sinusoidal vascular density (SVD) was constantly normal or lesser than normal and was inversely correlated with CVD ( R = −0·53; P = 0·04). In MMM patients who did not receive cytoreductive or radiation therapy in the month before splenectomy ( n = 9), the CVD was a significant determinant of spleen size ( R = 0·88; P = 0·04). In MMM patients, the number of spleen CD34+ haematopoietic stem cells was increased from 1·2 to 98 times the upper limit of normal, and predicted the expansion of CVD ( R = 0·57; P = 0·03). A population of cells expressing the CD34+/CD133+/VEGFR-2+ angiopoietic phenotype was present in the blood and spleen of five of seven patients. These results document that neoangiogenesis is an integral component of spleen re-localization of haematopoietic stem cells and suggest a cellular mechanism for spleen neoangiogenesis. [ABSTRACT FROM AUTHOR]

As environmental considerations for both the processing and disposal of electronic devices become increasingly important, the ability to replace plastic and glass substrates with bioderived and biodegradable materials remains a major technological goal. Here, the use of cellulose nanofiber‐coated paper is explored as an environmentally benign substrate for preparing low‐resistance (460 Ω sq−1), colorless (a* = −2.3, b* = −2.7) printed poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) electrodes. The PEDOT:PSS/paper electrodes support the reversible oxidation of three electrochromic polymers (ECPs) (cyan, magenta, and yellow), affording the possibility for fully printed, color displays on paper. Lateral electrochromic devices (ECDs) incorporating an ion gel electrolyte are demonstrated where a magenta‐to‐colorless device achieves a color contrast (ΔE*) of 56 owing to a highly color‐neutral bleached state of the ECP (a* = −0.5, b* = 2.9). Black‐to‐colorless devices achieve ΔE* = 29 and are able to retain 86% of their color contrast after 9000 switches. The switching times of these lateral devices are quantified through colorimetric image analysis which shows comparable performance for devices constructed on paper as devices using ITO/glass electrodes (10 Ω sq−1). The paper ECDs are then combusted in air leaving 3% of the initial mass at 600 °C, highlighting this approach as a promising route toward disposable displays. [ABSTRACT FROM AUTHOR]

Eye health has become a global health concern and attracted broad attention. Over the years, researchers have proposed many state‐of‐the‐art convolutional neural networks (CNNs) to assist ophthalmologists in diagnosing ocular diseases efficiently and precisely. However, most existing methods were dedicated to constructing sophisticated CNNs, inevitably ignoring the trade‐off between performance and model complexity. To alleviate this paradox, this paper proposes a lightweight yet efficient network architecture, mixed‐decomposed convolutional network (MDNet), to recognise ocular diseases. In MDNet, we introduce a novel mixed‐decomposed depthwise convolution method, which takes advantage of depthwise convolution and depthwise dilated convolution operations to capture low‐resolution and high‐resolution patterns by using fewer computations and fewer parameters. We conduct extensive experiments on the clinical anterior segment optical coherence tomography (AS‐OCT), LAG, University of California San Diego, and CIFAR‐100 datasets. The results show our MDNet achieves a better trade‐off between the performance and model complexity than efficient CNNs including MobileNets and MixNets. Specifically, our MDNet outperforms MobileNets by 2.5% of accuracy by using 22% fewer parameters and 30% fewer computations on the AS‐OCT dataset. [ABSTRACT FROM AUTHOR]

Migratory songbirds carry an inherited capacity to migrate several thousand kilometers each year crossing continental landmasses and barriers between distant breeding sites and wintering areas. How individual songbirds manage with extreme precision to find their way is still largely unknown. The functional characteristics of biological compasses used by songbird migrants has mainly been investigated by recording the birds directed migratory activity in circular cages, so-called Emlen funnels. This method is 50 years old and has not received major updates over the past decades. The aim of this work was to compare the results from newly developed digital methods with the established manual methods to evaluate songbird migratory activity and orientation in circular cages., We performed orientation experiments using the European robin ( Erithacus rubecula) using modified Emlen funnels equipped with thermal paper and simultaneously recorded the songbird movements from above. We evaluated and compared the results obtained with five different methods. Two methods have been commonly used in songbirds' orientation experiments; the other three methods were developed for this study and were based either on evaluation of the thermal paper using automated image analysis, or on the analysis of videos recorded during the experiment., The methods used to evaluate scratches produced by the claws of birds on the thermal papers presented some differences compared with the video analyses. These differences were caused mainly by differences in scatter, as any movement of the bird along the sloping walls of the funnel was recorded on the thermal paper, whereas video evaluations allowed us to detect single takeoff attempts by the birds and to consider only this behavior in the orientation analyses. Using computer vision, we were also able to identify and separately evaluate different behaviors that were impossible to record by the thermal paper., The traditional Emlen funnel is still the most used method to investigate compass orientation in songbirds under controlled conditions. However, new numerical image analysis techniques provide a much higher level of detail of songbirds' migratory behavior and will provide an increasing number of possibilities to evaluate and quantify specific behaviors as new algorithms will be developed. [ABSTRACT FROM AUTHOR]

Purpose: In recent years, the use of deep learning for medical image segmentation has become a popular trend, but its development also faces some challenges. Firstly, due to the specialized nature of medical data, precise annotation is time‐consuming and labor‐intensive. Training neural networks effectively with limited labeled data is a significant challenge in medical image analysis. Secondly, convolutional neural networks commonly used for medical image segmentation research often focus on local features in images. However, the recognition of complex anatomical structures or irregular lesions often requires the assistance of both local and global information, which has led to a bottleneck in its development. Addressing these two issues, in this paper, we propose a novel network architecture. Methods: We integrate a shift window mechanism to learn more comprehensive semantic information and employ a semi‐supervised learning strategy by incorporating a flexible amount of unlabeled data. Specifically, a typical U‐shaped encoder‐decoder structure is applied to obtain rich feature maps. Each encoder is designed as a dual‐branch structure, containing Swin modules equipped with windows of different size to capture features of multiple scales. To effectively utilize unlabeled data, a level set function is introduced to establish consistency between the function regression and pixel classification. Results: We conducted experiments on the COVID‐19 CT dataset and DRIVE dataset and compared our approach with various semi‐supervised and fully supervised learning models. On the COVID‐19 CT dataset, we achieved a segmentation accuracy of up to 74.56%. Our segmentation accuracy on the DRIVE dataset was 79.79%. Conclusions: The results demonstrate the outstanding performance of our method on several commonly used evaluation metrics. The high segmentation accuracy of our model demonstrates that utilizing Swin modules with different window sizes can enhance the feature extraction capability of the model, and the level set function can enable semi‐supervised models to more effectively utilize unlabeled data. This provides meaningful insights for the application of deep learning in medical image segmentation. Our code will be released once the manuscript is accepted for publication. [ABSTRACT FROM AUTHOR]

Key points Calcium indicator fluorescence imaging is a key tool for investigating the physiological mechanisms that initiate and regulate myometrial contractions. However, inhomogeneous tissue motion severely limits the effective spatial resolution of measurable calcium activity., We present an image analysis method that locally tracks contractions to produce a complete description of tissue-wide motion. This renders calcium signals measurable at the micrometre scale. Additionally, the method simultaneously extracts kinematics of the motion that will inform biomechanical models of contracting myometrium., The method is demonstrated on calcium-imaging datasets of contracting myometrium. Using the method, we observed significant heterogeneity in calcium activity and identified a characteristic length scale of 40-50 μm below which tissue motion remains locally homogeneous., We provide freely available and modifiable code to process datasets affected by motion artefacts. The method has potential application to in vivo neuron imaging, as well as to calcium imaging of other smooth muscle tissue., Abstract Successful childbirth depends on the occurrence of precisely coordinated uterine contractions during labour. Calcium indicator fluorescence imaging is one of the main techniques for investigating the mechanisms governing this physiological process and its pathologies. The effective spatiotemporal resolution of calcium signals is, however, limited by the motion of contracting tissue: structures of interest in the order of microns can move over a hundred times their width during a contraction. The simultaneous changes in local intensity and tissue configuration make motion tracking a non-trivial problem in image analysis and confound many of the standard techniques. This paper presents a method that tracks local motion throughout the tissue and allows for the almost complete removal of motion artefacts. This provides a stabilized calcium signal down to a pixel resolution, which, for the data examined, is in the order of a few microns. As a byproduct of image stabilization, a complete kinematic description of the contraction-relaxation cycle is also obtained. This contains novel information about the mechanical response of the tissue, such as the identification of a characteristic length scale, in the order of 40-50 μm, below which tissue motion is homogeneous. Applied to our data, we illustrate that the method allows for analyses of calcium dynamics in contracting myometrium in unprecedented spatiotemporal detail. Additionally, we use the kinematics of tissue motion to compare calcium signals at the subcellular level and local contractile motion. The computer code used is provided in a freely modifiable form and has potential applicability to in vivo calcium imaging of neural tissue, as well as other smooth muscle tissue. [ABSTRACT FROM AUTHOR]

To develop a fast and effective image deblurring method, the blind recovery of motion‐blurred images based on DeblurGAN(GAN, Generative Adversarial Networks) is researched.Firstly, the number of residual modules in the DeblurGAN network is changed, and an attempt is made to optimize the network structure to achieve better results in the blind recovery of motion‐blurred images. Secondly, an image deblurring method based on the improved DeblurGAN network is proposed.This paper makes corresponding adjustments to the generator and discriminator networks to change their input and output size to 512 × 512 while keeping the overall structure of the network unchanged, and the experimental results show that the quality of the restored images has been greatly improved. In addition, the images were divided into four classes of images,to achieve improved restoration of blurred images with specific content. Experimental results on the benchmark GoPro dataset validate that the improved DeblurGAN achieves more than 1.5 dB improvement than DeblurGAN in terms of peak signal‐to‐noise ratio (PSNR) as trained utilizing the same amount of data, and structural similarity assessment (SSIM) evaluation means and maximum values increased between 0.2 and 0.3. The improved DeblurGAN is more effective in terms of both blur removal and detail recovery. [ABSTRACT FROM AUTHOR]

Background Malignant melanoma and non-melanoma skin cancers are among the fastest increasing malignancies in many countries. With the help of new tools, such as teledermoscopy referrals between primary health care and dermatology clinics, the management of these patients could be made more efficient. Objective To evaluate the diagnostic agreement and interobserver concordance achieved when assessing referrals sent through a mobile teledermoscopic referral system as compared to referrals sent via the current paper-based system without images. Methods The referral information from 80 teledermoscopy referrals and 77 paper referrals were evaluated by six Swedish dermatologists. They were asked to answer questions about the probable diagnosis, the priority, and a management decision. Results Teledermoscopy generally resulted in higher diagnostic agreement, better triaging and more malignant tumours being booked directly to surgery. The largest difference between the referral methods was seen for invasive melanomas. Referrals for benign lesions were significantly more often correctly resent to primary health care with teledermoscopy. However, referrals for cases of melanoma in situ were also incorrectly resent five times. The interobserver concordance was moderate with both methods. Conclusion By adding clinical and dermoscopic images to referrals, the triage process for both benign and dangerous skin tumours can be improved. With teledermoscopy, patients with melanoma especially can receive treatment more swiftly. [ABSTRACT FROM AUTHOR]

The switching power supply is widely used in the secondary equipment of the power grid. The failure of the switching power supply will cause the protection device to malfunction, leading to a risk of large‐scale power failure. In this paper, a real‐time monitoring method based on only output voltage information is proposed. The experiment shows that this parameter‐reduced approach, relying only on voltage information, outperforms the existing method in detection accuracy. [ABSTRACT FROM AUTHOR]

Microfluidic devices are miniaturized systems that manipulate fluids on a small scale, typically at microlitre or nanolitre volumes. They have received significant attention in various industries, including petroleum applications. The recent advancements in microfluidic devices for petroleum applications have the potential to improve oil recovery efficiency, reduce costs, and provide valuable insights into fluid behaviour and reservoir characterization. This paper aims to provide a comprehensive overview of microfluidic devices, their materials, and their applications in the petroleum industry. The first section of the paper focuses on describing the materials used in microfluidic devices specifically tailored for energy sector applications. In the second section, the paper highlights relevant applications and discoveries in petroleum research, showcasing the innovative techniques and special features enabled by microfluidic devices. These applications include but are not limited to asphaltenes characterization, enhanced oil recovery (EOR), and water treatment. The versatility and customization of microfluidic devices have allowed researchers to accurately represent fractures, ensure chemical conformance, simulate high pressure–high temperature conditions and reservoir heterogeneity, and study geochemical interaction. Additionally, molecular tagging and machine learning techniques have been employed for image analysis, further enhancing the capabilities of microfluidic devices. Throughout the paper, the advantages and disadvantages of implementing microfluidic devices and utilizing specific materials are thoroughly discussed. This analysis provides valuable insights into the challenges and potential limitations associated with this technology. To conclude, the paper offers suggestions, ideas, and highlights for future research paths, pointing toward promising directions for further exploration in this field. [ABSTRACT FROM AUTHOR]

In this paper, a probabilistic methodology based on image analysis is proposed for earthquake‐induced loss estimation in rectangular reinforced concrete shear walls (RCSWs) in compliance with FEMA P‐58. The methodology is developed using a databank of 285 surface crack patterns gathered from the result of experimental research on 99 rectangular RCSWs with wide‐ranging structural and geometric characteristics. For the complexity extraction, three fractal geometry indices of the damaged specimens are taken into consideration. Fragility functions are generated to determine the probability of exceeding a FEMA P‐58 compliant damage state based on the various methods of repair for specific complexity indices. The fragility functions are obtained using four common probabilistic distributions including lognormal, Weibull, gamma, and beta. The fitness of the fragility curves is examined by two goodness‐of‐fit tests, namely the Kolmogorov–Smirnov test and Lilliefors test. Results show that the Weibull is the best‐fitting distribution for the majority of damage states and succolarity possesses the best results in goodness‐of‐fit tests among fractal geometry indices. Empirical loss indices are also derived in this paper aimed at optimizing the goodness‐of‐fits. Finally, the proposed procedure is utilized for a sample specimen from the databank at various damage levels. [ABSTRACT FROM AUTHOR]

Medical image segmentation is a key step in medical image analysis. The small differences in the background and foreground of medical images and the small size of most medical data sets make medical segmentation difficult. This paper uses a global‐local training strategy to train the network. In the global structure, ResNest is used as the backbone of the network, and parallel decoders are added to aggregate features, as well as gated axial attention to adapt to small datasets. In the local structure, the extraction of image details is accomplished by dividing the images into equal patches of the same size. To evaluate the performance of the model, qualitative and quantitative comparisons were performed on five datasets, Kvasir‐SEG, CVC‐ColonDB, CVC‐ClinicDB, CVC‐300, and ETIS‐LaribPolypDB, and the segmentation results were significantly better than the current mainstream polyp segmentation methods. The results show that the model has better segmentation performance and generalization ability. [ABSTRACT FROM AUTHOR]

Deep learning has recently been widely used in medical image analysis due to its ability to learn complex features in images. An increasing number of deep learning methods has been devoted to the classification of chest X‐ray (CXR) images. Most of the existing deep learning methods are based on classical pretraining models trained by global chest X‐ray images. In this paper, we classify 14 different thorax diseases based on the RE‐DSENet model, which applies the image registration algorithm to the multi‐atlas segmentation model. The experimental results on the ChestX‐ray14 dataset show that the RE‐DSENet's classify‐cation precision of 14 thorax diseases is significantly improved compared with three existing classic algorithms, and the average AUC (area under the ROC curve) score is up to 0.857. RE‐DSENet can also reduce the computations in the classification network and improve the training efficiency. In addition, the focus areas diagnosed by the RE‐DSENet model are visualized in this paper, and the results further prove the effectiveness of the model. [ABSTRACT FROM AUTHOR]

This article focuses on issues related to optometry. Dichotic motion can be perceived when sinunusoidally flickering gratings which are out of phase, both in space and in time, are presented to each eye. Although neither eye's view allows correct direction of motion discrimination, the binocular percept is of a smoothly moving stimulus. The motion of the stimulus has, in effect, been spill between the two eyes. This suggests that early motion filter, can integrate binocularly. It can however also be interpreted as high-level binocular integration where observers track the features which emerge from low-level analyses applied to the two images separately.

Background: The past decade has seen a substantial rise in the employment of modern data‐driven methods to study atopic dermatitis (AD)/eczema. The objective of this study is to summarise the past and future of data‐driven AD research, and identify areas in the field that would benefit from the application of these methods. Methods: We retrieved the publications that applied multivariate statistics (MS), artificial intelligence (AI, including machine learning‐ML), and Bayesian statistics (BS) to AD and eczema research from the SCOPUS database over the last 50 years. We conducted a bibliometric analysis to highlight the publication trends and conceptual knowledge structure of the field, and applied topic modelling to retrieve the key topics in the literature. Results: Five key themes of data‐driven research on AD and eczema were identified: (1) allergic co‐morbidities, (2) image analysis and classification, (3) disaggregation, (4) quality of life and treatment response, and (5) risk factors and prevalence. ML&AI methods mapped to studies investigating quality of life, prevalence, risk factors, allergic co‐morbidities and disaggregation of AD/eczema, but seldom in studies of therapies. MS was employed evenly between the topics, particularly in studies on risk factors and prevalence. BS was focused on three key topics: treatment, risk factors and allergy. The use of AD or eczema terms was not uniform, with studies applying ML&AI methods using the term eczema more often. Within MS, papers using cluster and factor analysis were often only identified with the term AD. In contrast, those using logistic regression and latent class/transition models were "eczema" papers. Conclusions: Research areas that could benefit from the application of data‐driven methods include the study of the pathogenesis of the condition and related risk factors, its disaggregation into validated subtypes, and personalised severity management and prognosis. We highlight BS as a new and promising approach in AD and eczema research. [ABSTRACT FROM AUTHOR]

Power line insulator defect identification usually suffers from complex backgrounds, small defect target sizes, and inconspicuous defect features. Traditional identification methods based on image processing, image analysis, and pattern classification have many limitations in solving the aforementioned problems. In recent decades, deep learning classification methods have gradually replaced traditional identification methods in the task of power line insulator defect identification. To accurately identify the locations of insulator defects, this paper proposes an insulator defect detection algorithm using an improved lightweight YOLOv4‐tiny network (ILYTN). First, the CBL (Conv‐BN‐LeakyReLU) modules of the backbone network are replaced with MobileViT blocks to enhance the feature extraction capability of the backbone network. Second, coordinate attention (CA) is introduced in the feature fusion part to improve the network's ability to focus on the location of defects. Finally, an EIOU (efficient intersection over union) loss function, instead of the original CIOU loss function, is used so that the convergence speed of the network can be improved. To verify the effectiveness of the proposed algorithm in this paper, the mViT‐yolo algorithm is compared with the mainstream Faster‐RCNN algorithm, SSD algorithm, YOLOv3 algorithm, and YOLOv4‐tiny algorithm. The experimental results show that the algorithm proposed in this paper outperforms all the above algorithms in terms of detection accuracy. Compared with the traditional YOLOv4‐tiny algorithm, the proposed algorithm increases the mean average precision (mAP) by 1.64%, the average precision (AP) of missing insulator defects by 0.32%, and the average precision (AP) of broken insulator defects by 4.96%. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Commercial light field imaging systems that support photography, depth analysis, and other applications are becoming available. We summarize the development of light field photography over the past century, briefly address issues such as depth of field and final-image resolution, and identify future photographic and imaging opportunities. [ABSTRACT FROM AUTHOR]

Manual measurements of quantitative traits are time consuming and error prone. Therefore, high‐throughput phenotyping methods that allow a rapid and accurate assessment are vital to a growing range of researchers such as agronomists, breeders, phytopathologists, geneticists, ecologists and biologists.Here, I describe the pliman R package, a collection of functions designed (but not limited) to conduct plant image analysis. The package will help researchers to (a) manipulate, segment and compute image indexes based on Red, Green, Blue, Red‐Edge and Near‐Infrared bands; (b) measure leaf area and shape; (c) quantify plant disease severity; (d) count and extract features (e.g. area, perimeter, radius, circularity and eccentricity) of objects such as grains, leaves, pods, pollen and cells; and (e) compute RGB indexes for each object in an image.In this paper, I describe the main features implemented in the package, guiding the user along a gentle learning curve with practical and reproducible examples. The results of validation studies implemented to count objects in different scenarios (combination of seed sizes, background colour and image resolution), measure leaf area, and quantify plant disease severity have shown that pliman measurements are highly concordant with existing 'gold standard' tools.The pliman package offers a flexible, intuitive and richly documented working environment for image‐based phenotyping, being an interesting alternative to free and commercial 'point‐and‐click' solutions. R users will find the package fairly easy to use and will be surprised at how the setting of a few arguments will allow processing thousands of images while they enjoy a cup of coffee. [ABSTRACT FROM AUTHOR]

Cationic collectors are widely used in the flotation of iron ore, silicate, oxides, and so forth. At this stage, so much research has been carried out on flotation column cells, but more attention needs to be paid to the hydrodynamic properties in the presence of cationic collectors in cells. In order to study foaming performance and hydrodynamic characteristics in the presence of GE609, the bubble characteristics are studied in an experimental scale flotation column. A high‐speed camera recorder is used to take bubbles' images, and the bubble size and shape under different conditions is measured by image analysis. This paper aims to explore the effect of dispersed gas and continuous liquid velocities on the bubble size and aspect ratio at different axial heights in a countercurrent flotation column in the presence and absence of GE609. In the systems, the bubble size distributions and shapes at different axial positions were obtained by using high‐speed camera technology in the superficial gas velocity (Ug) ranges of 0.0021–0.0106 m/s and superficial liquid velocity (UL) ranges of 0.00123–0.00209 m/s. Through the comparison with relevant papers and studying the experiment data, the relationship between the Sauter mean diameter and Ug, UL and axial heights have been analyzed by using multiple linear regression and dimensional analysis. The experimental data have been adopted to establish the correlation between the bubble diameter and the dimensionless array, which have been compared with other literature. And the predicted values/results of Weber and Morton number correlation in this work are relatively close to the experimental values. [ABSTRACT FROM AUTHOR]

Motivation: Medical image analysis involves a series of tasks used to assist physicians in qualitative and quantitative analyses of lesions or anatomical structures which can significantly improve the accuracy and reliability of medical diagnoses and prognoses. Traditionally, these tedious tasks were finished by experienced physicians or medical physicists and were marred with two major problems, low efficiency and bias. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models, attempts to use reinforcement learning in medical image analysis are still scarce. We hope that this review article could serve as the stepping stone for related research in the future. Significance: We found that although reinforcement learning has gradually gained momentum in recent years, many researchers in the medical analysis field still find it hard to understand and deploy in clinical settings. One possible cause is a lack of well‐organized review articles intended for readers without professional computer science backgrounds. Rather than to provide a comprehensive list of all reinforcement learning models applied in medical image analysis, the aim of this review is to help the readers formulate and solve their medical image analysis research through the lens of reinforcement learning. Approach & Results: We selected published articles from Google Scholar and PubMed. Considering the scarcity of related articles, we also included some outstanding newest preprints. The papers were carefully reviewed and categorized according to the type of image analysis task. In this article, we first reviewed the basic concepts and popular models of reinforcement learning. Then, we explored the applications of reinforcement learning models in medical image analysis. Finally, we concluded the article by discussing the reviewed reinforcement learning approaches' limitations and possible future improvements. [ABSTRACT FROM AUTHOR]

With the UK government's target of Net Zero by 2050, alongside the rising cost of energy in the UK, it is imperative that public opinion aligns with and promotes affordable, greener energy systems. Within this dialogue, young people's voices and lived experience are needed to deepen the impact of energy policy intervention strategies. This article stresses the importance of a formal curriculum that informs future generations on the subjects of Smart Local Energy Systems (SLES), and the digitalisation advances of energy systems. This paper explores the development of a toolkit to educate and engage young people in these subjects and reviews young people's attitudes and understanding from three schools in England, UK. We find through an interpretive qualitative analysis of both images drawn by the young people, and insights gathered from focus groups, that (i) for these young people, SLES are a useful innovation regarding energy management and distribution, (ii) young people recognise that the education system as it currently stands is not aware of, or informed by smart energy technologies and (iii) that despite being a generation that is the most integrated with personal assistance technology, these young people have significant concerns over the reliability and safety of AI in SLES. These findings are set alongside calls for widespread education in key stages 4 and 5 (ages 14–18) around the subject of SLES, and their benefits to wider society, thereby enabling a future that empowers young people to make change, encourage engineering and computer science‐based career options and secure a fairer, cleaner, sustainable energy transition in the future. [ABSTRACT FROM AUTHOR]

Background: Computed tomography (CT) relies on the attenuation of x‐rays, and is, hence, of limited use for weakly attenuating organs of the body, such as the lung. X‐ray dark‐field (DF) imaging is a recently developed technology that utilizes x‐ray optical gratings to enable small‐angle scattering as an alternative contrast mechanism. The DF signal provides structural information about the micromorphology of an object, complementary to the conventional attenuation signal. A first human‐scale x‐ray DF CT has been developed by our group. Despite specialized processing algorithms, reconstructed images remain affected by streaking artifacts, which often hinder image interpretation. In recent years, convolutional neural networks have gained popularity in the field of CT reconstruction, amongst others for streak artefact removal. Purpose: Reducing streak artifacts is essential for the optimization of image quality in DF CT, and artefact free images are a prerequisite for potential future clinical application. The purpose of this paper is to demonstrate the feasibility of CNN post‐processing for artefact reduction in x‐ray DF CT and how multi‐rotation scans can serve as a pathway for training data. Methods: We employed a supervised deep‐learning approach using a three‐dimensional dual‐frame UNet in order to remove streak artifacts. Required training data were obtained from the experimental x‐ray DF CT prototype at our institute. Two different operating modes were used to generate input and corresponding ground truth data sets. Clinically relevant scans at dose‐compatible radiation levels were used as input data, and extended scans with substantially fewer artifacts were used as ground truth data. The latter is neither dose‐, nor time‐compatible and, therefore, unfeasible for clinical imaging of patients. Results: The trained CNN was able to greatly reduce streak artifacts in DF CT images. The network was tested against images with entirely different, previously unseen image characteristics. In all cases, CNN processing substantially increased the image quality, which was quantitatively confirmed by increased image quality metrics. Fine details are preserved during processing, despite the output images appearing smoother than the ground truth images. Conclusions: Our results showcase the potential of a neural network to reduce streak artifacts in x‐ray DF CT. The image quality is successfully enhanced in dose‐compatible x‐ray DF CT, which plays an essential role for the adoption of x‐ray DF CT into modern clinical radiology. [ABSTRACT FROM AUTHOR]

Background: Automatic segmentation of vertebrae in spinal x‐ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions. Purpose: However, due to the inherent characteristics of x‐ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer‐aided spine image analysis and disease diagnosis applications. Methods: In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x‐ray images, to aid doctors in diagnosing spinal‐related diseases. To enhance feature extraction, the network incorporates a Dual‐branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual‐branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task‐irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high‐level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability. Results: In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x‐ray images compared to other advanced methods, achieving 92.61 ± 0.431 for MIoU, 92.42 ± 0.329 for DSC, and 99.51 ± 0.037 for Global_accuracy. Conclusions: Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions. [ABSTRACT FROM AUTHOR]

ABSTRACT Accurate three‐dimensional (3D) reconstruction of granular grains from x‐ray micro‐computed tomography (µCT) images is a long‐standing challenge, particularly for dense soil samples. This study develops a machine learning (ML) enhanced approach to automatically reconstruct granular grains from µCT images. The novel academic contributions of this paper include (a) a hierarchical strategy based on parameter‐independent polygonal approximation, area, and concavity analysis, for the first time, to identify and eliminate both intergranular and intragranular voids; (b) incorporation of a recursive segmentation scheme and ML‐based grain classifier to avoid over‐segmentation; (c) novel modifications on the determination of splitting paths to enhance segmentation accuracy; and (d) an effective approach of assigning initial level set functions for reconstructing granular grains automatically. The hybrid ML algorithm is applied to µCT images of dense Mojave Mars Simulant. The results indicate that the proposed method can accurately segment grain clumps with unclear boundaries. The new automatic reconstruction algorithm eliminates ineffective operations and achieves a three‐fold increase in computational speed than previous methods documented in the literature. Ninety‐one percent of grains with distinct boundaries can be reconstructed and the reconstruction ratio reaches 81% even for grains without distinct boundaries. The overall reconstruction ratio of grains increases by 20% compared with previous methods, achieving a step‐change improvement for one‐to‐one mapping of real soil samples. [ABSTRACT FROM AUTHOR]

The influence of circulating liquid velocity on bubble size distribution (BSD) and turbulence characteristics of an external loop airlift reactor was studied in this paper. The instantaneous and time‐averaged velocity in the riser were studied using particle image velocimetry (PIV), and BSDs were measured by digital image analysis technique based on machine learning. Then turbulence kinetic energy (TKE) and energy dissipation rate (EDR) were calculated through the velocity field. The results indicate that as the circulating liquid velocity increased, the peak value of BSD rapidly decreased from nearly 6 mm to approximately 2 mm. The radial velocity of the liquid gradually decreased and changed direction, eventually increasing again. TKE first decreased and then increased. Compared with bubble flow with a BSD peak of 2–6 mm, bubble flow with a BSD peak of 2 mm had larger TKE. The radial movement of bubbles had great influence on the turbulence characteristics. This study demonstrates that selecting an appropriate circulating liquid velocity can reduce the diameter of bubbles while obtaining greater TKE, thereby improving the mass transfer and reaction efficiency in external loop airlift reactors (EL‐ALR). [ABSTRACT FROM AUTHOR]

Diabetic retinopathy (DR) poses a serious threat to vision, emphasising the need for early detection. Manual analysis of fundus images, though common, is error‐prone and time‐intensive. Existing automated diagnostic methods lack precision, particularly in the early stages of DR. This paper introduces the Soft Convolutional Block Attention Module‐based Network (Soft‐CBAMNet), a deep learning network designed for severity detection, which features Soft‐CBAM attention to capture complex features from fundus images. The proposed network integrates both the convolutional block attention module (CBAM) and the soft‐attention components, ensuring simultaneous processing of input features. Following this, attention maps undergo a max‐pooling operation, and refined features are concatenated before passing through a dropout layer with a dropout rate of 50%. Experimental results on the APTOS dataset demonstrate the superior performance of Soft‐CBAMNet, achieving an accuracy of 85.4% in multiclass DR grading. The proposed architecture has shown strong robustness and general feature learning capability, achieving a mean AUC of 0.81 on the IDRID dataset. Soft‐CBAMNet's dynamic feature extraction capability across all classes is further justified by the inspection of intermediate feature maps. The model excels in identifying all stages of DR with increased precision, surpassing contemporary approaches. Soft‐CBAMNet presents a significant advancement in DR diagnosis, offering improved accuracy and efficiency for timely intervention. [ABSTRACT FROM AUTHOR]

Medical image labeling requires specialized knowledge; hence, the solution to the challenge of medical image classification lies in efficiently utilizing the few labeled samples to create a high‐performance model. Building a high‐performance model requires a complicated convolutional neural network (CNN) model with numerous parameters to be trained which makes the test quite expensive. In this paper, we propose optimizing a lightweight deep learning model with only five convolutional layers using the particle swarm optimization (PSO) algorithm to find the best number of kernel filters for each convolutional layer. For colored red, green, and blue (RGB) images acquired from different data sources, we suggest using stain separation using color deconvolution and horizontal and vertical flipping to produce new versions that can concentrate the representation of the images on structures and patterns. To mitigate the effect of training with incorrectly or uncertainly labeled images, grades of disease could have small variances, we apply a second‐pass training excluding uncertain data. With a small number of parameters and higher accuracy, the proposed lightweight deep learning model optimization (LDLMO) algorithm shows strong resilience and generalization ability compared with most recent research on four MedMNIST datasets (RetinaMNIST, BreastMNIST, DermMNIST, and OCTMNIST), Medical‐MNIST, and brain tumor MRI datasets. [ABSTRACT FROM AUTHOR]

The skin, a crucial organ, plays a protective role in the human body, emphasizing the significance of early detection of skin diseases to prevent potential progression to skin cancer. The challenge lies in diagnosing these diseases at their early stages, where visual resemblance complicates differentiation, highlighting the need for an innovative automated method for precisely identifying skin lesions in biomedical images. This paper introduces a holistic methodology that combines DenseNet, multi‐scale feature boundary module (MFBM), and feature fusion and decoding engine (FFDE) to tackle challenges in existing deep‐learning image segmentation methods. Furthermore, a convolutional neural network model is designed for the classification of segmented images. The DenseNet encoder efficiently extracts features at four resolution levels, leveraging dense connectivity to capture intricate hierarchical features. The proposed MFBM plays a crucial role in extracting boundary information, employing parallel dilated convolutions with various dilation rates for effective multi‐scale information capture. To overcome potential disadvantages related to the conversion of features during segmentation, our approach ensures the preservation of context features. The proposed FFDE method adaptively fuses features from different levels, restoring skin lesion location information while preserving local details. The evaluation of the model is conducted on the HAM10000 dataset, which consists of 10 015 dermoscopy images, yielding promising results. [ABSTRACT FROM AUTHOR]

In response to increasing power supply needs, maintaining stable substations is vital for reliable electricity. Traditional manual equipment inspections in these substations are inefficient and risky, often leading to hazards and delayed detection of faults. Therefore, there's a growing shift towards using intelligent image recognition technology in video surveillance systems for safer and more efficient inspections. This paper focuses on enhancing the level of intelligent inspection in substations using artificial intelligence‐based visual recognition technology. It introduces a novel small‐sample classification algorithm based on the CLIP architecture. This method uses cross‐modal equipment status information as additional training samples, optimizing the loss function together with image samples, and devises hand‐crafted strategies for text sample inputs to distinguish between equipment and states. The experimental results show that with only 16 training samples per category for 21 types of electrical equipment states, our method achieved a maximum accuracy of 93.38%. This represents a 2.98% higher accuracy than the PPLCNet trained on the full dataset and an 8.63% higher accuracy than the PPLCNet trained with an equal number of samples, with significantly reduced training time. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI‐based tumor subregion analysis in medical imaging. We summarize the latest AI‐based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI‐based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential applications of AI in tumor subregion analysis are discussed. [ABSTRACT FROM AUTHOR]

Image binarization algorithms in document image analysis divide pixel values into two groups, including white as background and black as foreground. Among others, the local contrast and mean (LCM)‐based thresholding algorithm offers excellent performance in processing degraded documents. This algorithm, however, is susceptible to noise and requires significant hardware resources. In this paper, an energy‐efficient and fault‐tolerant architecture is proposed for implementing the LCM algorithm in stochastic computing (SC). Leveraging correlated input bitstreams, this architecture saves energy and improves the fault tolerance of the implementation. Experimental results show that the proposed LCM stochastic architecture significantly outperforms the stochastic implementation of the Sauvola algorithm in terms of both binarization accuracy and hardware overhead and energy consumption. Even using 16‐bit streams, the proposed circuit produces an error rate lower than 5%. The stochastic implementation of the LCM algorithm using a 16‐bit length FSM‐based LD sequence is 22 times less in area, 26 times less in total power, 28 times less in energy consumption and more fault‐tolerant than the conventional 8‐bit bit‐width weighted binary with the same frequency constraints. [ABSTRACT FROM AUTHOR]

The physicochemical events at ink intersections are largely understudied. Chemical imaging techniques and multivariate analyses applied to this problem thus far lack the performance characteristics to make confident conclusions about these processes. This deficiency leads to subjective and controversial deposition order determinations in forensic investigations. In this comprehensive report, 44 unique crossings involving laser toners and stamps, as well as felt‐tip, rollerball, gel, and ballpoint pen inks, were imaged with X‐ray photoelectron spectroscopy (XPS), using a 50‐μm X‐ray spot size and 100‐μm steps. The specificity and surface sensitivity of XPS enabled the objective visualization of the inks' chemistry upon deposition via spatial elemental distribution in‐situ. Signal intensity and atomic concentration were mapped for each element detected. Discrimination was possible in 100% of written and printed inks analyzed, and the relationship between the elemental concentration profile in each intersection was compared with the known sequence. Formation of ink layers, mixing, and separation of ink components by way of electrostatic forces were observed. Insights into the known complexity of ink intersection processes were revealed and highlight the need for understanding the chosen analytical technique's information depth and for complementary analyses to increase sequencing confidence. We also provide evidence that these processes invalidate the utility of principal component analysis for analyzing ink intersections. However, sequencing accuracy was 84% for all samples analyzed, and its success was highly dependent on the presence of physical features (i.e., thick coatings and embossed regions). In some intersection types, especially toners, the features exposed with XPS imaging enabled a sequencing accuracy of 100%. [ABSTRACT FROM AUTHOR]

Image analysis presents a set of powerful methods to receive additional information about multiphase processes. It enables the development of advanced applications for process monitoring and optimization or, so‐called, soft sensors. However, the integration of advanced smart sensor systems based on image analysis into the process control system presents a complex task. To address this challenge, a modular automation concept offers a standardized interface to integrate modules. This paper presents an integration profile as a service specification that allows a plug‐and‐measure integration of smart visual sensors into modular plants. To verify the concept, we applied it to three different use cases. At the end, we discuss open challenges in the integration of complex analysis systems with multidimensional data streams into modular plants. [ABSTRACT FROM AUTHOR]

Ultrasonography can be used for canine pregnancy diagnosis, determination of gestational age, assessing foetal maturation and readiness for birth, monitoring high‐risk pregnancies, assessing foetal distress, evaluating bitches in dystocia and foetal gender determination. As the quality and resolution of ultrasound machines have improved, the clinician's abilities to utilize ultrasound as an integral part of reproductive evaluation of all aspects of pregnancy have exponentially increased. This paper reviews the use of ultrasonography throughout pregnancy and the advances made in the interpretation of captured images. [ABSTRACT FROM AUTHOR]

As soon as the SARS‐Cov2 disease was recognized by experts to potentially cause a serious pandemic, a three dimensional diagrammatic image of the virus, colored in strong red, conquered public media globally. This study confronts this iconic virus image with a historic image analysis of 33,000 biomedical articles on coronaviruses published between 1968–2020 and interviews with some of their authors. Only a small fraction of scientific virus publications entail images of the complete virus. Red as an alarm color is not used at all by scientists who don't aim for a non‐scientific public. Circulation in this case concerns the movement of iconic images from a scientific context into a general public. On the basis of hps‐studies on scientific diagrams and especially on color use in scientific diagrams to convey specific messages in public, the paper discusses the role of the claim of public corona‐virus diagram as "scientific." It points at relevant differences between most frequent scientific corona‐virus images and the diagrammatic image used in public. Both author‐ and readerships (in science and public) follow contrasting aims and values. Thus, the images meet non‐expert readers for whom the images entail very different – and potentially unintended – meanings then to virus experts. [ABSTRACT FROM AUTHOR]

Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes Development of a Phantom for Tomosynthesis with Potential for Automated Analysis via the Cloud. Several studies are underway to investigate the effectiveness of Tomosynthesis Mammographic Image Screening, including the large TMIST project as funded by the National Cancer Institute https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/tmist. The development of the phantom described in this paper follows initiatives from the FDA, the AAPM TG245 task group, and European Reference Organization (EUREF) for Quality Assured Breast Screening and Diagnostic Services Committee report noting, that no formal endorsement nor recommendation for use has been sought, or granted by any of these groups. This paper reports on the possibility of using this newly developed Tomosynthesis Phantom for Quality Assurance, field testing of image performance, including remote monitoring of DBT system performance, e.g., via transmission over the cloud. The phantom includes tests for: phantom positioning and alignment (important for remote analysis), scan geometry (x and y), chest wall offset, scan slice width and Slice Sensitivity Profile (SSP(z)) slice geometry (slice width), scan slice incrementation (z), z axis geometry bead, low contrast detectability using low contrast spheres, spatial resolution via Point Spread Function (PSF), Image uniformity, Signal to Noise Ratio (SNR), and Contrast to Noise Ratio (CNR) via readings over an Aluminum square. The phantom is designed for use with automated analysis via transmission of images over the cloud and the analysis package includes test of positioning accuracy (roll, pitch, and yaw). Data are shown from several commercial Tomosynthesis Scanners including Fuji, GE, Hologic, IMS‐Giotti, and Siemens; however, the focus of this paper is on phantom design, and not in general aimed at direct commercial comparisons, and wherever possible the identity of the data is anonymized. Results of automated analysis of the phantom are shown, and it is demonstrated that reliable analysis of such a phantom can be achieved remotely, including transmission of data through the cloud. [ABSTRACT FROM AUTHOR]