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The dimensional absorbency properties of paper towels were studied, focusing on three- and two-dimensional absorption mechanisms. Key factors affecting these absorption mechanisms were identified through a series of experiments and principal component analysis (PCA). The results showed that the water absorption capacity, driven by capillary action (porosity), exhibited differences between two-dimensional surface absorption (in the X and Y directions) and three-dimensional bulk absorption (including the Z direction, or thickness). Porosity analysis revealed that three-dimensional absorbency is highly correlated with porosity, whereas two-dimensional absorbency has a relatively low correlation and is influenced by fiber properties such as length and width, as well as mass-related characteristics including fines content and freeness. The findings highlight the need to balance these dimensional properties to achieve optimal absorbency in paper towel products. Additionally, this study provides a foundation for developing more efficient paper towels and offers valuable insights into the complex mechanisms of paper towel absorbency, which will aid in the development of improved hygiene paper products.

Curl propensity is a critical-to-quality (CTQ) property of paper, as it causes severe problems during printing and other final conversion operations. The main papermaking factor causing the curl phenomenon is the existence of a fiber orientation (FO) gradient across the thickness direction (or ZD), also known as two-sidedness. Therefore, a methodology that characterizes the FO across the ZD is fundamental for papermakers. In this work, we propose and validate an efficient and cost-effective protocol based on sheet splitting and image analysis. Besides assessing the level of FO two-sidedness, the methodology also provides insights into the flow dynamics in the draining zone of the forming section of the paper machine and the drying stresses built into the paper. This information is relevant for monitoring, optimizing, and troubleshooting activities in the paper industry. [ABSTRACT FROM AUTHOR]

One of the most popular and well-established forms of payment in use today is paper money. Handling paper money might be challenging for those with vision impairments. Assistive technology has been reinventing itself throughout time to better serve the elderly and disabled people. To detect paper currency and extract other useful information from them, image processing techniques and other advanced technologies, such as Artificial Intelligence, Deep Learning, etc., can be used. In this paper, we present a meticulously curated and comprehensive dataset named ‘NSTU-BDTAKA’ tailored for the simultaneous detection and recognition of a specific object of cultural significance - the Bangladeshi paper currency (in Bengali it is called ‘Taka’). This research aims to facilitate the development and evaluation of models for both taka detection and recognition tasks, offering a rich resource for researchers and practitioners alike. The dataset is divided into two distinct components: (i) taka detection, and (ii) taka recognition. The taka detection subset comprises 3,111 high-resolution images, each meticulously annotated with rectangular bounding boxes that encompass instances of the taka. These annotations serve as ground truth for training and validating object detection models, and we adopt the state-of-the-art YOLOv5 architecture for this purpose. In the taka recognition subset, the dataset has been extended to include a vast collection of 28,875 images, each showcasing various instances of the taka captured in diverse contexts and environments. The recognition dataset is designed to address the nuanced task of taka recognition providing researchers with a comprehensive set of images to train, validate, and test recognition models. This subset encompasses challenges such as variations in lighting, scale, orientation, and occlusion, further enhancing the robustness of developed recognition algorithms. The dataset NSTU-BDTAKA not only serves as a benchmark for taka detection and recognition but also fosters advancements in object detection and recognition methods that can be extrapolated to other cultural artifacts and objects. We envision that the dataset will catalyze research efforts in the field of computer vision, enabling the development of more accurate, robust, and efficient models for both detection and recognition tasks.

Dried blood spots (DBSs) are formed by collecting a small sample of blood on specialized filter paper and allowing it to dry naturally. Various domains of life sciences and drug research extensively use DBSs as a sampling technique. The "Hematocrit (Ht) effect" affects assay bias, and several strategies have been put forth to deal with it, including the correction of quantified concentrations using an appropriate correction factor. The approach was previously applied, following the utilization of an image processing algorithm developed in Matlab® to derive a reliable equation correlating DBS areas to Ht% values. The present work looks more closely at the application of image analysis to the evaluation of Ht in DBS samples. Utilizing image analysis software, DBS samples with known Ht values were processed. Preparation of cards has followed a previously developed protocol for the appropriate formation of uniform area DBSs, irrespective of Ht. The resulting areas showed close resemblance to the respective theoretical areas calculated by applying the correlation equation. Following that, the equation was utilized to determine the Ht values for each sample, and a comprehensive comparison of measured versus calculated Ht was carried out using various statistical approaches for method comparison. The results demonstrated a strong correlation, suggesting the method's viability in estimating Ht for unknown DBS samples. [ABSTRACT FROM AUTHOR]

The evaluation of wrinkles in deep-drawn fiber-based materials is crucial for the assessment of product quality and the optimization of manufacturing processes. Wrinkling is a common phenomenon in the deep-drawing process and is caused by tangential compressive stresses on the flanges of the blank. This phenomenon is particularly prevalent for fiber-based materials with high tensile depths and can seriously affect the appearance and mechanical properties of the final product. The objective of this study is to identify the key process parameters affecting wrinkling and to deepen the understanding of their roles and interactions using wrinkle data for deep-drawn paper products. Image analysis techniques are employed, supported by a specially constructed darkroom platform to ensure uniform light intensity for capturing photographs. An automated program is developed for the detection and evaluation of wrinkle characteristics and distribution, which allows the free choice of the region to be detected and the representation of the wrinkle geometry not limited by the number. To enhance the precision of this program, the ellipticity is initially rectified for products without flanges, specifically cup-shaped deep-drawn products. The ellipticity is caused by the pronounced springback effect of the paperboard. The approach is employed to investigate the impact of material properties, blank holder force, drawing depth, drawing clearance, and punch speed on wrinkling formation after the deep-drawing process. The findings reveal that the blank holder force and drawing clearance are critical factors in wrinkle formation, with higher blank holder force generally leading to increased wrinkle numbers. [ABSTRACT FROM AUTHOR]

We studied the feasibility of image-based quantitative analysis of foxing stains on collections of old (16th−20th century) European books stored in the Rare Book Library of the Seoul National University in Korea. We were able to quantitatively determine the foxing affected areas on books from their photographs using a newly developed image processing software (PicMan) including cultural property characterization applications, specifically. Dimensional and color analysis of photographs were successfully done quantitatively. Histograms of RGB (red, green, blue) pixels of photographs clearly showed the change in color distribution of foxing stains compared to the other areas of the photographs. Several sample images of quantitative measurement of foxing stains and virtually restored images were generated to provide easy visual inspection and comparison between restored images and the original photographs. Image quality, resolution, and digital file format requirements for quantitative analysis are described. Image-based quantitative analysis of foxing stains on paper documents are found to be very promising towards automation for objective characterization of photographs of cultural properties. This technique can be used to create a cultural property digital database. Quantitative and statistical analysis techniques can be introduced to monitor the effect of storage and conservation environment on the cultural properties.

During the storage of celluloid pictures for animation films over half a century, an interleave paper adhered to acrylic paint. The purpose of this study is to establish a methodology to cleanly remove the paper from the paint. A layered film, a replica of the celluloid pictures, adhered with paper was prepared and immersed in water or ethanol. The effect of these solvents on the peeling behavior was investigated using a peel test. The maximum peel force for the dry layered film in was distributed at ~0.5 N, independently of the peel speed. The peel force was significantly reduced after the layered film was immersed in pure water or ethanol. A morphological observation revealed that the dry paper was peeled off via the cohesive failure of the paper. After the layered film was immersed in pure water, the paper was also peeled off via cohesive failure. The layered film immersed in ethanol was peeled off at the paper/paint interface. To clear the effect of the volume change in the paint on peel behavior, the relative volume was determined via image analysis. The relative volume of paint was 1.56 in pure water and 1.37 in ethanol. It can be considered that the large difference in the volume of paint induces a large shear stress at the paint/paper interface. [ABSTRACT FROM AUTHOR]

Flow and spontaneous imbibition phenomena in porous media are important for various industrial applications, including printing and medical lateral flow assays. Their quantitative characterization is important to better understand and select the appropriate raw materials. However, standard methods often require time-consuming tests, and/or expensive equipment. Different time scales must be considered, limiting the range of possible characterization tools. A novel experimental approach based on image analysis for characterizing spontaneous imbibition processes is presented. Hence, ultra-fast diffusion may be quantitatively characterized. Models are issued from the literature to consider physical phenomena at small (milliseconds) and medium range (seconds) of time scales. The obtained experimental data fit with theoretical results, providing valuable insights into the understanding of fluid flow behavior at different time scales. Furthermore, the identification of some physical properties for either the fluid, or the substrate, based on the theoretical models are possible, as the contact angle, which remains to be otherwise challenging. This study contributes to bridging the gap between spontaneous imbibition and capillary phenomena at different time scales, their modeling, and a characterization of material and/or fluid properties paving the way for enhanced understanding and control of fluid behavior in porous media. Different papers are considered to illustrate the method. [ABSTRACT FROM AUTHOR]

We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients. [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]

Aging infrastructure assets, including buildings, require efficient rehabilitation to sustain services. However, current rehabilitation planning is a complex process that is challenged by strict budgets and the difficulty of identifying and delivering the assets that are most worthy of rehabilitation. Existing methods struggle not only with collecting inspection data but also with how to use these data to allocate the limited rehabilitation funds efficiently across the asset portfolio. This paper introduces a smart infrastructure rehabilitation framework that integrates inspection, fund allocation, and scheduling phases. Focusing on roofing elements, the proposed framework uses four novel components: (1) asset image analysis to quantify the size of sustained damage; (2) data-mining to further analyze the asset condition; (3) clustering and optimization to shortlist the assets that are most worthy of rehabilitation under a strict budget; and (4) repetitive scheduling to generate timely delivery plans for the budgeted rehabilitation works. The model was applied to the Toronto District School Board asset portfolio (300+ schools), and the model identified the buildings in most need of rehabilitation given a strict budget ($2.3 million) and offer a schedule for the efficient delivery of the required rehabilitation work within a 45-day period of the school's summer break. Although this paper focuses on built-up building roofs, the framework is scalable to other types of assets. [ABSTRACT FROM AUTHOR]

Paper used in the printing industry generally contains a relatively thin porous coating covering a thicker fibrous base layer. The three-dimensional pore structure of coatings has a major effect on fluid flow patterns inside the paper medium. Understanding and quantifying the flow properties of thin coating layers is hence crucial. Pore spaces within the coating have an average size of about 180 nm. We used scanning electron microscopy combined with focused ion beam (FIB-SEM) to visualize the nano-scale pore structure of the paper coating layer. Post-processing of the FIB-SEM images allowed us to reconstruct the three-dimensional pore space of the coating. The 3D FIB-SEM images were analyzed in detail to obtain pore size distribution and porosity value. The permeability was estimated using the GeoDict software, based on solutions of the Stokes equation. By determining the porosity and permeability of increasingly larger domain sizes, we estimated the size of a representative elementary volume (REV) for the coating layer to be 60 µm 3 , which is well within the volume analyzed using FIB-SEM. The estimated porosity and permeability of the REV domain were 0.34 and 0.09 mDarcy, respectively. Using the pore morphology method, capillary pressure-saturation ( P c - S ) and relative permeability curves of the REV domain could be constructed next. The P c -S curves showed that the coating had a high air entry suction, which is very favorable for printing in that ink will invade the coating as soon as it is applied to the coating. Our results are essential for macroscale modelling of ink penetration into a coating layer during inkjet printing. Macroscopic models can be valuable tools for optimization of the penetration depth and the spreading of ink on and within paper substrates. [ABSTRACT FROM AUTHOR]

Medical imaging refers to the process of obtaining images of internal organs for therapeutic purposes such as discovering or studying diseases. The primary objective of medical image analysis is to improve the efficacy of clinical research and treatment options. Deep learning has revamped medical image analysis, yielding excellent results in image processing tasks such as registration, segmentation, feature extraction, and classification. The prime motivations for this are the availability of computational resources and the resurgence of deep convolutional neural networks. Deep learning techniques are good at observing hidden patterns in images and supporting clinicians in achieving diagnostic perfection. It has proven to be the most effective method for organ segmentation, cancer detection, disease categorization, and computer-assisted diagnosis. Many deep learning approaches have been published to analyze medical images for various diagnostic purposes. In this paper, we review the work exploiting current state-of-the-art deep learning approaches in medical image processing. We begin the survey by providing a synopsis of research works in medical imaging based on convolutional neural networks. Second, we discuss popular pretrained models and general adversarial networks that aid in improving convolutional networks' performance. Finally, to ease direct evaluation, we compile the performance metrics of deep learning models focusing on COVID-19 detection and child bone age prediction. [ABSTRACT FROM AUTHOR]

The formation index of filter paper is one of the most important characteristics used in industrial quality control. Its estimation is often based on subjective comparison chart rating or, even more objective, on the power spectrum of the paper structure observed on a transmission light table. It is shown that paper formation can be modeled as Gaussian random fields with a well defined class of correlation functions, and a formation index can be derived from the density of the Bartlett spectrum estimated from image data. More precisely, the formation index is the mean of the Bessel transform of the correlation taken for wave lengths between 2 and 5 mm.

The article presents a new technique for analyzing phenomena occurring during the measurement of the strength properties of paper in the conditions of compression of the tested samples with forces acting in the paper plane. The technique is based on collecting data on the current distance of the clamps holding the tested sample and the force exerted on the sample using a universal testing machine and on the simultaneous recording of image sequence of the sample during the measurement. Next, the resulting images are subjected to processing and analysis, the purpose of which is to extract information about the shape of the sample edge in all phases of the measurement. Its advantage is the ability to determine the deflection arrow of the sample and describe its shape using the selected function given by the analytical parametric formula. It will be helpful in further research on the development of an analytical model describing the phenomena occurring during paper compression, and a method to determine the mechanism of paper destruction and the corresponding maximum force that destroys a paper sample. [ABSTRACT FROM AUTHOR]

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation. [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.

Plant root system architecture (RSA) has an important role in crop production, particularly for water and nutrient uptake under limiting conditions. In the last few years, several root phenotyping methods have been developed. Here we present a new technique which has been developed for non-destructive, inexpensive and high-throughput root growth studies and RSA analyses. To illustrate the potential applications, this method was tested in an experiment with nitrogen and phosphorous deficiencies in a nutrient solution, affecting RSA parameters of two spring barley varieties (Bojos and Barke). This technique is based on root growth on vertically positioned black filter paper (30 × 60 cm) placed between two black plastic (PVC-P) foils and micro-irrigation systems providing the recirculation of nutrient solution. The pre-germinated seeds were placed in the slit between two plastic bars which carry the filter paper and plastic sheets and fix the plant in the vertical position. This system allows easy repeated non-invasive access to roots for their measuring and sampling. Eighteen days after transplanting the root imaging was done using an RGB digital camera. To evaluate the root architecture parameters the "SmartRoot" software was used. The results revealed that the system is able to detect changes in RSA which are caused mainly by P deficiency (particularly changes in lateral root length and total root area). It can be concluded that this technique has a great potential for non-destructive root growth studies, RSA measurement and root sampling.

The study was conducted at the University of Nebraska Pesticide Application and Technology Laboratory in North Platte, Nebraska in July 2015. Two application volume rates (100 and 200 l · ha-1) and three nozzle types (XR, AIXR, TTI) were selected at two flow rates (0.8 and 1.6 l · min-1) and at a single application speed of 7.7 km · h-1. Each collector type [Mylar washed (MW), Mylar image analysis (MIA), water-sensitive paper (WSP), and Kromekote (KK)] was arranged in a randomized complete block design. Each nozzle treatment was replicated twice, providing six cards of each collector type for each nozzle treatment. A water + 0.4% v/v Rhodamine WT spray solution was applied, given the fluorescent and visible qualities of Rhodamine, which allows it to be applied over all the collector types. MW had the highest coverage at 18.3% across nozzle type, followed by WSP at 18%, KK at 12% and lastly by MIA at 4%. MW resulted in a 58% increase in coverage, WSP in a 56% increase, and KK only an increase of 39% when the volume rate was doubled from 100 l · ha-1 to 200 l · ha-1 across nozzle type. MW coverage was similar to KK for half of the nozzles (XR 11002, XR 11004, AIXR 11002). Droplet number density fixed effects were all significant for nozzle type and collector type (p < 0.001) as was the interaction of nozzle type and collector type (p < 0.001). Results from this study suggest a strong correlation to data produced with WSP and MW collectors, as there was full agreement between both types except for the TTI 11004. Using both collector types in the same study would allow for a visual understanding of the distribution of the spray, while also giving an idea of the concentration of that distribution. [ABSTRACT FROM AUTHOR]

In recycling, waste papers are segregated into various grades as they are subjected to different recycling processes. Highly sorted paper streams facilitate high quality end products and save processing chemicals and energy. Automated paper sorting systems offer significant advantages over human inspection in terms of worker fatigue, throughput, speed, and accuracy. As a consequence, many automated mechanical and optical paper sorting methods have been developed to fill the paper sorting demand during 1932 to 2009. Because of inadequate throughput and some major drawbacks of mechanical paper sorting systems, the popularity of optical paper sorting systems has increased. The implementation of the previous methods, while being a step forward in the large-volume automated sorting technology, is still complex, expensive and sometimes offers limited reliability. This research attempts to develop a smart vision sensing system that is able to separate the different grades of paper using chromaticity. For constructing template database, hue and saturation of the paper object image in a selected area are considered. The paper grade is identified based on the maximum occurrence of a specific template in the paper object image. The classification success rates .for white paper, old newsprint paper and old corrugated cardboard are 95%, 92% and 90%, respectively. Finally, the best result of the proposed method is compared with the results published in literature where waste paper grade identification systems were developed using other methods. The remarkable achievement obtained with the method is the accurate identification and dynamic sorting of all grades of papers using chromaticity, which is the best among the prevailing techniques of optical or electronic image based systems. [ABSTRACT FROM AUTHOR]

The article presents a new technique for measuring paper deformation in unidirectional tensile tests, based on recording and analysis of a series of specimen images. The proposed technique differs from the DIC-based deformation measurement in that the cross-correlation of image data has been replaced with linear filtering. For this purpose, a regular grid of markers is printed on the sample. Filtering the image creates local maxima in the places where markers occur. The developed algorithm finds their location with sub-pixel accuracy. Printing a grid of markers on tested paper and use of reference objects visible in the same image as the paper sample, freed from the need to mechanically connect the camera and the universal testing machine and from the necessity to electronically synchronize their work. The obtained deformation distributions and Poisson's ratios are in accordance with the literature data which confirms the correctness of the developed measurement technique. [ABSTRACT FROM AUTHOR]

Eco-design has become very important in last few years. According to the environmental suggestions not to waste materials, energy etc., and use of recycle materials we were study paper recycling process in relation to used printed typefaces, their sizes and used different leading. For prints conventional and model offset inks with greater environmental benefits were used for printing. Characteristics of the recycled fibers followed by image analysis and some spectrophotometric methods are discussed. The results of these studies except for the scientific contribution should serve as a reference in design, especially for environmentally friendly graphic products. [ABSTRACT FROM AUTHOR]

The formation index of filter paper is one of the most important characteristics used in industrial quality control. Its estimation is often based on subjective comparison chart rating or, more objective, on the power spectrum of the paper structure observed on a transmission light table. It is shown that paper formation can be modeled by means of Gaussian random fields with a well-defined class of correlation functions, and a formation index which is derived from the density of the Bartlett spectrum estimated from image data: the mean of the Bessel transform of the correlation function taken for wave lengths between 2 and 5mm. Furthermore, it is shown that a considerable variation of the local grammage can be observed also in cases where the the fibers are uniformly and independently scattered in the paper sheet. [ABSTRACT FROM AUTHOR]

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed. [ABSTRACT FROM AUTHOR]

This article analyses Italian illustrated editions of Little Women published between 1908 and 1945. After an overview of the publishing history of the novel in Italy, the paper will examine Italian illustrations as hermeneutic tools in order to examine their ideological function in the representation of gender. The belated reception of Alcott's novel in Italy, caused by its representation of a nonconformist educational model for girls, is mirrored in the tension between the nationalistic drive and the influence of foreign models that can be pinpointed in the illustrated editions. By analyzing adaptations of American illustrations, original Italian creations, and omissions of iconic illustrations, this paper will unveil how much the visual element is embedded in the Italian historical and cultural context, as the choice (or omission) of particular images impacted the interpretation of the book. By examining the Italian editions of Little Women published in this time frame, we will also be able to retrace the links with the American illustrated history of the book and bear testimony to the powerful impact of the 1934 film version on printed editions. [ABSTRACT FROM AUTHOR]

Abstract: A method for the comparison of paper structure using light transmission images and frequency analysis was developed. The resolution of the light transmission images and the algorithm for the feature extraction were greatly improved to enhance the visibility of peaks in the 2D power spectrum that results from frequency analysis. A comparison method based on correlation measures how well the spectra match as a function of the orientation of the paper, yielding an objective and quantitative measure of similarity between 0 and 1. A technical validation was carried out with 25 different papers showing the potential of this method with common copy papers. Finally, the method was applied in a case. [Copyright &y& Elsevier]

A paper web produced under industrial conditions always exhibits an anisotropy in its mechanical properties measured in the machine and cross directions. Proper and fast information about current paper anisotropy would significantly increase the efficiency of web quality control systems and allow to produce paper with greater precision. The main objective of the work presented was to determine the possibility of measurement of paper anisotropy. The method proposed is based on the image analysis of the orientation and location of special markers -- luminescent fibres -- which were introduced to the structure of the paper. Studies have shown that the method of image analysis provides a detection of fibres with a satisfactory level of efficiency, thus allowing accurate examination of the anisotropy of the paper samples analysed. On the basis of experiments, it was also found that the method of image analysis proposed allows to detect fibres with an accuracy appropriate for the problem under consideration. The invention presented has already been registered in a Patent Office as submission no. P.411294. [ABSTRACT FROM AUTHOR]

Microfluidic paper-based analytical devices (μPADs) have been extensively proposed as ideal tools for point-of-care (POC) testing with minimal user training and technical requirements. However, most μPADs use dried bioreagents, which complicate production, reduce device reproducibility and stability, and require transport and storage under temperature and humidity-controlled conditions. In this work, we propose a μPAD produced using an affordable craft-cutter and stored at room temperature, which is used to partially automate a single-step colorimetric magneto-immunoassay. As a proof-of-concept, the μPAD has been applied to the quantitative detection of Plasmodium falciparum lactate dehydrogenase (Pf-LDH), a biomarker of malaria infection. In this system, detection is based on a single-step magneto-immunoassay that consists of a single 5-min incubation of the lysed blood sample with immuno-modified magnetic beads (MB), detection antibody, and an enzymatic signal amplifier (Poly-HRP). This mixture is then transferred to a single-piece paper device where, after on-chip MB magnetic concentration and washing, signal generation is achieved by adding a chromogenic enzyme substrate. The colorimetric readout is achieved by the naked eye or using a smartphone camera and free software for image analysis. This μPAD afforded quantitative Pf-LDH detection in <15 min, with a detection limit of 6.25 ng mL−1 when the result was interpreted by the naked eye and 1.4 ng mL−1 when analysed using the smartphone imaging system. Moreover, the study of a battery of clinical samples revealed concentrations of Pf-LDH that correlated with those provided by the reference ELISA and with better sensitivity than a commercial rapid diagnostic test (RDT). These results demonstrate that magneto-immunoassays can be partly automated by employing a μPAD, achieving a level of handling that approaches the requirements of POC testing. [ABSTRACT FROM AUTHOR]

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

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]

Plant root system architecture (RSA) has an important role in crop production, particularly for water and nutrient uptake under limiting conditions. In the last few years, several root phenotyping methods have been developed. Here we present a new technique which has been developed for non-destructive, inexpensive and high-throughput root growth studies and RSA analyses. To illustrate the potential applications, this method was tested in an experiment with nitrogen and phosphorous deficiencies in a nutrient solution, affecting RSA parameters of two spring barley varieties (Bojos and Barke). This technique is based on root growth on vertically positioned black filter paper (30 × 60 cm) placed between two black plastic (PVC-P) foils and micro-irrigation systems providing the recirculation of nutrient solution. The pre-germinated seeds were placed in the slit between two plastic bars which carry the filter paper and plastic sheets and fix the plant in the vertical position. This system allows easy repeated non-invasive access to roots for their measuring and sampling. Eighteen days after transplanting the root imaging was done using an RGB digital camera. To evaluate the root architecture parameters the "SmartRoot" software was used. The results revealed that the system is able to detect changes in RSA which are caused mainly by P deficiency (particularly changes in lateral root length and total root area). It can be concluded that this technique has a great potential for non-destructive root growth studies, RSA measurement and root sampling. [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]

A simple measurement method for pressure sensitive adhesives (PSA) in an agglomeration deinking system of mixed office waste paper was studied. This method was based on the different scanning performance of ink and PSA specks in hot-pressed and oven-dried handsheets with the change of contrast values that had been selected and set in the image analysis software. The numbers of ink specks per square meter (NPM) were well recognized at both low and high contrast values and exhibited a very good linear relationship within a range of contrast values. The PSA specks, on the other hand, could not be recognized at the low contrast values and could only be recognized at high contrast values. The NPM value of the ink specks was found to have the highest values at the high contrast values and could be accurately predicted by its NPM value at the low contrast values. Thus, the NPM value of the PSA specks could be easily calculated by the total NPM of the handsheet at the high contrast value minus the projected NPM of the ink specks from its low contrast conditions. Compared to the dye method, which was also used on the measurement of microstickies, this method is suggested as a simple and quick laboratory tool to measure the relative quantity of PSA in the mixed office waste paper with minimum interference from the residual toner ink. [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]

Within a few decades of development, Internet is been refreshing the world's awareness of its development and potentials at an unexpected speed. In recent years, virtual reality technology has begun to be applied to more and more fields. Especially in instant transmission of information, the applications of VR are incomparable. Akin to the wide-ranged advantages of the technology in education and health, the applications of VR in the art of artistic image designing need to be addressed. With this research work, a comprehensive study is presented about the applications of virtual reality in the art of image visualization. The research intends to analyze the possibility of intermingling modern image visualization with the emerging VR technology. Following a systematic approach, the history and development of display design are reviewed besides studying the artistry techniques used in the technology of art image visualization design. The interdisciplinary association of the two fields is explored with the help of relevant algorithms. The model proposed in the paper for the design of art image is based on the visual interactive process, rigorous assembly sequence, and appropriate equipment mode. As a result, the whole design process is more controllable and feasible. The suggested design concept is verified dynamically. Through the collection and comparison of feedback information, the hypothesis algorithm of the model is updated to accommodate the dynamic information so that to reflect the complete image design process. To assess performance of the model, evaluation was conducted from various perspectives. The three basic image operations such as illumination change, occlusion change, and color change were performed by using the two technologies such as the traditional image design and VR-based image design. From the comparison of experimental data, it was revealed that the effects of traditional art image visualization products produced by the art image visualization and that of VR technology are quite different. For instance, the visualization score achieved for the lighting change operation using the VR technology was 95.48%, whereas 85.57% was obtained for the same operation using the traditional image design technology. Similarly, while adding visual effects using the VR technology, an average of 9% higher score was obtained for the said image operations. The promising results attained for the VR-based design testify that the technology is feasible and suitable to be intermingled with the visual design of art image. As a whole, the findings justify that the proposed approach is quite applicable in visual design of art image systems. In view of the significance of the research, the study can be extended to related domains of computer-based designing and visualization as well. [ABSTRACT FROM AUTHOR]

Abstract: The reaction kinetics of alkaline phosphatase (ALP) adsorbed on paper was quantified by image analysis using a colorimetric technique under conditions of excess enzyme. A classical reaction kinetics approach integrated with image processing technique was used to analyse the coloured product formation from the enzyme substrate reaction against time. The order of reaction and rate constant of enzyme activities on paper surface were calculated. ALP was either physisorbed directly on paper or adsorbed on paper treated with a monolayer of a high molecular weight cationic polyacrylamide (CPAM), an anionic polyacrylic acid (PAA) or a high molecular weight polyethylene oxide (PEO). The reaction rate of enzymatic paper follows a first order reaction with respect to the concentration of enzyme–substrate complex. ALP immobilized on paper has a reaction rate 2 to 3 orders of magnitude lower than the free ALP enzyme in buffer solution. This might be due to a critical loss of enzyme flexibility upon adsorption. No increase in reaction rate was achieved by immobilizing ALP on paper treated with a high molecular weight cationic polyacrylamide (CPAM) or a highly charged anionic polyacrylic acid oligomer (PAA), which suggests the enzyme orientation was not significantly affected. However, the reaction rate for ALP on PEO treated paper was lower than that on CPAM or PAA treated papers. That is probably due to some PEO-enzyme interaction affecting the tertiary structure of the ALP enzyme. [Copyright &y& Elsevier]

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]

When young children do not receive adequate amounts of the micronutrient iodine in their diet, their growth and cognitive development can be impaired. Nearly every country in the world has programs in place to track iodine intake and provide supplemental iodine if needed, usually in the form of fortified salt. The iodine nutrition status of a population can be tracked by monitoring iodine levels in urine samples to see if the median value falls in the range of 100–300 micrograms of iodine per liter of urine (μg I/L), which indicates adequate or more than adequate iodine nutrition. Many low and middle-income countries (LMIC) do not have a laboratory capable of carrying out this challenging assay, so samples must be sent out for assay in external labs, which is expensive and time-consuming. In most LMIC, population iodine surveys are carried out every 5–10 years, which limits the utility of the data for program monitoring and evaluation. To solve this problem, we developed a field-friendly paper test card that uses the Sandell-Kolthoff reaction to measure urinary iodine levels. A blind internal validation study showed that 93% of samples (n = 60) of iodide in an artificial urine matrix were categorized correctly by visual analysis as deficient, adequate, or excessive for levels set forth by the World Health Organization. Quantitative measurements based on computer image analysis had an error of 40 ± 20 μg I/L (n = 35 for samples in the calibration range) and these results categorized 88% of the samples (n = 60) correctly. We employed lifecycle analysis principles to address the known toxicity of arsenic, which is an obligatory reagent in the Sandell-Kolthoff reaction. Disposal of the cards in a landfill (their most likely destination after use) could let arsenic leach into groundwater; toxicity characteristic leaching procedure (TCLP) tests showed that the level of arsenic leached from the cards was 28.78 ppm, which is above the United States Environmental Protection Agency’s limit of 5 parts per million for solid waste. We integrated a remediation module into the card. This module contains oxone, to oxidize As(III) to As(V) oxyacids, and the iron oxide goethite. TCLP testing showed that the leachable amount of arsenic was reduced by at least 97.6%—from 28.8 ppm to lower than 0.7 ± 0.7 ppm (n = 20). This upstream intervention rendered the test card suitable for landfilling while retaining its functionality to perform a critical public health evaluation. [ABSTRACT FROM AUTHOR]